IP Library › Granted Patent US 12,354,072
Granted Patent B2
US 12,354,072 · App. 17/942,061 · Granted Jul 8, 2025

AI-managed additive manufacturing for value chain networks

Inventors: Charles Howard Cella (Pembroke, MA); Brent Bliven (Austin, TX); Kunal Sharma (Mumbai, IN); Teymour S. El-Tahry (Detroit, MI)
Assignee: STRONG FORCE VCN PORTFOLIO 2019, LLC
G06Q20/14B25J9/163B25J9/1653B25J9/1661B25J9/1671B25J9/1682B25J9/1697B29C64/386B29C64/393B33Y10/00B33Y50/00B33Y50/02G02B3/14G02B26/00G05B13/0265G05B13/042G05B17/02G05B19/402G05B19/4099G05D1/0027G05D1/0297G06F30/27G06N20/00G06N20/20G06Q10/06311G06Q10/0633G06T7/70H04L9/3239H04L9/50H04L63/1441G05B2219/32015G05B2219/40113G05B2219/49023G06F2113/10G06Q10/06G06Q10/0631G06Q10/063114G06Q10/06313G06Q10/06316G06Q10/0831G06Q10/0833G06Q10/087G06Q30/0201G06Q2220/00G06T2207/20081
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Quick Facts
Patent No.
US 12,354,072
App. No.
17/942,061
Filed
Sep 9, 2022
Granted
Jul 8, 2025
Kind
B2
Art Unit
2117
USPC
700/98
Abstract

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

Claims (90)

1. A distributed manufacturing network information technology system, comprising:

a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities;

a set of applications for enabling the cloud-based additive manufacturing management platform to manage a set of distributed manufacturing network entities; and

an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to optimize manufacturing and value chain workflows,

wherein the artificial intelligence system is configured to build, maintain, and provide a library of parts with preconfigured parameters,

wherein the library of parts is searchable by two or more of: materials, properties, functions, equipment compatibility, shape compatibility, interface compatibility, part type, part class, industry, or compliance,

wherein the artificial intelligence system uses a machine learning model to learn on the training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to optimize the manufacturing and value chain workflows,

wherein the machine learning model is configured to learn on the training set through at least one of: supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, or association rules to optimize the manufacturing and value chain workflows,

wherein the artificial intelligence system is configured to provide the optimization of the manufacturing and value chain workflows by optimizing dynamic nesting for the set of distributed manufacturing network entities to maximize a number of printed parts while minimizing raw material waste,

wherein the optimized dynamic nesting includes at least one of: optimized dynamic two-dimensional (2D) nesting, optimized dynamic two-and-a-half-dimensional (2.5D) nesting, or optimized dynamic three-dimensional (3D) nesting,

wherein the artificial intelligence system is configured to provide the optimized dynamic nesting by using a nesting algorithm implemented by the machine learning model,

wherein the machine learning model includes at least one of: an artificial neural network, a decision tree, a support vector machine, a Bayesian network, or a genetic algorithm that is used to learn on the training set in order to provide the optimized dynamic nesting, and

wherein the nesting algorithm is configured to provide the optimized dynamic nesting by minimizing travel time for a cutting tool of at least one of the set of distributed manufacturing network entities.

2. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to provide optimization and process control across an entire lifecycle of manufacturing from product conception and design through manufacturing and distribution to service and maintenance.

3. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to provide generative design and topology optimization to determine at least one product design suitable for fabrication.

4. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to optimize a part orientation process for superior production results.

5. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to optimize a toolpath generation process.

6. The distributed manufacturing network information technology system of claim 1 wherein the user interface includes a dashboard providing tracking and tracing of production history of one or more 3D printed parts.

7. The distributed manufacturing network information technology system of claim 1 wherein the user interface includes a dashboard providing batch traceability to identify parts from a same batch.

8. The distributed manufacturing network information technology system of claim 1 wherein the user interface includes a digital twin interface to resolve queries from a user of a network related to a part or a product.

9. The distributed manufacturing network information technology system of claim 1 wherein the user interface includes a virtual reality (VR) interface configured to enable a user to build 3D models in VR.

10. The distributed manufacturing network information technology system of claim 1 wherein the set of applications includes at least one of: production management applications, production reporting applications, production analysis applications, or value chain management applications.

11. The distributed manufacturing network information technology system of claim 1 wherein at least one application of the set of applications is an order tracking application configured to track a product order through its movement in a distributed manufacturing network.

12. The distributed manufacturing network information technology system of claim 1 wherein at least one application of the set of applications is a workflow management application configured to manage a complete 3D printing production workflow.

13. The distributed manufacturing network information technology system of claim 1 wherein at least one application of the set of applications is an alerts and notifications application configured to generate alerts, notifications, and reports about one or more events in a distributed manufacturing network to a user or customer of the distributed manufacturing network.

14. The distributed manufacturing network information technology system of claim 13 wherein the alerts and notifications application is configured to transmit alerts related to print errors or failures to a computing device of a user.

15. The distributed manufacturing network information technology system of claim 1 wherein at least one application of the set of applications is a payment gateway application configured to manage an entire billing, payment, and invoicing process for a customer ordering a product using a distributed manufacturing network.

16. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to automatically classify and cluster 3D printed parts based on similarity of attributes, including at least one of: physical attributes, shapes, functional attributes, material attributes, performance attributes, or economic attributes.

17. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to:

analyze usage patterns associated with one or more users;

learn user preferences with respect to at least one of: outputs, materials, orientations, timing, colors, shapes, or print strategies;

use the user preferences to develop a profile for the one or more users; and

suggest preferences of the one or more users based on the profile.

18. The distributed manufacturing network information technology system of claim 1 wherein the artificial intelligence system is configured to minimize material waste production during an additive manufacturing process.

19. The distributed manufacturing network information technology system of claim 1 wherein:

the artificial intelligence system is configured to manage real time dynamics affecting inventory levels for smart inventory and materials management in a distributed manufacturing network; and

the real time dynamics affecting inventory levels include at least one of: a set of demand factors or a set of supply factors.

20. The distributed manufacturing network information technology system of claim 1 wherein:

the artificial intelligence system is configured to build, maintain, and provide a library of parts with preconfigured parameters, and

the library of parts is searchable by two or more of; materials, properties, functions, equipment compatibility, shape compatibility, interface compatibility, part type, part class, industry, or compliance.

21. The distributed manufacturing network information technology system of claim 1 wherein:

the distributed manufacturing network information technology system is configured to provide 3D printed products that conform to a body part or an anatomy of a user; and

at least one of the 3D printed products is a wearable device including at least one of: eyewear, footwear, earwear, or headgear.

22. The distributed manufacturing network information technology system of claim 1 wherein the distributed manufacturing network information technology system is configured to support at least one of: manufacture, replacement, or service of parts by using at least one of: portable additive manufacturing units equipped with robotic or other autonomous mobility, mobile additive manufacturing units equipped with robotic or other autonomous mobility, units positioned in or on vehicles, or units located in sufficiently close proximity to a customer.

23. The distributed manufacturing network information technology system of claim 1 wherein the distributed manufacturing network information technology system is configured to support printing of parts or products using an additive manufacturing unit with multiple source materials and multiple extrusion nozzles that allow for voxelated soft matter printing or metal printing via multi-material, multi-nozzle printing, with high-speed switching between materials.

24. The distributed manufacturing network information technology system of claim 1 wherein the distributed manufacturing network information technology system is configured to support printing of parts or products including functionally graded materials (FGMs).

25. The distributed manufacturing network information technology system of claim 1 wherein the dynamic nesting is optimized such that the nesting algorithm evaluates an individual part priority to ensure high priority parts are awarded priority of positioning.

26. The distributed manufacturing network information technology system of claim 1 wherein the dynamic nesting is optimized such that the nesting algorithm integrates with support structure optimization.

27. A distributed manufacturing network information technology system, comprising:

a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities;

a set of applications for enabling the cloud-based additive manufacturing management platform to manage a set of distributed manufacturing network entities; and

an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to optimize manufacturing and value chain workflows,

wherein the artificial intelligence system is configured to build, maintain, and provide a library of parts with preconfigured parameters,

wherein the library of parts is searchable by two or more of: materials, properties, functions, equipment compatibility, shape compatibility, interface compatibility, part type, part class, industry, or compliance,

wherein the artificial intelligence system uses a machine learning model to learn on the training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to optimize the manufacturing and value chain workflows,

wherein the machine learning model is configured to learn on the training set through at least one of: supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, or association rules to optimize the manufacturing and value chain workflows,

wherein the artificial intelligence system is configured to provide the optimization of the manufacturing and value chain workflows by optimizing dynamic nesting for the set of distributed manufacturing network entities to maximize a number of printed parts while minimizing raw material waste,

wherein the optimized dynamic nesting includes at least one of: optimized dynamic two-dimensional (2D) nesting, optimized dynamic two-and-a-half-dimensional (2.5D) nesting, or optimized dynamic three-dimensional (3D) nesting,

wherein the artificial intelligence system is configured to provide the optimized dynamic nesting by using a nesting algorithm implemented by the machine learning model,

wherein the machine learning model includes at least one of an artificial neural network, a decision tree, a support vector machine, a Bayesian network, or a genetic algorithm that is used to learn on the training set in order to provide the optimized dynamic nesting, and

wherein the dynamic nesting is optimized such that the nesting algorithm integrates with support structure optimization.

28. The distributed manufacturing network information technology system of claim 27 wherein the dynamic nesting is optimized such that the nesting algorithm evaluates an individual part priority to ensure high priority parts are awarded priority of positioning.

29. The distributed manufacturing network information technology system of claim 1 further comprising a distributed ledger system integrated with digital threads of the set of distributed manufacturing network entities for storing information on at least one of: events, activities, or transactions related to the set of distributed manufacturing network entities, wherein the training set includes the information stored in the distributed ledger system.

30. The distributed manufacturing network information technology system of claim 29 further comprising:

a smart contract system configured to implement and manage a set of smart contracts via the distributed ledger system,

wherein the set of smart contracts is configured to automate and manage the manufacturing and value chain workflows, and the set of smart contracts represents at least one customer order.

31. The distributed manufacturing network information technology system of claim 30 further comprising:

a matching system configured to match the set of smart contracts representing the at least one customer order with at least one distributed manufacturing network entity of the set of distributed manufacturing network entities,

wherein the matching is based on the optimized dynamic nesting for the at least one distributed manufacturing network entity.

32. The distributed manufacturing network information technology system of claim 27 wherein the support structure optimization includes optimizing a support structure of the printed parts to minimize at least one of: material costs, print time, post processing, or risk of damage to the printed parts when removing the support structure.

33. The distributed manufacturing network information technology system of claim 27 wherein the support structure optimization includes optimizing a-support structure of the printed parts for an automated, hands-free removal of the support structure.

34. The distributed manufacturing network information technology system of claim 27 wherein:

the artificial intelligence system is congured to build, maintain, and provide a library of parts with preconfigured parameters, and

the library of parts is searchable by two or more of: materials, properties, functions, equipment compatibility, shape compatibility, interface compatibility, part type, part class, industry, or compliance.

35. A computer-implemented method of controlling a distributed manufacturing network information technology system, the computer-implemented method comprising:

managing a set of distributed manufacturing network entities using a cloud-based additive manufacturing management platform;

training a machine learning model of an artificial intelligence system on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to optimize manufacturing and value chain workflows,

wherein the artificial intelligence system is configured to build, maintain, and provide a library of parts with preconfigured parameters,

wherein the library of parts is searchable by two or more of: materials, properties, functions, equipment compatibility, shape compatibility, interface compatibility, part type, part class, industry, or compliance, and

wherein the training is performed using at least one of: supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, or association rules to optimize the manufacturing and value chain workflows; and

optimizing the manufacturing and value chain workflows by optimizing dynamic nesting for the set of distributed manufacturing network entities to maximize a number of printed parts while minimizing raw material waste,

wherein the optimized dynamic nesting includes at least one of: optimized dynamic two-dimensional (2D) nesting, optimized dynamic two-and-a-half-dimensional (2.5D) nesting, or optimized dynamic three-dimensional (3D) nesting,

wherein the optimization of the dynamic nesting uses a nesting algorithm implemented by the machine learning model,

wherein the machine learning model includes at least one of: an artificial neural network, a decision tree, a support vector machine, a Bayesian network, or a genetic algorithm that is used to learn on the training set in order to provide the optimized dynamic nesting, and

wherein the optimization of the dynamic nesting is by at least one of:

minimizing travel time for a cutting tool of at least one of the set of distributed manufacturing network entities, or

integrating the nesting algorithm with support structure optimization.

36. The computer-implemented method of claim 35 wherein:

the artificial intelligence system is configured to build, maintain, and provide a library of parts with preconfigured parameters, and

the library of parts is searchable by two or more of: materials, properties, functions, equipment compatibility, shape compatibility, interface compatibility, part type, part class, industry, or compliance.

Assignments (2)
CORRECTIVE ASSIGNMENT TO CORRECT THE SECOND ASSIGNOR'S FIRST NAME ON THE COVER SHEET PREVIOUSLY RECORDED AT REEL: 062070 FRAME: 0908. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Dec 20, 2022
From: CELLA, CHARLES HOWARD; BLIVEN, BRENT; SHARMA, KUNAL; EL-TAHRY, TEYMOUR S.
To: STRONG FORCE VCN PORTFOLIO 2019, LLC
Reel/Frame 062183/0878 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 13, 2022
From: CELLA, CHARLES HOWARD; BLIVEN, BREN; SHARMA, KUNAL; EL-TAHRY, TEYMOUR S.
To: STRONG FORCE VCN PORTFOLIO 2019, LLC
Reel/Frame 062070/0908 →
Priority Claims (2)
IN 202111029964 · Jul 3, 2021 · national
IN 202111036187 · Aug 10, 2021 · national
Continuity (4)
Continuation PCTUS2021064233 · Dec 17, 2021
Provisional Application 63185348 · May 6, 2021
Provisional Application 63127983 · Dec 18, 2020
Related Publication 20230135553A1 · May 4, 2023
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