System and method for generating fabrication parameters of a part to be fabricated

Patent metadata

Field	Value
Publication number	US-11635747-B2
Application number	US-202017130962-A
Country	US
Kind code	B2
Filing date	Dec 22, 2020
Priority date	Oct 27, 2016
Publication date	Apr 25, 2023
Grant date	Apr 25, 2023

Abstract

A method for generating a quote for fabrication of a part to be fabricated includes receiving, from a customer device associated with a customer, a design request for a part to be fabricated by a fabrication process, the design request including a two-dimensional drawing file representing the part to be fabricated and descriptive information including a descriptive datum. The method includes extracting a first feature from the 2D drawing file, wherein the first feature represents a geometry of the part to be fabricated. The method includes extracting a second feature from the descriptive information, wherein the second feature represents the descriptive datum. The method includes generating, as a function of the first and second features, a quote for fabrication for the part to be fabricated, the quote for fabrication including a cost and time to fabricate the part to be fabricated and sending the quote for fabrication to the customer.

First claim

What is claimed is:

1. A method for generating parameters of a part to be fabricated, the method comprising:
receiving a design request for a part to be fabricated by a fabrication process, wherein the design request includes descriptive information of the part to the fabricated, wherein the descriptive information includes an audio file, and wherein the design request includes a manufacturing tool selected from a plurality of manufacturing tools;
generating a three-dimensional (3D) model file as a function of the descriptive information, wherein the 3D model file is generated using speech-to-text processing;
determining a measure of similarity for the 3D model file and selecting one or more model files based on their respective measures of similarity;
ranking the model files according to their respective measures of similarity and selecting model files having a ranking higher than a threshold ranking
determining an orientation on a build plate for fabricating the part to be fabricated, wherein determining an orientation comprises selecting an orientation to maximize the number of instances of the part to be fabricated that a printer can print simultaneously;
generating, as a function of the 3D model file and the selected manufacturing tool, a plurality of fabrication parameters-for the part to be fabricated, wherein the fabrication parameters include a cost, a time, and the selected manufacturing tool to fabricate the part to be fabricated; and
sending the plurality of fabrication parameters to the customer.

2. The method of claim 1 , wherein the descriptive information comprises a number of instances of the part to be manufactured.

3. The method of claim 1 , wherein generating the plurality of fabrication parameters comprises using one or more machine-learning modules, wherein the one or more machine-learning modules are configured to:
determine, as a function of a first and a second extracted features, a plurality of fabrication methods associated with a part to be fabricated; and
determine, for each of the fabrication methods of the plurality of fabrication methods, a respective cost associated with the part to be fabricated.

4. The method of claim 1 , wherein generating the plurality of fabrication parameters comprises using one or more machine-learning modules, wherein the one or more machine-learning modules are configured to:
receive a plurality of fabrication methods associated with a part to be fabricated;
score each fabrication method of the plurality of fabrication methods;
select, as a function of the scores for each fabrication method of the plurality of fabrication methods, a fabrication method from the plurality of fabrication methods; and
send, to the customer, the selected fabrication method.

5. The method of claim 1 , wherein a parameter of the plurality of fabrication parameters of the part to be fabricated is associated with at least an anticipated fabricating tool.

6. A system for generating fabrication parameters for fabrication of a part to be fabricated, the system comprising:
a computing device, the computing device configured to:
receive, from a customer device associated with a customer, a design request for a part to be fabricated by a fabrication process, the design request includes-descriptive information of the part to the fabricated, wherein the descriptive information includes an audio file, and wherein the design request includes a manufacturing tool selected from a plurality of manufacturing tools;
generate a three-dimensional (3D) model file as a function of the descriptive information, wherein the 3D model file is generated using speech-to-text processing;
determine a measure of similarity for the 3D model file and selecting one or more model files based on their respective measures of similarity;
rank the model files according to their respective measures of similarity and select model files having a ranking higher than a threshold ranking;
determine an orientation on a build plate for fabricating the part to be fabricated, wherein determining an orientation comprises selecting an orientation to maximize the number of instances of the part to be fabricated that a printer can print simultaneously;
select from a plurality of distinct manufacturing tools, a manufacturing tool; and
generate, as a function of the 3D model file and the selected manufacturing tool, a plurality of fabrication parameters for the part to be fabricated, wherein the fabrication parameters include a cost, a time, and the selected manufacturing tool to fabricate the part to be fabricated; and;
send the plurality of fabrication parameters to the customer.

7. The system of claim 6 , wherein the descriptive information comprises a number of instances of the part to be manufactured.

8. The system of claim 6 , further comprising one or more machine-learning modules operating on the computing device, wherein the one or more machine-learning modules is configured to:
determine, as a function of a first and a second extracted features, a plurality of fabrication methods associated with a part to be fabricated; and
determine, for each of the fabrication methods of the plurality of fabrication methods, a respective cost associated with the part to be fabricated.

9. The system of claim 6 , further comprising one or more machine-learning modules operating on the computing device, wherein the one or more machine-learning modules are configured to:
receive a plurality of fabrication methods associated with a part to be fabricated;
score each fabrication method of the plurality of fabrication methods;
select, as a function of the scores for each fabrication method of the plurality of fabrication methods, a fabrication method from the plurality of fabrication methods; and
send, to the customer, the selected fabrication method.

10. The system of claim 6 , wherein a parameter of the plurality of fabrication parameters of the part to be fabricated is associated with at least an anticipated fabricating tool.

11. The method of claim 1 , wherein the audio file is included in a video file.

12. The method of claim 1 , further comprising identifying from a plurality of 3D models in a model file library, at least a 3D model as a function of descriptive feature, wherein the plurality of 3D models further comprises at least a model feature associated with each 3D model of the plurality of 3D models.

13. The method of claim 1 , further comprising:
receiving, as a function of the design request, at least one mechanical requirement, wherein the mechanical requirement corresponds to the use of the part to be fabricated; and
selecting a part orientation as a function of the mechanical requirement.

14. The method of claim 1 , further comprising:
determining at least one keyword from the design request using natural language processing; and
identifying at least one structure as a function of the at least one keyword using a machine-learning model.

15. The method of claim 14 , wherein the machine-learning model includes a decision tree, a neural network, or a logistic classifier.

16. The system of claim 6 , wherein the audio file is included in a video file.

17. The system of claim 6 , wherein the computing device is further configured to identify from a plurality of 3D models in a model file library, at least a 3D model as a function of a descriptive feature, wherein the plurality of 3D models further comprises at least a model feature associated with each 3D model of the plurality of 3D models.

18. The system of claim 6 , wherein the comput

Assignees

• Desprez Llc

Inventors

• Schwartz Jonathan
• Friefeld Max
• Ortlieb Oliver

Classifications

• B33Y50/00

  Data acquisition or data processing for additive manufacturing · CPC title

• B29C64/393

  for controlling or regulating additive manufacturing processes · CPC title

• G05B19/41865

  characterised by job scheduling, process planning, material flow · CPC title

• G05B2219/49005

  Map 2-D pattern on 3-D · CPC title

• Y02P90/02

  Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] · CPC title