Transferability analysis of data-driven additive manufacturing knowledge: a case study between powder bed fusion and directed energy deposition

TL;DR

This paper introduces a framework for transferring data-driven AI knowledge between different additive manufacturing processes, demonstrated through a case study from powder bed fusion to directed energy deposition.

Contribution

It develops a three-step transferability analysis framework for AM knowledge, enabling effective transfer of AI solutions across different AM technologies.

Findings

Successful transfer of AI models between AM processes at multiple levels

Featurization of AM knowledge components facilitates transferability

Automated pipeline potential for cross-process knowledge exchange

Abstract

Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that have not been mined and formalized in an integrated way. Moreover, no tools or guidelines exist to support data-driven knowledge transfer from one context to another. As a result, data-driven solutions using specific AI techniques are being developed and validated only for specific AM process technologies. There is a potential to exploit the inherent similarities across various AM technologies and adapt the existing solutions from one process or problem to another using AI, such as Transfer Learning. We propose a three-step knowledge transferability analysis framework in AM to support data-driven AM knowledge transfer. As a prerequisite to transferability analysis, AM knowledge is featurized into identified knowledge components. The framework consists of pre-transfer, transfer, and post-transfer steps to accomplish knowledge transfer. A case study is conducted between flagship metal AM processes. Laser Powder Bed Fusion (LPBF) is the source of knowledge motivated by its relative matureness in applying AI over Directed Energy Deposition (DED), which drives the need for knowledge transfer as the less explored target process. We show successful transfer at different levels of the data-driven solution, including data representation, model architecture, and model parameters. The pipeline of AM knowledge transfer can be automated in the future to allow efficient cross-context or cross-process knowledge exchange.