Artificial Intelligence/Machine Learning, ASA(ALT), Phase I

Artificial Intelligence (AI) for Additive Manufacturing (AM) Part Selection

Release Date: 01/12/2022
Solicitation: 22.4
Open Date: 01/27/2022
Topic Number: A224-001
Application Due Date: 03/01/2022
Duration: Up to 4 months
Close Date: 03/01/2022
Amount Up To: 250K

Objective

The objective of this Phase I topic is to develop Artificial Intelligence (AI) capabilities that analyzes technical data information and assesses the candidacy of a component for additive manufacturing, automate manual processes in order to reduce the time of engineering analysis by up to 80%, increase the pool of Additive Manufacturing (AM) candidates which leads to new opportunities and program creation, optimize the “Can Print / Should Print” analysis for higher yield of impactful AM candidates, and improve logistics trails and increase readiness through increased usage of additive manufacturing.

Description

The purpose of this Phase I topic is to develop an AI capability that greatly improves the method for identifying and analyzing AM candidate parts. Currently, there is a manual process in place performed by engineers who are AM Subject Matter Experts. AM SME engineers search through Army databases to pull technical and logistics data and analyze data to determine printability. The development of an AI system which can automate the technical data analysis process through critical factors will greatly benefit efforts. AM can be integrated in a multitude of DoD programs and supply chains will be greatly improved with the increase of AM candidate parts, saving time, money and resources.

Phase I

When completing the Phase I proposal, submission must demonstrate developed capability where technical data can be processed by an AI system to provide information and analysis on AM candidacy. Criteria may include the following: Material, Tolerance, Size, System, Supplier, and Item owner.

Phase II

When completing Phase II of this topic, submission must build upon and improve the AI system to increase efficiency and throughput and expand candidacy criteria. The effort should focus on the printability of the part and deviations against component requirements.

Phase III

Success In order to successfully complete Phase III, submission must show the performance of scaling and integration of the AI system with current Army Digital Management Systems.

For more information, and to submit your full proposal package, visit the DSIP Portal.

Microprocessor

References:

https://www.ieomsociety.org/singapore2021/papers/476.pdf

Objective

The objective of this Phase I topic is to develop Artificial Intelligence (AI) capabilities that analyzes technical data information and assesses the candidacy of a component for additive manufacturing, automate manual processes in order to reduce the time of engineering analysis by up to 80%, increase the pool of Additive Manufacturing (AM) candidates which leads to new opportunities and program creation, optimize the “Can Print / Should Print” analysis for higher yield of impactful AM candidates, and improve logistics trails and increase readiness through increased usage of additive manufacturing.

Description

The purpose of this Phase I topic is to develop an AI capability that greatly improves the method for identifying and analyzing AM candidate parts. Currently, there is a manual process in place performed by engineers who are AM Subject Matter Experts. AM SME engineers search through Army databases to pull technical and logistics data and analyze data to determine printability. The development of an AI system which can automate the technical data analysis process through critical factors will greatly benefit efforts. AM can be integrated in a multitude of DoD programs and supply chains will be greatly improved with the increase of AM candidate parts, saving time, money and resources.

Phase I

When completing the Phase I proposal, submission must demonstrate developed capability where technical data can be processed by an AI system to provide information and analysis on AM candidacy. Criteria may include the following: Material, Tolerance, Size, System, Supplier, and Item owner.

Phase II

When completing Phase II of this topic, submission must build upon and improve the AI system to increase efficiency and throughput and expand candidacy criteria. The effort should focus on the printability of the part and deviations against component requirements.

Phase III

Success In order to successfully complete Phase III, submission must show the performance of scaling and integration of the AI system with current Army Digital Management Systems.

For more information, and to submit your full proposal package, visit the DSIP Portal.

References:

https://www.ieomsociety.org/singapore2021/papers/476.pdf

Microprocessor

Artificial Intelligence (AI) for Additive Manufacturing (AM) Part Selection

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