# US 11,347,201 B2 · Methods and apparatus for machine learning predictions of manufacture processes

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- Number: US 11,347,201 B2
- Document ID: US11347201B2
- Status: Granted
- Kind: B2
- Application number: 16/928,499
- Filed: 2020-07-14
- Published: 2020-11-05
- Granted: 2022-05-31
- Earliest claimed: 2016-11-01
- Assignees: Xometry, Inc.
- Applicants: Xometry, Inc.
- Inventors: Valerie R. Coffman; Yuan Chen; Luke S. Hendrix; William J. Sankey; Joshua Ryan Smith; Daniel Wheeler
- Web page: https://patentgrep.com/patent/US11347201B2

## CPC

- G05B 19/4097: characterised by using design data to control NC machines, e.g. CAD/CAM (G05B19/4093 takes precedence)
- B33Y 50/00: Data acquisition or data processing for additive manufacturing
- G05B 19/401: characterised by control arrangements for measuring, e.g. calibration and initialisation, measuring workpiece for machining purposes (G05B19/19 takes precedence)
- G06F 30/00: Computer-aided design [CAD]
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G05B 2219/35134: 3-D cad-cam
- G05B 2219/35499: Model of process, machine and parameters
- G06F 2113/10: Additive manufacturing, e.g. three-dimensional [3D] printing
- Y02P 90/02: Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- B33Y: ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## Abstract

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

## Claims

### Claim 1

A system for predicting aspects of a manufacturing process, comprising:
a user interface tool configured to receive a digital representation of a physical object to be manufactured;
one or more analysis engines configured to generate a set of parameters of the physical object to be manufactured;
a first machine learning engine configured to generate a second set of parameters associated with the physical object to be manufactured from at least one parameter of the first set of parameters;
a second machine learning engine configured to generate at least one set of predictions, the at least one set of predictions being at least partially based on one or more parameters of the second set of parameters, and the at least one set of predictions further being associated with a process to manufacture the physical object;
wherein the user interface tool is further configured to provide one or more predictions from the at least one set of predictions to a user.

### Claim 2

A system for predictions of manufacturing processes, comprising:
a graphical user interface tool including computerized instructions for receiving at a compute device, an electronic file including a digital model representative of a physical object to be manufactured, and for returning, in near real-time or faster, responsive information associated with manufacturing the physical object, the responsive information including information related at least to a predictive value generated by a predictive engine:
wherein the predictive value generated by the predictive engine is generated by the predictive engine based at least in part upon (a) at least one of a set of parameters associated with the digital model or information derived from the set of parameters, and (b) one or more neural network machine learning models, at least one of the one or more neural network machine learning models being trained using a collection of digital models of a domain of mechanical objects;
wherein the at least one predictive value corresponds to one or more of the following: predicted cost, set-up time, cycle time, a number of Computer Numerical Control (CNC) operations, requirement of a mill machine, requirement of a lathe machine, requirement of a sheet metal process, a type of blank used, or a type of fixture used;
wherein the graphical user interface also receives at least one manufacturing parameter taken from a group consisting of: an identification of a manufacturing machine, an identification of a fabrication material, or an identification of a manufacturing process; and
at least one of the one or more neural network machine learning models is selected from an available set of a plurality of neural network machine learning models based upon the received manufacturing parameter.

### Claim 3

A system for predictions of manufacturing processes, comprising:
a graphical user interface tool including computerized instructions for receiving, at a compute device, an electronic file including a digital model representative of a physical object to be manufactured, and for returning responsive information associated with manufacturing the physical object, the responsive information including information related at least to a predictive outcome generated by a predictive engine;
wherein the predictive outcome generated by the predictive engine is generated by the predictive engine based at least in part upon (a) at least one of a set of parameters associated with the digital model or information derived from the set of parameters and (b) one or more neural network machine learning models, at least one of the one or more neural network machine learning models, being trained using a collection of digital models of a domain of mechanical objects;
wherein the at least one predictive outcome corresponds to one or more of the following: predicted cost, set-up time, cycle time, a number of Computer Numerical Control (CNC) operations, requirement of a mill machine, requirement of a lathe machine, requirement of a sheet metal process, a type blank used or a type of fixture used;
wherein the graphical user interface also receives an identification of a fabrication parameter; and
the predictive engine predicts a manufacturing feasibility outcome for the physical object using the identified fabrication parameter.