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 .
Claims 1-10 are pending.
Claim Objections
Claim 10 objected to because of the following informalities: the status of the claim 10 is unclear because it shows “withdrawn-currently amended”. For examination purpose, the examiner considers the claim 10 as currently amended claim. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation " to be printed into a neural network using a …software" in lines 4-5. It is unclear how input data can be “printed into a neural network”. Appropriated correction is required.
Claim 8 recites the limitation “neural network is implemented by hardware or software” which is indefinite. It is unclear how the neural network can be hardware. Appropriated correction is required.
Claims 10 recites “the 3D printing system comprises: the neural network according to claim 1” which is indefinite. Claim 10 is a product by process claim, but claim 1 is a method claim which recites act of performing “inputting” and “calculating….using the neural network”, it is unclear how claim 10 contain and use the neural network in claim 1 because the claim 1 is method claim. Appropriated correction is required.
Claim 10 recites the limitation " the component" in line 5. There is insufficient antecedent basis for this limitation in the claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over LONG et al. (hereinafter “LONG”) (US 20220088851 A1) in view of MEHR et al. (hereinafter “MEHR”) (US 20180341248 A1) .
As to claims 1 and 9, LONG teaches the invention of printing a component using a 3D printer comprises:
inputting a desired dose distribution in terms of an amount of UV light absorbed as a function of location within the component to be printed [0017, 00241, 0186-0191] using a software;
calculating an exposure strategy including an exposure data, which optimally maps the desired dose distribution for the component [0017, 0024, 01862-0191, 0206, 0208, 0212-0213]; and
printing the component with the calculated exposure data [0206, 0212-02133].
LONG teaches printing a component using a 3D printer via VAT photopolymerization through iterative optimization of projected intensity distribution results in the fabrication of pillars with improved dimensional accuracy and edge definition to select the optimized printing parameters for this work [0017, 0186]. LONG does not explicitly teach using a neural network and a CAD/CAM software to provide an optimal set or sequence of one or more process control parameters for fabricating the object.
However, MEHR teaches a system and method for real-time adaptive control of additive manufacturing processes using machine learning. Especially, MEHR teaches using a CAD/CAM software and an artificial neural network to determine optimized process control parameters for fabricating the object using a machine learning algorithm that has been trained using trained data set [0002, 0004, 0029-0031, 0100-0106, 01334].
LONG and MEHR are analogous art because they are from the same field of endeavor of additive manufacturing with iterative simulation. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to use a neural network and a CAD/CAM software to provide rapid optimization and adjustment of process control parameters for additive manufacturing. The suggestion for doing so would have been obvious to simulate process control operation iteratively to determine optimal process control parameters by using machine learning model and trained data set. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of MEHR with the teachings of LONG for the purpose of providing a neural network and a CAD/CAM software to calculate process control parameters as specified in the claim 1.
As to claim 2, LONG teaches in each simulation the dose distribution in the component is calculated for predetermined exposure data of a print job, which contain the process data to be processed by the 3D printer [0017, 0024, 0186-0191, 0206, 0208, 0212-0213]. And MEHR teaches the neural network is trained by using training data originating from simulations ([0029-0031, 0100-0106, 0133] providing a predicted optimal set or sequence of one or more process control parameters for fabricating the object, wherein the predicted optimal set of one or more process control parameters are derived using a machine learning algorithm that has been trained using the training data set of step (b)).
As to claim 3, LONG teaches iteratively simulate different dose distributions resulting from the exposure data [0017, 0024, 0186-0191, 0206, 0208, 0212-0213]. And MEHR teaches during training of the neural network, a deviation resulting from the data suggested by the neural network is also included as a criterion in a measure for optimization [0002, 0004, 0029-0031, 0100-0106, 0133-0139].
As to claim 4, MEHR teaches during training of the neural network, a printing time or a printing speed resulting from the exposure data suggested by the neural network is also included as a criterion in a measure for optimization [0003, 0078, 0085-0097, 0108, 0139-0140].
As to claim 5, MEHR teaches during training of the neural network, mechanical characteristic values resulting from the exposure data suggested by the neural network are also included as a criterion in a measure for optimization [0002, 0004, 0029-0031, 0100-0106, 0133-0139].
As to claim 6, MEHR teaches when training the neural network, a dimensional accuracy resulting from the exposure data suggested by the neural network is also included as a criterion in a measure for optimization [0002, 0004, 0029-0031, 0100-0106, 0133-0139].
As to claim 7, MEHR teaches additionally a triangulation of the component to be printed is input into the neural network, and the neural network calculates a layer decomposition of the component to be printed [0002, 0004, 0029-0031, 0084, 0096-0106, 0133-0139].
As to claim 8, MEHR teaches the neural network is implemented by hardware or software, the software comprising computer-readable code which, when executed on a computing unit connected or connectable to a 3D printer, causes the 3D printer to print the component according to the calculated exposure data [0002, 0004, 0029-0031, 0100-0106, 0133].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over LONG in view of MEHR, and further in view of Thompson (US 20190126536 A1) .
As to claim 10, LONG teaches a 3D printing system comprising a 3D printer, comprises: a vat for receiving liquid photoreactive resin for producing a solid component [Figs. 3, 7A] [0017, 0214]; a building platform [Figs. 3, 7A] [0093, 0180, 0189]; a projector for projecting the layer geometry [Figs. 3, 7A] [0093, 0180-0189]; a transport apparatus for at least moving the building platform down and up [Figs. 3, 7A] [0024, 0093, 0180]; and a control device for controlling the projector and the transport apparatus [Figs. 3, 7A] [0017, 0024, 0185]; wherein the control device causes printing of the component according to the calculated exposure data [0206, 0212-0213].
MEHR teaches the neural network according to claim 1, and/or comprises a communication interface for receiving the exposure data calculated by the neural network according to claim 1 [0002, 0004, 0029-0031, 0100-0106, 0133].
LONG and MEHR do not explicitly teach the detail structure of the 3D printer such as the vat having an at least partially transparent bottom for receiving liquid photoreactive resin for producing a solid component; the building platform for pulling the component out of the vat layer by layer; the projector for projecting the layer geometry onto the transparent bottom; and the transport apparatus for at least moving the building platform down and up in the vat.
However, Thompson teaches a vat-based additive manufacturing apparatus having a vat [vat 11] having an at least partially transparent bottom for receiving liquid photoreactive resin for producing a solid component; a building platform[stage 14] for pulling the component out of the vat layer by layer; a projector [projector 48] for projecting the layer geometry onto the transparent bottom; a transport apparatus [actuator 32] for at least moving the building platform down and up in the vat [Figs. 1-4] [0022-0028, 0030-0034, 0040, 0048-0055].
LONG and MEHR and Thompson are analogous art because they are from the same field of endeavor of additive manufacturing. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to use different type of additive manufacturing apparatus for producing a component layer-by-layer. The suggestion for doing so would have been obvious to use a vat-based additive manufacturing apparatus for curable material handling in additive manufacturing. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of Thompson with the teachings of LONG and MEHR for the purpose of using a vat-based 3D printer as specified in the claim 10.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIPENG WANG whose telephone number is (571)272-5437. The examiner can normally be reached Monday-Friday 10-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at 5712722279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZHIPENG WANG/Primary Examiner, Art Unit 2115
1 [0024] FIG. 14 is a flowchart of a method to determine the process parameters for printing with the S-MPVP system. First the desired energy distribution (ER) is numerically computed. Then, through the use of known parameters (pixel intensity distribution measured via in-situ computer vision technique and resin curing properties (Ec and Dp), the scan speed (V) and intensity (I) required to fabricate the part are iteratively determined. Additional constrains such as speed limit of the linear stages (Vlim) and the maximum intensity of the UV lamp are supplied to ensure the predicted parameters are within achievable ranges.
2 [0186] The reference energy distribution required to fabricate an accurate test specimen was numerically determined by setting all the energy levels equal to the resin's Ec. Then, the bitmap pattern corresponding to a layer to be fabricated was fed into the irradiance model (Equation 2) and the actual intensity distribution on the resin surface was computed. Using an algorithm to iteratively select the exposure time and projection intensity, the cured specimen dimensions were simulated with the S-MPVP model and multiple energy distributions were generated. The simulated cure dimensions were then compared against the desired specimen dimension. The combination of exposure time and intensity that resulted in fabrication of feature with error <10 μm was selected for specimen fabrication. The flowchart for the process parameter optimization is shown in FIG. 14.
3 [0213] Based on predetermined exposure time for a desired layer thickness (via the Jacobs equation), a characterization specimen with square pillars was fabricated. The printed pillar (FIG. 6E) and the simulations of the energy profiles for the Schwarz lattice (FIGS. 17A-17B) for the photocurable latex demonstrate poor edge definition and a disagreement between the projected cure width (I.sub.w) and the design-specified width (I.sub.wd) due to light scattering. An optimization scheme (Scheme 8) corrected this inaccuracy, as demonstrated in FIG. 6F, by iteratively varying the exposure time (t) and pixel gray-scaling ratio (p), which enabled both gross and fine control of cure width, respectively. As an illustrative example, the utilization of this optimization scheme to print a layer of the Schwarz primitive lattice from photocurable latex yielded an adjusted exposure time, gray-scaling ratio and layer thickness of 8 s, 0.7 and 129 μm, respectively. Specimens were printed with a layer thickness of 100 μm to improve inter-layer network formation.
4 [0133] Disclosed herein are methods and systems for providing real-time adaptive control of deposition processes, e.g., additive manufacturing or welding processes. In general, the disclosed methods comprise a) providing an input design geometry for an object (e.g., a 3D CAD model); b) providing a training data set, wherein the training data set comprises process simulation data, process characterization data, post-build inspection data, or any combination thereof, for a plurality of design geometries or portions thereof that are the same as or different from the input design geometry of step (a); c) providing a predicted optimal set or sequence of one or more process control parameters for fabricating the object, wherein the predicted optimal set of one or more process control parameters are derived using a machine learning algorithm that has been trained using the training data set of step (b); and d) performing the deposition process, e.g., an additive manufacturing process, to fabricate the object, wherein real-time process characterization data is provided by one or more sensors as input to the machine learning algorithm to adjust one or more process control parameters in real-time. In some embodiments, steps (b)-(d) are performed iteratively and the process characterization data, post-build inspection data, or any combination thereof for each iteration is incorporated into the training data set. The disclosed process control methods may be used for any of a variety of deposition processes, including additive manufacturing processes, known to those of skill in the art, for example, stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), electronic beam melting (EBM) process, laser beam welding, MIG (metal inert gas) welding, TIG (tungsten inert gas) welding, and the like. In a preferred embodiment, the disclosed process control methods are applied to a liquid-to-solid free form deposition process, for example, to a laser metal-wire deposition process.