DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/26/2026 has been entered.
Response to Amendment
Applicant has submitted the following:
Claims 1-10, and 16-25 are pending examination;
Claims 11-15 are cancelled;
Claims 16-25 are newly added; and
Claims 1-10 are newly amended.
Response to Arguments
Applicant's arguments filed 04/24/2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art does not teach the newly amended limitations, including the surface being measured after the object has been additively manufactured, a preliminary planar object surface, and a revised planar object surface.
Examiner respectfully disagrees. Previously cited Chen teaches receiving measured surface data after an additive manufacturing apparatus has additively manufactured an object ([0029] “A sensor 160 (sometimes referred to as a scanner) is positioned above the object under fabrication 121 and is used to determine physical characteristics of the partially fabricated object. For example, the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”). Under broadest reasonable interpretation, the partially fabricated object, being additively manufactured according to a model ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”), is the additively manufactured object and the subsequent scan data (301) is taken after the object is additively manufactured. Therefore, even if the finished object is not complete, the receiving of the surface measurements is after the object (e.g. partially fabricated object) is additively manufactured.
Previously cited Buller teaches determining ([0111] lines 1-6, “the target surface is detected by a detection system. The detection system may comprise at least one sensor. The detection system may comprise a light source operable to illuminate a portion of the 3D forming (e.g., printing) system enclosure (e.g., the target surface).”) a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data ([0140] lines 18-31, “The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools). An average layering plane (e.g., FIG. 8C, 814) may correspond to a (e.g., imaginary) plane that is an estimated or calculated average. A calculated average may correspond to an arithmetic mean of (e.g., a number of) point locations on a boundary between layers. A calculated average may be calculated using, for example, a linear regression analysis. In some cases, the average layering plane consider deviations from a nominal planar shape.”; see [0159] “The target parameter may vary in time (e.g., in real time) and/or in location. The location may comprise a location at the exposed surface of the material bed. The location may comprise a location at the top surface of the (e.g., forming) 3D object. The target parameter may correlate to the controllable property. The (e.g., input) target parameter may vary in time and/or location in the material bed (e.g., on the forming 3D object)” and “The geometric information may derive from the 3D object (or a correctively deviated (e.g., altered) model thereof). The geometry may comprise geometric information of a previously printed portion of the 3D object (e.g., comprising a local thickness below a given layer, local build angle, proximity to an edge on a given layer, or proximity to layer boundaries).”), determining a revised planar object surface of the object corresponding to the planar model surface([0137] lines 30-37, “The structural correction may comprise any pre-print correction to the model of the requested 3D object that may result in reduced deformation of the formed 3D object and adherence to the requested dimensionality constraints of the 3D object that is formed. The structural correction may comprise a geometric correction to the geometric model of the requested 3D object.”; “the analysis data is compared to requested data. For example, a geometry of the printed object(s) may be compared with the geometry of the requested object(s). In some embodiments, the analysis data is used (e.g., FIG. 7, 717) to adjust the simulation (e.g., FIG. 7, 710). The adjusted simulation may be used, for example, in formation of subsequent object(s).”), by performing a statistical analysis of the measured surface data offset from the preliminary planar object surface in an interpretation direction ([0140] lines 23-31, “An average layering plane (e.g., FIG. 8C, 814) may correspond to a (e.g., imaginary) plane that is an estimated or calculated average. A calculated average may correspond to an arithmetic mean of (e.g., a number of) point locations on a boundary between layers. A calculated average may be calculated using, for example, a linear regression analysis. In some cases, the average layering plane consider deviations from a nominal planar shape.; [0204] lines 16-22, “Generated forming instructions may comprise application of a pre-transformed material, application of an amount of energy emitted to a selected location, a detection system activation and deactivation, sensor data and/or signal acquisition, image processing, process parameters (e.g., dispenser layer height, planarization, chamber pressure), or any combination thereof.”). The target surface, based off of the measured layer plane, is the preliminary planar object surface. The geometric correction, based off of the average layering plane, and which informs formation of subsequent objects, is the revised planar object surface.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6, 8-10, 16-19, and 21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20200215761 A1, previously cited) in view of Buller et al. (US 20200004225 A1).
Regarding claim 1, Chen teaches A method comprising:
after an additive manufacturing apparatus (Fig. 1) has additively manufactured an object (object 121) according to a model (model 190), receiving measured surface data ([0029] “A sensor 160 (sometimes referred to as a scanner) is positioned above the object under fabrication 121 and is used to determine physical characteristics of the partially fabricated object. For example, the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”). Under broadest reasonable interpretation, the partially fabricated object, being additively manufactured according to a model ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”), is the additively manufactured object and the subsequent scan data (301) is taken after the object is additively manufactured. Therefore, even if the finished object is not complete, the receiving of the surface measurements is after the object (e.g. partially fabricated object) is additively manufactured.
for a ([0029] lines 4-15, “the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”) the ([0038] lines 3-13, “In FIG. 3, the expected surface height is represented as a line 418. The expected surface height is useful for comparison to the actual printed surface height. For example, when fabricating the object 121, discrepancies between the model 190 and the actual material deposited often occur. Even if the jets are controlled to deposit a planned thickness, the achieved thickness of the layer may deviate from the plan due to variable or unpredicted characteristics of the jetting and/or due to changes after jetting such as during curing or flowing on the surface before curing.”);
determining a revised planar object surface of the object corresponding to the planar model surface ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”), by performing a statistical analysis of the measured surface data offset from the preliminary planar object surface (Fig. 2, steps 309-313; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”) in an interpretation direction ([0029] lines 11-15, “the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”), the direction of the gradient is the interpretation direction;
adjusting either or both of a control parameter or a calibration parameter of the additive manufacturing apparatus based on the revised planar object surface ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”)
Chen does not teach the method, comprising:
receiving measured surface data for a non-planar object surface of the object corresponding to a planar model surface of the model determining a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data; and
causing the additive manufacturing apparatus to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter.
Buller teaches an analogous method of making a measurement (Abstract; Fig. 1; detection system of [0111]), comprising:
receiving measured surface data for a non-planar object surface of the object ([0140] lines 13-23, “Boundaries (e.g., FIG. 8C, 806, 808, 810 and 812) between the layers may be visible (e.g., by human eye or using microscopy). The microscopy method may comprise optical microscopy, scanning electron microscopy, or transmission electron microscopy. The boundaries between the layers may be evident by a microstructure of the 3D object. The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools).”) corresponding to a planar model surface of the model ([0140] lines 3-9, “FIG. 8A, layers (e.g., FIG. 8A, 872, 874, and 876) of the geometric model are depicted. Layers of the geometric model may correspond to (e.g., successive) layers of the formed 3D object (e.g., that formed in a layer-wise manner) and/or slices of the geometric model. In some cases, a layer comprises a layering plane that corresponds to an average layering plane.”)
determining ([0111] lines 1-6, “the target surface is detected by a detection system. The detection system may comprise at least one sensor. The detection system may comprise a light source operable to illuminate a portion of the 3D forming (e.g., printing) system enclosure (e.g., the target surface).”) a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data ([0140] lines 18-31, “The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools). An average layering plane (e.g., FIG. 8C, 814) may correspond to a (e.g., imaginary) plane that is an estimated or calculated average. A calculated average may correspond to an arithmetic mean of (e.g., a number of) point locations on a boundary between layers. A calculated average may be calculated using, for example, a linear regression analysis. In some cases, the average layering plane consider deviations from a nominal planar shape.”; see [0159] “The target parameter may vary in time (e.g., in real time) and/or in location. The location may comprise a location at the exposed surface of the material bed. The location may comprise a location at the top surface of the (e.g., forming) 3D object. The target parameter may correlate to the controllable property. The (e.g., input) target parameter may vary in time and/or location in the material bed (e.g., on the forming 3D object)” and “The geometric information may derive from the 3D object (or a correctively deviated (e.g., altered) model thereof). The geometry may comprise geometric information of a previously printed portion of the 3D object (e.g., comprising a local thickness below a given layer, local build angle, proximity to an edge on a given layer, or proximity to layer boundaries).”); and
causing the additive manufacturing apparatus (Fig. 1, 3D printer 100). to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter (Fig. 7; [0137] lines 75-81, “ monitoring is done before, during and/or after printing. The monitoring may use historical measurements (e.g., as an analytical tool and/or to set a threshold value). Monitoring of one or more aspects of formation can optionally be used to (e.g., directly) modify the forming instructions (e.g., FIG. 7, 713) and/or adjust the one or more simulations (e.g., FIG. 7, 715).”; lines 119-131, “The roughness can be measured with a surface profilometer. In some cases, the analysis provides data concerning geometry of the object(s). In some cases, the analysis provides data concerning one or more material properties (e.g., porosity, surface roughness, grain structure, internal strain and/or chemical composition) of the object(s). In some embodiments, the analysis data is compared to requested data. For example, a geometry of the printed object(s) may be compared with the geometry of the requested object(s). In some embodiments, the analysis data is used (e.g., FIG. 7, 717) to adjust the simulation (e.g., FIG. 7, 710). The adjusted simulation may be used, for example, in formation of subsequent object(s).”; [0137] lines 104-111, “The target signal may be a value, a set of values, or a function (e.g., a time dependent function). The one or more 3D objects may optionally be analyzed (e.g., FIG. 7, 716). In some embodiments, a target (e.g., thermal) signal is obtained from historical data of 3D objects (or portions thereof) that have been analyzed. In some embodiments, the object(s) or portion(s) thereof is analyzed using an inspection tool”). The adjustment of simulation in the formation of subsequent objects and modification of the forming instructions thereof, wherein the monitoring and adjustment may occur during or after printing, and wherein the adjustment includes a target based on historical data of 3D objects, is the causing the manufacture of a subsequent object according to the adjustment of a control parameter of the additive manufacturing apparatus.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chen to include the measured surface data for a non-planar object surface, preliminary planar object surface, and manufacture of a subsequent object of Buller because it would yield predictable and advantageous results. The measurement of non-planar surface (for an expected planar surface) is predictable result for detecting deviations from the desired planar surface. The determination of a preliminary planar object surface yields predictable results of determining a plane for comparison between model and measured object. The manufacture of subsequent objects according to an adjusted control parameter based on previous measurements would predictably enable the additive manufacturing apparatus to be controlled to alter its manufacturing process after the surface deviate is determined. Further, it would yield advantageous results of, due to the adjustment of the control parameter, manufacturing a subsequent object without the surface deviation of the preceding manufactured object.
Regarding claim 2, Chen in view of Buller teaches The method of claim 1, further comprising:
determining the interpretation direction based on a reference object surface of the object (Chen: [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”; [0058]” Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space.”). The interpretation direction (i.e. direction of gradient) depends on a surface fit to reference surface data representing at least a portion of a reference surface of the object (output estimated depth data, based on spatio-temporal or spatial neighborhood data of the object).
Regarding claim 3, Chen in view of Buller teaches The method of claim 2, wherein the reference object surface is other than the preliminary planar object surface (Chen: [0058]- [0059] “Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space. Referring to FIG. 9, the optimal depth estimate mapped into the input space 321 is shown along with the successive scan data 301 and the expected height data 418 for the (x, y) position.”). The mapped optimal depth estimate, as shown in Fig. 9, is a surface other than the preliminary planar object.
Regarding claim 4, Chen in view of Buller teaches The method of claim 1, wherein determining the revised planar object surface by performing the statistical analysis comprises:
identifying a data point corresponding to a kth percentile data point of the measured surface data offset from the preliminary planar object surface in the interpretation direction (Chen: [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”; Buller: [0174] lines 1-3, “At times, an energy beam is directed onto a specified area of at least a portion of the target surface for a specified time period.”). The median filter, among other statistical approaches for determining the measurement surface, identifies a kth percentile data point, wherein the median is the 50th percentile.; and
determining the revised planar object surface based on the identified data point ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”).
Regarding claim 5, Chen in view of Buller teaches The method of claim 1, wherein determining the revised planar object surface by performing the statistical analysis comprises:
orienting, or aligning and orienting, the revised planar object surface in relation to the measured surface data (Chen: [0049] lines 3-7, “In some embodiments, a simple horizontal filter is applied to the array/image when the data is aligned. In other embodiments, the filter needs to follow (e.g., be oriented with) the depth changes when the data is not aligned.”); and
positioning the revised planar object surface at a location (Buller: Figs. 8A-B) along the interpretation direction based on the statistical analysis (Chen: Fig. 9; [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”).
Regarding claim 6, Chen in view of Buller teaches The method of claim 1, wherein determining revised planar object surface by performing the statistical analysis comprises:
determining the revised planar object surface based on the statistical analysis of the measured surface data corresponding to a predefined portion of the preliminary planar object surface (Chen: [0041] lines 4-8, “If the system does not print a given location in between the scans this means that the scan data should be the same for the two scans. For these instances, the data reduction process might average the scan data or choose a value from one of the scans to reduce the data.”; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; Buller: boundaries between layers). The median filter with limited extent is the statistical analysis of a restricted portion of the measured surface data, wherein each limited extent is a predefined portion. Further, the averaging of data where the system did not print between scans is performing a statistical analysis of a portion corresponding to a predefined portion of the surface.
Regarding claim 8, Chen in view of Buller teaches The method of claim 1, wherein the revised planar object surface has an orientation depending on the interpretation direction (Chen: [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”; [0058]” Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space.”). The surface determined (estimated depth data) has the orientation dependent on the interpretation direction (the direction given by the gradient).
Regarding claim 9, Chen in view of Buller teaches The method of claim 1, further comprising: adjusting a pre-compensation factor to be applied to the model (Chen: [0032]-[0033] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160. The controller 110 includes a sensor data processor 111 that implements a depth reconstruction procedure (described in greater detail below). The sensor data processor 111 receives the model 190 as well as scan data from the sensor 160 as input. As described below, the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; Buller: [0137] lines 29-36, “The structural correction may comprise any pre-print correction to the model of the requested 3D object that may result in reduced deformation of the formed 3D object and adherence to the requested dimensionality constraints of the 3D object that is formed. The structural correction may comprise a geometric correction to the geometric model of the requested 3D object.”). The pre-print correction to the model, such that is used for the fabrication plan, is the pre-compensation factor applied to the model.
Regarding claim 10, Chen in view of Buller teaches The method of claim 1, wherein the measured surface data offset from the measurement preliminary planar object surface in the interpretation direction (Chen: [0046] lines 7-20, “If the printer were to print perfectly, all the data in the aligned data set 309 would be aligned along one horizontal line. For example, making the data flat removes the variable delta (i.e., the amount of printed material) from the problem. At a point (x,y), a variable z is slightly oscillating above and below a level (i.e., the horizontal line). Put another way, shifting the data by the expected depth (after printing each layer) effectively aligns all surfaces (after printing each layer) to be along one horizontal line. Due to the inaccuracies in printing this is typically not the case but there should be a continuous line (e.g., from left to right in the figure)—this is because there is continuity in the surface as it is being printed”) comprises noise of the measured surface data (Chen: [0041] lines 6-9, “For these instances, the data reduction process might average the scan data or choose a value from one of the scans to reduce the data. Averaging data typically reduces the measurement noise.”), outlier data representing outlier portions of the measured surface data, or both the noise and the outlier data. The average of measurement data, including measurement noise of the measurement surface, reduces but does not eliminate the noise from the data, therefore the measured surface data is offset.
Regarding claim 16, Chen teaches A system comprising: a processor (controller 110, sensor processor 111) and; a memory storing instructions executable by the processor to perform processing ([0070] lines 6-13, “in a programmed approach the software may include procedures in one or more computer programs that execute on one or more programmed or programmable computing system (which may be of various architectures such as distributed, client/server, or grid) each including at least one processor, at least one data storage system (including volatile and/or non-volatile memory and/or storage elements)”) comprising:
after an additive manufacturing apparatus (Fig. 1) has additively manufactured an object (object 121) according to a model (model 190), receiving measured surface data ([0029] “A sensor 160 (sometimes referred to as a scanner) is positioned above the object under fabrication 121 and is used to determine physical characteristics of the partially fabricated object. For example, the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”). Under broadest reasonable interpretation, the partially fabricated object, being additively manufactured according to a model ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”), is the additively manufactured object and the subsequent scan data (301) is taken after the object is additively manufactured. Therefore, even if the finished object is not complete, the receiving of the surface measurements is after the object (e.g. partially fabricated object) is additively manufactured.
for a ([0029] lines 4-15, “the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”), the ([0038] lines 3-13, “In FIG. 3, the expected surface height is represented as a line 418. The expected surface height is useful for comparison to the actual printed surface height. For example, when fabricating the object 121, discrepancies between the model 190 and the actual material deposited often occur. Even if the jets are controlled to deposit a planned thickness, the achieved thickness of the layer may deviate from the plan due to variable or unpredicted characteristics of the jetting and/or due to changes after jetting such as during curing or flowing on the surface before curing.”);
determining a revised planar object surface of the object corresponding to the planar model surface ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”), by performing a statistical analysis of the measured surface data offset from the preliminary planar object surface (Fig. 2, steps 309-313; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”) in an interpretation direction ([0029] lines 11-15, “the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”), the direction of the gradient is the interpretation direction;
adjusting either or both of a control parameter or a calibration parameter of the additive manufacturing apparatus based on the revised planar object surface; and causing the additive manufacturing apparatus to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”).
Chen does not teach the system, comprising:
receiving measured surface data for a non-planar object surface of the object corresponding to a planar model surface of the model determining a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data; and
causing the additive manufacturing apparatus to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter.
Buller teaches an analogous system of making a measurement (Abstract; Fig. 1; detection system of [0111]), comprising:
receiving measured surface data for a non-planar object surface of the object ([0140] lines 13-23, “Boundaries (e.g., FIG. 8C, 806, 808, 810 and 812) between the layers may be visible (e.g., by human eye or using microscopy). The microscopy method may comprise optical microscopy, scanning electron microscopy, or transmission electron microscopy. The boundaries between the layers may be evident by a microstructure of the 3D object. The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools).”) corresponding to a planar model surface of the model ([0140] lines 3-9, “FIG. 8A, layers (e.g., FIG. 8A, 872, 874, and 876) of the geometric model are depicted. Layers of the geometric model may correspond to (e.g., successive) layers of the formed 3D object (e.g., that formed in a layer-wise manner) and/or slices of the geometric model. In some cases, a layer comprises a layering plane that corresponds to an average layering plane.”)
determining ([0111] lines 1-6, “the target surface is detected by a detection system. The detection system may comprise at least one sensor. The detection system may comprise a light source operable to illuminate a portion of the 3D forming (e.g., printing) system enclosure (e.g., the target surface).”) a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data ([0140] lines 18-31, “The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools). An average layering plane (e.g., FIG. 8C, 814) may correspond to a (e.g., imaginary) plane that is an estimated or calculated average. A calculated average may correspond to an arithmetic mean of (e.g., a number of) point locations on a boundary between layers. A calculated average may be calculated using, for example, a linear regression analysis. In some cases, the average layering plane consider deviations from a nominal planar shape.”; see [0159] “The target parameter may vary in time (e.g., in real time) and/or in location. The location may comprise a location at the exposed surface of the material bed. The location may comprise a location at the top surface of the (e.g., forming) 3D object. The target parameter may correlate to the controllable property. The (e.g., input) target parameter may vary in time and/or location in the material bed (e.g., on the forming 3D object)” and “The geometric information may derive from the 3D object (or a correctively deviated (e.g., altered) model thereof). The geometry may comprise geometric information of a previously printed portion of the 3D object (e.g., comprising a local thickness below a given layer, local build angle, proximity to an edge on a given layer, or proximity to layer boundaries).”); and
causing the additive manufacturing apparatus (Fig. 1, 3D printer 100). to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter (Fig. 7; [0137] lines 75-81, “ monitoring is done before, during and/or after printing. The monitoring may use historical measurements (e.g., as an analytical tool and/or to set a threshold value). Monitoring of one or more aspects of formation can optionally be used to (e.g., directly) modify the forming instructions (e.g., FIG. 7, 713) and/or adjust the one or more simulations (e.g., FIG. 7, 715).”; lines 119-131, “The roughness can be measured with a surface profilometer. In some cases, the analysis provides data concerning geometry of the object(s). In some cases, the analysis provides data concerning one or more material properties (e.g., porosity, surface roughness, grain structure, internal strain and/or chemical composition) of the object(s). In some embodiments, the analysis data is compared to requested data. For example, a geometry of the printed object(s) may be compared with the geometry of the requested object(s). In some embodiments, the analysis data is used (e.g., FIG. 7, 717) to adjust the simulation (e.g., FIG. 7, 710). The adjusted simulation may be used, for example, in formation of subsequent object(s).”; [0137] lines 104-111, “The target signal may be a value, a set of values, or a function (e.g., a time dependent function). The one or more 3D objects may optionally be analyzed (e.g., FIG. 7, 716). In some embodiments, a target (e.g., thermal) signal is obtained from historical data of 3D objects (or portions thereof) that have been analyzed. In some embodiments, the object(s) or portion(s) thereof is analyzed using an inspection tool”). The adjustment of simulation in the formation of subsequent objects and modification of the forming instructions thereof, wherein the monitoring and adjustment may occur during or after printing, and wherein the adjustment includes a target based on historical data of 3D objects, is the causing the manufacture of a subsequent object according to the adjustment of a control parameter of the additive manufacturing apparatus.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen to include the measured surface data for a non-planar object surface, preliminary planar object surface, and manufacture of a subsequent object of Buller because it would yield predictable and advantageous results. The measurement of non-planar surface (for an expected planar surface) is predictable result for detecting deviations from the desired planar surface. The determination of a preliminary planar object surface yields predictable results of determining a plane for comparison between model and measured object. The manufacture of subsequent objects according to an adjusted control parameter based on previous measurements would predictably enable the additive manufacturing apparatus to be controlled to alter its manufacturing process after the surface deviate is determined. Further, it would yield advantageous results of, due to the adjustment of the control parameter, manufacturing a subsequent object without the surface deviation of the preceding manufactured object.
Regarding claim 17, Chen in view of Buller teaches The system of claim 16, wherein determining the revised planar object surface by performing the statistical analysis comprises: identifying a data point corresponding to a kth percentile data point of the measured surface data offset from the preliminary planar object surface in the interpretation direction (Chen: [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”; Buller: [0174] lines 1-3, “At times, an energy beam is directed onto a specified area of at least a portion of the target surface for a specified time period.”). The median filter, among other statistical approaches for determining the measurement surface, identifies a kth percentile data point, wherein the median is the 50th percentile.; and
determining the revised planar object surface based on the identified data point ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”).
Regarding claim 18, Chen in view of Buller teaches The system of claim 16, wherein determining the revised planar object surface by performing the statistical analysis comprises: orienting, or aligning and orienting, the revised planar object surface in relation to the measured surface data (Chen: [0049] lines 3-7, “In some embodiments, a simple horizontal filter is applied to the array/image when the data is aligned. In other embodiments, the filter needs to follow (e.g., be oriented with) the depth changes when the data is not aligned.”); and
positioning the revised planar object surface at a location (Buller: Figs. 8A-B) along the interpretation direction based on the statistical analysis (Chen: Fig. 9; [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”).
Regarding claim 19, Chen in view of Buller teaches The system of claim 16, wherein determining the revised planar object surface by performing the statistical analysis comprises: determining the revised planar object surface based on the statistical analysis of the measured surface data corresponding to a predefined portion of the preliminary planar object surface (Chen: [0041] lines 4-8, “If the system does not print a given location in between the scans this means that the scan data should be the same for the two scans. For these instances, the data reduction process might average the scan data or choose a value from one of the scans to reduce the data.”; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; Buller: boundaries between layers). The median filter with limited extent is the statistical analysis of a restricted portion of the measured surface data, wherein each limited extent is a predefined portion. Further, the averaging of data where the system did not print between scans is performing a statistical analysis of a portion corresponding to a predefined portion of the surface.
Regarding claim 21, Chen teaches A non-transitory machine-readable medium storing instructions ([0071] lines 1-7, “The software may be stored in non-transitory form, such as being embodied in a volatile or non-volatile storage medium, or any other non-transitory medium, using a physical property of the medium (e.g., surface pits and lands, magnetic domains, or electrical charge) for a period of time (e.g., the time between refresh periods of a dynamic memory device such as a dynamic RAM).”) executable by a processor (controller 110, sensor processor 111) to perform processing ([0070] lines 6-13, “in a programmed approach the software may include procedures in one or more computer programs that execute on one or more programmed or programmable computing system (which may be of various architectures such as distributed, client/server, or grid) each including at least one processor, at least one data storage system (including volatile and/or non-volatile memory and/or storage elements)”) comprising:
after an additive manufacturing apparatus (Fig. 1) has additively manufactured an object (object 121) according to a model (model 190), receiving measured surface data ([0029] “A sensor 160 (sometimes referred to as a scanner) is positioned above the object under fabrication 121 and is used to determine physical characteristics of the partially fabricated object. For example, the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”). Under broadest reasonable interpretation, the partially fabricated object, being additively manufactured according to a model ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”), is the additively manufactured object and the subsequent scan data (301) is taken after the object is additively manufactured. Therefore, even if the finished object is not complete, the receiving of the surface measurements is after the object (e.g. partially fabricated object) is additively manufactured.
for a ([0029] lines 4-15, “the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”), the ([0038] lines 3-13, “In FIG. 3, the expected surface height is represented as a line 418. The expected surface height is useful for comparison to the actual printed surface height. For example, when fabricating the object 121, discrepancies between the model 190 and the actual material deposited often occur. Even if the jets are controlled to deposit a planned thickness, the achieved thickness of the layer may deviate from the plan due to variable or unpredicted characteristics of the jetting and/or due to changes after jetting such as during curing or flowing on the surface before curing.”);
determining a revised planar object surface of the object corresponding to the planar model surface ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”), by performing a statistical analysis of the measured surface data offset from the preliminary planar object surface (Fig. 2, steps 309-313; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”) in an interpretation direction ([0029] lines 11-15, “the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”), the direction of the gradient is the interpretation direction;
adjusting either or both of a control parameter or a calibration parameter of the additive manufacturing apparatus based on the revised planar object surface; and causing the additive manufacturing apparatus to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”).
Chen does not teach the instructions, comprising:
receiving measured surface data for a non-planar object surface of the object corresponding to a planar model surface of the model determining a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data; and
causing the additive manufacturing apparatus to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter.
Buller teaches an analogous instructions (Abstract; Fig. 1; detection system of [0111]; see [0017]), comprising:
receiving measured surface data for a non-planar object surface of the object ([0140] lines 13-23, “Boundaries (e.g., FIG. 8C, 806, 808, 810 and 812) between the layers may be visible (e.g., by human eye or using microscopy). The microscopy method may comprise optical microscopy, scanning electron microscopy, or transmission electron microscopy. The boundaries between the layers may be evident by a microstructure of the 3D object. The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools).”) corresponding to a planar model surface of the model ([0140] lines 3-9, “FIG. 8A, layers (e.g., FIG. 8A, 872, 874, and 876) of the geometric model are depicted. Layers of the geometric model may correspond to (e.g., successive) layers of the formed 3D object (e.g., that formed in a layer-wise manner) and/or slices of the geometric model. In some cases, a layer comprises a layering plane that corresponds to an average layering plane.”)
determining ([0111] lines 1-6, “the target surface is detected by a detection system. The detection system may comprise at least one sensor. The detection system may comprise a light source operable to illuminate a portion of the 3D forming (e.g., printing) system enclosure (e.g., the target surface).”) a preliminary planar object surface of the object corresponding to the planar model surface, based on the measured surface data ([0140] lines 18-31, “The boundaries between the layers may be (e.g., substantially) planar. The boundaries between the layers may have some irregularity (e.g., roughness) due to the transformation (e.g., melting and or sintering) process (e.g., and formation of any microstructure such as melt pools). An average layering plane (e.g., FIG. 8C, 814) may correspond to a (e.g., imaginary) plane that is an estimated or calculated average. A calculated average may correspond to an arithmetic mean of (e.g., a number of) point locations on a boundary between layers. A calculated average may be calculated using, for example, a linear regression analysis. In some cases, the average layering plane consider deviations from a nominal planar shape.”; see [0159] “The target parameter may vary in time (e.g., in real time) and/or in location. The location may comprise a location at the exposed surface of the material bed. The location may comprise a location at the top surface of the (e.g., forming) 3D object. The target parameter may correlate to the controllable property. The (e.g., input) target parameter may vary in time and/or location in the material bed (e.g., on the forming 3D object)” and “The geometric information may derive from the 3D object (or a correctively deviated (e.g., altered) model thereof). The geometry may comprise geometric information of a previously printed portion of the 3D object (e.g., comprising a local thickness below a given layer, local build angle, proximity to an edge on a given layer, or proximity to layer boundaries).”); and
causing the additive manufacturing apparatus (Fig. 1, 3D printer 100). to additively manufacture a subsequent object according to either or both of the adjusted control parameter and/or the adjusted calibration parameter (Fig. 7; [0137] lines 75-81, “ monitoring is done before, during and/or after printing. The monitoring may use historical measurements (e.g., as an analytical tool and/or to set a threshold value). Monitoring of one or more aspects of formation can optionally be used to (e.g., directly) modify the forming instructions (e.g., FIG. 7, 713) and/or adjust the one or more simulations (e.g., FIG. 7, 715).”; lines 119-131, “The roughness can be measured with a surface profilometer. In some cases, the analysis provides data concerning geometry of the object(s). In some cases, the analysis provides data concerning one or more material properties (e.g., porosity, surface roughness, grain structure, internal strain and/or chemical composition) of the object(s). In some embodiments, the analysis data is compared to requested data. For example, a geometry of the printed object(s) may be compared with the geometry of the requested object(s). In some embodiments, the analysis data is used (e.g., FIG. 7, 717) to adjust the simulation (e.g., FIG. 7, 710). The adjusted simulation may be used, for example, in formation of subsequent object(s).”; [0137] lines 104-111, “The target signal may be a value, a set of values, or a function (e.g., a time dependent function). The one or more 3D objects may optionally be analyzed (e.g., FIG. 7, 716). In some embodiments, a target (e.g., thermal) signal is obtained from historical data of 3D objects (or portions thereof) that have been analyzed. In some embodiments, the object(s) or portion(s) thereof is analyzed using an inspection tool”). The adjustment of simulation in the formation of subsequent objects and modification of the forming instructions thereof, wherein the monitoring and adjustment may occur during or after printing, and wherein the adjustment includes a target based on historical data of 3D objects, is the causing the manufacture of a subsequent object according to the adjustment of a control parameter of the additive manufacturing apparatus.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the instructions of Chen to include the measured surface data for a non-planar object surface, preliminary planar object surface, and manufacture of a subsequent object of Buller because it would yield predictable and advantageous results. The measurement of non-planar surface (for an expected planar surface) is predictable result for detecting deviations from the desired planar surface. The determination of a preliminary planar object surface yields predictable results of determining a plane for comparison between model and measured object. The manufacture of subsequent objects according to an adjusted control parameter based on previous measurements would predictably enable the additive manufacturing apparatus to be controlled to alter its manufacturing process after the surface deviate is determined. Further, it would yield advantageous results of, due to the adjustment of the control parameter, manufacturing a subsequent object without the surface deviation of the preceding manufactured object.
Regarding claim 22, Chen in view of Buller teaches The non-transitory machine-readable medium of claim 21, wherein determining the revised planar object surface by performing the statistical analysis comprises:
identifying a data point corresponding to a kth percentile data point of the measured surface data offset from the preliminary planar object surface in the interpretation direction (Chen: [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”; Buller: [0174] lines 1-3, “At times, an energy beam is directed onto a specified area of at least a portion of the target surface for a specified time period.”). The median filter, among other statistical approaches for determining the measurement surface, identifies a kth percentile data point, wherein the median is the 50th percentile.; and
determining the revised planar object surface based on the identified data point ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”).
Regarding claim 23, Chen in view of Buller teaches The non-transitory machine-readable medium of claim 21, wherein determining the revised planar object surface by performing the statistical analysis comprises: orienting, or aligning and orienting, the revised planar object surface in relation to the measured surface data (Chen: [0049] lines 3-7, “In some embodiments, a simple horizontal filter is applied to the array/image when the data is aligned. In other embodiments, the filter needs to follow (e.g., be oriented with) the depth changes when the data is not aligned.”); and
positioning the revised planar object surface at a location (Buller: Figs. 8A-B) along the interpretation direction based on the statistical analysis (Chen: Fig. 9; [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”).
Regarding claim 24, Chen in view of Buller teaches The non-transitory machine-readable medium of claim 21, wherein determining the revised planar object surface by performing the statistical analysis comprises:
determining the revised planar object surface based on the statistical analysis of the measured surface data corresponding to a predefined portion of the preliminary planar object surface (Chen: [0041] lines 4-8, “If the system does not print a given location in between the scans this means that the scan data should be the same for the two scans. For these instances, the data reduction process might average the scan data or choose a value from one of the scans to reduce the data.”; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; Buller: boundaries between layers). The median filter with limited extent is the statistical analysis of a restricted portion of the measured surface data, wherein each limited extent is a predefined portion. Further, the averaging of data where the system did not print between scans is performing a statistical analysis of a portion corresponding to a predefined portion of the surface.
Claim(s) 7, 20, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Buller as applied to claim 1 above, and further in view of Spink et al. (US 20180250745 A1, previously cited).
Regarding claim 7, Chen in view of Buller teaches The method of claim 1.
Chen in view of Buller does not teach the method, wherein the surface deviation of the measured surface comprises a sink.
Spink teaches an analogous method of measuring the surface deviation of an object made by additive manufacturing, wherein the surface deviation of the measured surface comprises a sink ([0226] lines 17-23, “The exterior surface can have regions of relative depression (e.g., FIG. 51B, 5122 or FIG. 51C, 5142) and regions of relative elevation (e.g., FIG. 51B, 5124 or FIG. 51C, 5144). The regions of relative elevation may be referred to as peaks. The regions of relative depression may be referred to as valleys. The peaks and valleys may be an alternating series of peaks and valleys.”; [0301] lines 1-10, “In some examples, at least a portion of the 3D object can be vertically displaced (e.g., sink) in the material bed. At least a portion of the 3D object can be surrounded by pre-transformed material within the material bed (e.g., submerged). At least a portion of the 3D object can rest in the pre-transformed material without substantial vertical movement (e.g., displacement). Lack of substantial vertical displacement can amount to a vertical movement (e.g., sinking) of at most about 40%, 20%, 10%, 5%, or 1% of the layer thickness.”). The valleys and vertically displaced sinks are the sink of the surface deviation.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chen in view of Buller to include the sink of Spink because measuring a surface deviation comprising a sink would yield predictable results including detecting where the object has deviated from the model, such as a lack of deposited material in the manufacturing process, thereby advantageously enabling diagnosis of deviation and correction to be taken.
Regarding claim 20, Chen in view of Buller teaches The system of claim 16.
Chen in view of Buller does not teach the system, wherein the surface deviation of the measured surface comprises a sink.
Spink teaches an analogous system of measuring the surface deviation of an object made by additive manufacturing, wherein the surface deviation of the measured surface comprises a sink ([0226] lines 17-23, “The exterior surface can have regions of relative depression (e.g., FIG. 51B, 5122 or FIG. 51C, 5142) and regions of relative elevation (e.g., FIG. 51B, 5124 or FIG. 51C, 5144). The regions of relative elevation may be referred to as peaks. The regions of relative depression may be referred to as valleys. The peaks and valleys may be an alternating series of peaks and valleys.”; [0301] lines 1-10, “In some examples, at least a portion of the 3D object can be vertically displaced (e.g., sink) in the material bed. At least a portion of the 3D object can be surrounded by pre-transformed material within the material bed (e.g., submerged). At least a portion of the 3D object can rest in the pre-transformed material without substantial vertical movement (e.g., displacement). Lack of substantial vertical displacement can amount to a vertical movement (e.g., sinking) of at most about 40%, 20%, 10%, 5%, or 1% of the layer thickness.”). The valleys and vertically displaced sinks are the sink of the surface deviation.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen in view of Buller to include the sink of Spink because measuring a surface deviation comprising a sink would yield predictable results including detecting where the object has deviated from the model, such as a lack of deposited material in the manufacturing process, thereby advantageously enabling diagnosis of deviation and correction to be taken.
Regarding claim 25, Chen in view of Buller teaches The non-transitory machine-readable medium of claim 21.
Chen in view of Buller does not teach the instructions, wherein the surface deviation of the measured surface comprises a sink.
Spink teaches an analogous instructions of measuring the surface deviation of an object made by additive manufacturing, wherein the surface deviation of the measured surface comprises a sink ([0226] lines 17-23, “The exterior surface can have regions of relative depression (e.g., FIG. 51B, 5122 or FIG. 51C, 5142) and regions of relative elevation (e.g., FIG. 51B, 5124 or FIG. 51C, 5144). The regions of relative elevation may be referred to as peaks. The regions of relative depression may be referred to as valleys. The peaks and valleys may be an alternating series of peaks and valleys.”; [0301] lines 1-10, “In some examples, at least a portion of the 3D object can be vertically displaced (e.g., sink) in the material bed. At least a portion of the 3D object can be surrounded by pre-transformed material within the material bed (e.g., submerged). At least a portion of the 3D object can rest in the pre-transformed material without substantial vertical movement (e.g., displacement). Lack of substantial vertical displacement can amount to a vertical movement (e.g., sinking) of at most about 40%, 20%, 10%, 5%, or 1% of the layer thickness.”). The valleys and vertically displaced sinks are the sink of the surface deviation.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the instructions of Chen in view of Buller to include the sink of Spink because measuring a surface deviation comprising a sink would yield predictable results including detecting where the object has deviated from the model, such as a lack of deposited material in the manufacturing process, thereby advantageously enabling diagnosis of deviation and correction to be taken.
Conclusion
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/B.B.G./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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