US 12,354,072 B2
AI-managed additive manufacturing for value chain networks
Charles Howard Cella, Pembroke, MA (US); Brent Bliven, Austin, TX (US); Kunal Sharma, Mumbai (IN); and Teymour S. El-Tahry, Detroit, MI (US)
Assigned to STRONG FORCE VCN PORTFOLIO 2019, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force VCN Portfolio 2019, LLC, Fort Lauderdale, FL (US)
Filed on Sep. 9, 2022, as Appl. No. 17/942,061.
Application 17/942,061 is a continuation of application No. PCT/US2021/064233, filed on Dec. 17, 2021.
Claims priority of provisional application 63/185,348, filed on May 6, 2021.
Claims priority of provisional application 63/127,983, filed on Dec. 18, 2020.
Claims priority of application No. 202111029964 (IN), filed on Jul. 3, 2021; and application No. 202111036187 (IN), filed on Aug. 10, 2021.
Prior Publication US 2023/0135553 A1, May 4, 2023
Int. Cl. G05B 17/02 (2006.01); B25J 9/16 (2006.01); B29C 64/386 (2017.01); B29C 64/393 (2017.01); B33Y 10/00 (2015.01); B33Y 50/00 (2015.01); B33Y 50/02 (2015.01); G02B 3/14 (2006.01); G02B 26/00 (2006.01); G05B 13/02 (2006.01); G05B 13/04 (2006.01); G05B 19/402 (2006.01); G05B 19/4099 (2006.01); G05D 1/00 (2006.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0633 (2023.01); G06Q 20/14 (2012.01); G06T 7/70 (2017.01); H04L 9/00 (2022.01); H04L 9/32 (2006.01); H04L 9/40 (2022.01); G06F 113/10 (2020.01); G06Q 10/06 (2023.01); G06Q 10/0831 (2023.01); G06Q 10/0833 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0201 (2023.01)
CPC G06Q 20/14 (2013.01) [B25J 9/163 (2013.01); B25J 9/1653 (2013.01); B25J 9/1661 (2013.01); B25J 9/1671 (2013.01); B25J 9/1682 (2013.01); B25J 9/1697 (2013.01); B29C 64/386 (2017.08); B29C 64/393 (2017.08); B33Y 10/00 (2014.12); B33Y 50/00 (2014.12); B33Y 50/02 (2014.12); G02B 3/14 (2013.01); G02B 26/00 (2013.01); G05B 13/0265 (2013.01); G05B 13/042 (2013.01); G05B 17/02 (2013.01); G05B 19/402 (2013.01); G05B 19/4099 (2013.01); G05D 1/0027 (2013.01); G05D 1/0297 (2013.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/06311 (2013.01); G06Q 10/0633 (2013.01); G06T 7/70 (2017.01); H04L 9/3239 (2013.01); H04L 9/50 (2022.05); H04L 63/1441 (2013.01); G05B 2219/32015 (2013.01); G05B 2219/40113 (2013.01); G05B 2219/49023 (2013.01); G06F 2113/10 (2020.01); G06Q 10/06 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/063114 (2013.01); G06Q 10/06313 (2013.01); G06Q 10/06316 (2013.01); G06Q 10/0831 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0201 (2013.01); G06Q 2220/00 (2013.01); G06T 2207/20081 (2013.01)] 36 Claims
OG exemplary drawing
 
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.