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

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 computing device is further configured to:

receive, 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

select a part orientation as a function of the mechanical requirement.

19. The system of claim 6 , wherein the computing device is further configured to:

determine at least one keyword from the design request using natural language processing; and

identify at least one structure as a function of the at least one keyword using a machine-learning model.

20. The system of claim 19 , wherein the machine-learning model includes a decision tree, a neural network, or a logistic classifier.