Daniel R Wheeler

US Patent:

20220365509, Nov 17, 2022

Filed:

May 23, 2022

Appl. No.:

17/750549

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06F 30/00
G06N 20/20
G06N 5/04

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.

US Patent:

20210365003, Nov 25, 2021

Filed:

Aug 10, 2021

Appl. No.:

17/398409

Inventors:

- Gaithersburg MD, US
Mark WICKS - Alexandria VA, US
Daniel WHEELER - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 7/00
G06N 3/12
G06N 20/00
G06N 20/20

Abstract:

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

US Patent:

20200348646, Nov 5, 2020

Filed:

Jul 14, 2020

Appl. No.:

16/928499

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06F 30/00
G06N 5/04

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.

US Patent:

20200183355, Jun 11, 2020

Filed:

Feb 10, 2020

Appl. No.:

16/786454

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 5/04
G05B 19/401
G06N 20/00
G06F 30/00

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.

US Patent:

20190339669, Nov 7, 2019

Filed:

Jun 27, 2019

Appl. No.:

16/454756

Inventors:

- Gaithersburg MD, US
Mark WICKS - Alexandria VA, US
Daniel WHEELER - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 20/00
G06N 3/12
G06N 7/00
G06N 20/20

Abstract:

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

US Patent:

20190271966, Sep 5, 2019

Filed:

Apr 26, 2019

Appl. No.:

16/395940

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06N 5/04
G06F 17/50

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.

US Patent:

20180341246, Nov 29, 2018

Filed:

Jul 26, 2018

Appl. No.:

16/046519

Inventors:

- Geithesburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

Assignee:

Xometry, Inc. - Geithesburg MD

International Classification:

G05B 19/4097
G06F 17/50
G05B 19/401
G06N 99/00
G06N 5/04

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.

US Patent:

20180120813, May 3, 2018

Filed:

Nov 1, 2016

Appl. No.:

15/340338

Inventors:

- Gaithersburg MD, US
Yuan CHEN - Rockville MD, US
Luke S. HENDRIX - Bethesda MD, US
William J. SANKEY - College Park MD, US
Joshua Ryan SMITH - Washington DC, US
Daniel WHEELER - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 5/04
G06N 99/00

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.