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 .
1. Claims 1-12 are presented for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
2. 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.
2.1 Claim(s) 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yue (Analytical Prediction of Residual Stress in the Machined Surface during Milling) in view of Lappas (US 20180095450 A) further in view of Jiang (CN 113191050 B).
Regarding claim 1, Yue discloses method for estimating a residual stress field in a workpiece during machining (Abstract, an analytical prediction model for residual stress during milling is established), said workpiece being obtained from a raw workpiece (Page 1, Par 2, established an analytical model of residual stress for flank milling or plastic) said method comprising at least the following steps executed successively:
a measurement step of measuring geometric parameters of the workpiece at a given moment during machining thereof at which a residual stress field in said workpiece is to be known, in such a manner as to determine a geometric profile of said workpiece (Page 2, section 2, Fig. 2 b and c, the cutting force can be calculated based on the uniformly distributed stress on the shear zone, and the cutting mechanism is analyzed based on the geometric relationship, Page 9, section 4.1, When calculating the residual stress, the stress loading history inside the workpiece needs to beknown and it can be calculated by Hertzian rolling contact theory);
a first estimation step of estimating, from the geometric profile (Page 2, section 2, Fig. 2 b and c, analyzed based on the geometric relationship) and an estimation model subsequent deformations that the workpiece is liable to undergo during further machining (Page 1, Par. 2 (introduction), analytical model for residual stress regeneration in milling and predicted the residual stress distribution in multi-pass milling and proposed an analytical model to predict the deformation of thin-walled parts caused by residual stress); and
a second estimation step of estimating from the subsequent deformations estimated during the first estimation step (Page 1, Par. 2 (introduction), analytical model for residual stress regeneration in milling and predicted the residual stress distribution in multi-pass milling and proposed an analytical model to predict the deformation of thin-walled parts caused by residual stress).
Yue fails to disclose estimation model being trained beforehand by machine learning on so-called training workpieces in such a manner as to be able to use the geometric profile of the workpiece; a simulation models the residual stress field in the workpiece, said simulation model being a generic physical model based on simulations defining a variability of the residual stress field in the workpiece as a function of manufacturing parameters of the raw workpiece.
However, Lappas discloses the estimation model being trained beforehand by machine learning on so-called training workpieces in such a manner as to be able to use the geometric profile of the workpiece ([0022], [0039], [0169], the acceptable dimensional accuracy range corresponds to a predetermined threshold rang and comparing at least one predicted deformation with a learning algorithm, for example, neural networks, or machine learning with a predetermine threshold), and prerecorded data on the training workpieces ([0039], the acceptable dimensional accuracy range corresponds to a predetermined threshold range) to estimate said subsequent deformations of the workpiece ([0038]-[0039], generating the three-dimensional object using program instruction employing the adjusted physics model, and comparison employs (neural networks or a learning algorithm) and comparing at least one predicted deformation of the simulated object with at least one deformation of the test object);
Yue and Lappas are analogous art. They relate to predicating the deformation of workpiece.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify reduction of deformation, taught by Lappas, incorporated with analytical prediction model for residual stress during milling, taught by Yue, in order to accurately tracking of 3D object formation on the 3D printing that is assistance in reducing and controlling deformation that occur during formation of a physical 3D object.
The combination of Yue and Lappas fail to disclose a simulation model the residual stress field in the workpiece, said simulation model being a generic physical model based on simulations defining a variability of the residual stress field in the workpiece as a function of manufacturing parameters of the raw workpiece.
However, Jiang discloses a simulation model the residual stress field in the workpiece (Abstract, a finite element simulation model), said simulation model being a generic physical model based on simulations defining a variability of the residual stress field in the workpiece as a function of manufacturing parameters of the raw workpiece (Abstract, page 3, Par. 5, a finite element simulation model is established to predict the deformation of the workpiece according to the initial residual stress distribution obtained by actual measurement and the removal amount of the upper surface material and the lower surface material of the workpiece).
Jiang, Yue and Lappas are analogous art. They relate to predicating the deformation of workpiece.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify a workpiece deformation simulation prediction, taught by Jiang, incorporated with the teaching of Yue and Lappas, as state above, in order to obtain the stress distribution situation which is more consistent with the actual situation, and greatly improves the precision of the workpiece deformation simulation prediction model.
Regarding claim 2, Lappas discloses during the measurement step (comparing is of location, shape, volume, fundamental length scale, and/or a material property) a principal component analysis ([0022], performing a data analysis, the data analysis comprises principal component analysis (PCA)),
is applied to the measured geometric parameters in such a manner as to reduce a dimension of the geometric profile of the workpiece to be determined (Abstract, [0008], [0047], determining a strain and/or a stress in a three-dimensional object, comprising: computing a plurality of modes employing a geometric model of a requested three-dimensional object, a geometric model, a reduction of deformation that may be caused by the forming process of the 3D object).
Regarding claim 3, Lappas discloses the estimation model is trained ([0022], [0039], [0169], comparing at least one predicted deformation with a learning algorithm, for example, neural networks, or machine learning) using a regression method ([0022], the data analysis linear regression, least squares fit, Gaussian process regression, kernel regression, nonparametric multiplicative regression (NPMR), regression tree) enabling estimation of the subsequent deformations ([0022],[0030], the estimated thermo-mechanical change comprises an estimated thermo-mechanical deformation), based on data measured on the training workpieces ([0039], the learning module comprises an inelastic response to generating the three-dimensional object).
Regarding claim 4, Jiang discloses the geometric profile of the workpiece (Page 7, Pag. 4, Page 9, Par. 17, workpiece creates a geometric model of the workpiece, and define material properties) determined in the measurement step is used to enrich the estimation model (Abstract, Page 9, Par. 1, workpiece deformation simulation prediction method for optimizing the initial residual stress is the accuracy of a workpiece deformation simulation prediction model is low due to inaccurate measurement of the initial residual stress is solved optimizing initial residual stress).
Regarding claim 5, the combination of Jiang and Lappas disclose:
Jiang discloses the simulation model of the residual stress field is based on finite element simulations (Abstract, Page 3, Par. 2, Page 4, section 3.2, a finite element simulation model is established to predict the deformation of the workpiece according to the initial residual stress distribution obtained by actual measurement and the removal amount of the upper surface material and the lower surface material of the workpiece); and
Lappas discloses constructed with a singular value decomposition ([0045],[0068], [0170], generating a simulated object is operation (a), and forming an adjusted physics model is operation and performing singular value decomposition).
Regarding claim 6, Jiang discloses the simulation model is configured to define a variability of the residual stress field in the workpiece based on manufacturing parameters of the raw workpiece (Page 4-7, a workpiece deformation simulation prediction method based on MSVR-GA optimization initial residual stress mainly comprises the residual stress 1 distribution of the workpiece along the thickness variable, said raw workpiece having undergone at least one of the following manufacturing operations: forging, heat treatment, cold rolling, machining (Page 4, Par. 2, Heat treating of workpieces).
Allowable Subject Matter
3. Claims 7-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The allowability of the dependent claims 7, resides, at least in part, in that closest prior art of Wie-wing (US 2013/0091955 A1) discloses a process for machining a raw workpiece to produce a finished workpiece ([0032], a workpiece 10 has been formed by a known manufacturing process such as roll forming), said process comprising the following steps executed successively: an initial measurement step of measuring geometric parameters of the raw workpiece Abstract, measuring the deflection of the workpiece at the cut point); an initial machining step of carrying out at least one initial machining operation on the raw workpiece to obtain a first intermediate workpiece ([0038] A machine tool 42 can be lowered onto the upper surface of the workpiece 10 at the corresponding position to the deflection probe 28. The machine tool 42 is capable of taking sequential cuts of a known distance into the workpiece 10 and, as such, comprises a motion transducer in order to record the depth of cut into the workpiece 10); and a first processing step of estimating a residual stress field in the first intermediate workpiece using the method as claimed in claim 1 (see claim 1 rejection); however, the prior art does not disclose or suggest, alone or in combination, a second processing step of estimating, from the residual stress field in the first intermediate workpiece estimated in the first processing step, a subsequent mechanical behavior of said first intermediate workpiece during subsequent machining operations; a correction step of modifying the value of at least one machining parameter of said subsequent machining operations as a function of the subsequent mechanical behavior of the first intermediate workpiece estimated in the second processing step to minimize deformations of the first intermediate workpiece during said subsequent machining operations; and a machining step of carrying out at least one machining operation on the first intermediate workpiece, said machining operation taking into account said modified value of said machining parameter(s) in such a manner as to obtain a second intermediate workpiece, in combination with the other elements and features of the claimed invention.
As claims 8-12 are directly or indirectly dependent on claim 7, those claims are also allowable at least by virtue of their dependency.
Citation Pertinent prior art
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang (US 2015/0356402 A1) discloses calculating and predicting residual stresses and distortion in cast aluminum components during a quenching or cooling process, and more particularly to rapidly performing such calculating and predicting such that accurate results are obtained without the use of traditional, time-intensive predictive approaches
Prime (US6470756B1) discloses determining the residual stress within an elastic object. In the method, an elastic object is cut along a path having a known configuration. The cut creates a portion of the object having a new free surface. The free surface then deforms to a contour which is different from the path. Next, the contour is measured to determine how much deformation has occurred across the new free surface.
Yilbas (US20100305910A1) discloses the method of modeling residual stresses during laser cutting utilizes thermal diffusion and stress equations and a discretization numerical method to model temperature variation and residual stresses in a substrate material due to laser cutting therethrough of small-diameter holes.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/ Primary Examiner, Art Unit 2119