N23B-T032 TITLE: Development of an Additive Manufacturing (AM) Candidate Assessment Tool
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Sustainment; Trusted AI and Autonomy
OBJECTIVE: Design and develop a data access tool that can determine if a part could be and should be produced via additive manufacturing (AM). These disciplines can include, but are not limited to the following: engineering design, manufacturability, producibility, testing, and machine learning to develop expert-guided algorithms to identify which readiness degraders, sustainment issues, and next generation components can be produced via AM.
DESCRIPTION: AM has the potential to increase readiness and improve maintenance and sustainment operations by reducing long lead times and eliminating obsolescence related issues. Furthermore, the technology enables improvements to current systems (e.g., light-weighting, part count reduction, increased system performance) through designs that are not possible by conventional manufacturing techniques. However, for the technology to continue to transition from indirect uses to efficiently producing qualified end use parts several technology barriers need to be overcome. One of the primary needs is the development and integration of data access tools with analytical capability to optimize the selection of viable families of AM candidate parts without requiring the burden of manual item-by-item review. The solution also should include analytical capabilities to effectively manage product technical and logistics information and provide users with substantive assessments on an itemís suitability to AM production.
Knowledge of computer aided design (CAD), technical data packages (TDPs), and product lifecycle management (PLM) tools is required, as well as the ability to quantify the limitations of existing AM systems and processes. Innovative design concepts are being sought for the development of an AM candidate assessment tool with the ability to:
(1) coarsely filter and screen for irrelevant parts,
(2) identify candidate parts using criteria such as material, performance requirements and parts family types,
(3) predict production estimates and delivery schedules by building/expanding upon a cost and time estimation tool, and
(4) automatically search Navy databases for parts most suitable for AM and subsequently validate them using a machine learning model or algorithm.
PHASE I: Develop, design, and demonstrate feasibility of a concept for an AM candidate assessment tool utilizing representative data. Develop a "coarse" filter or screening mechanism for candidate parts. The filter will use binary (yes/no) expert judgments, combined with active machine learning (ML) (e.g., adding expert judgements iteratively to understand the value of additional information), to filter parts unsuitable for AM. The tool will screen by critical dimensions (i.e., work envelope or bounding box) and known limitations of existing additive manufacturing systems of interest. Design should consider other criteria such as material, performance requirements, and parts family when determining the suitability of a part for AM. Refine existing cost and time estimation tools to predict production cost estimates and delivery schedules for representative AM part candidates. Production cost estimates should consider all post-processing operations (e.g., heat treatment, surface treatment, final machining, and inspection) required to meet the partís acceptance criteria. The Phase I effort will include prototype plans to be developed under Phase II.
PHASE II: Extend the decision model(s) developed under Phase I to address Navy part characteristics and mission priorities to develop a mutually agreed upon prioritization schema. Produce a ML algorithm, seeded with the aforementioned models, to integrate and search Navy databases for parts most suitable for AM, and the value of potentially (costly) additional information. Demonstrate and validate the prototype by utilizing actual Navy data.
PHASE III DUAL USE APPLICATIONS: Transition the tool under the guidance of PEO-CS Digital Thread team and/or NAWCAD LKEís Digital Enterprise Tools Branch. Commercialize the tool resulting from the Phase I/II R/R&D activities. This would likely involve further integration with existing, commercially-available CAD and PLM platforms.
Military and Commercial sectors that could benefit from this AM part identification tool include: aerospace, shipping, space, transportation, rail, automobile, and medical. Applications include almost all technology areas such as engine parts, structural parts, mechanical or electrical parts, medical prosthetics, and dental implants. Support the Navy/DoD to help transitioning the system to a DoD SYSCOM in support of various programs.
KEYWORDS: Additive Manufacturing; AM; Artificial Intelligence; AI; Machine Learning; ML; ; Neural Networks; Laser-Based Powder Bed Fusion; Candidate Identification; Decision Making
** TOPIC NOTICE **
The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoD 23.B STTR BAA. Please see the official DoD Topic website at www.defensesbirsttr.mil/SBIR-STTR/Opportunities/#announcements for any updates.
The DoD issued its Navy 23.B STTR Topics pre-release on April 19, 2023 which opens to receive proposals on May 17, 2023, and closes June 14, 2023 (12:00pm ET).
Direct Contact with Topic Authors: During the pre-release period (April 19, 2023 through May 16, 2023) proposing firms have an opportunity to directly contact the Technical Point of Contact (TPOC) to ask technical questions about the specific BAA topic. Once DoD begins accepting proposals on May 17, 2023 no further direct contact between proposers and topic authors is allowed unless the Topic Author is responding to a question submitted during the Pre-release period.
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|5/26/23||Q.||What details does the Navy database provide for parts that the algorithm will be able to use? CAD data? Technical drawings? Material/performance specifications?|
|A.||The tool should use data that can be derived from the Technical Data Package (TDP) and supply system(s). The TDP commonly provides: Part Number, Nomenclature, T/M/S, Material, Size, GD&T, Acceptance Criteria, Product Manufacturing Information (PMI), and Data Rights. The supply system commonly provides: NIIN, Part Number, Nomenclature, T/M/S, Criticality, Demand, OEM or Supplier, Federal Supply Class (FSC).|
|5/22/23||Q.||In the Phase I section, it mentions "existing AM systems of interest." The keywords mention Laser-Based Powder Bed Fusion. Are other metal AM processes of interest as well (e.g. binder jetting)? Or should the candidate assessment tool focus on laser baed PBF systems only?|
|A.||NAVAIR is primarily interested in Laser-based Powder Bed Fusion and Material Extrusion (Fused Deposition Modeling specifically) AM processes. The tool should consider the utilization of an extensible architecture to allow for inclusion of additional processes in the future.|