article · Journal of Materials Research and Technology
Laser powder bed fusion and machine learning are increasingly combined within advanced manufacturing, particularly for stainless steel components. The additive manufacturing of steel involves specific metallurgical difficulties, including chemical composition variations, anisotropic microstructures, and oxide film formation, which demand specialised operational considerations. In response, machine learning methods are deployed to improve the process through predictive modelling of operational parameters, real-time defect detection, and enhanced quality control. These data-driven techniques help accelerate both the fundamental understanding of manufacturing processes and the qualification of finished parts. Recent developments demonstrate that integrating machine learning with laser powder bed fusion addresses traditional manufacturing defects and optimises production workflows. Continued integration of computational models into steel additive manufacturing offers pathways to advance quality assurance and operational efficiency across the sector.
Metal 3D printing offers significant advantages for industrial manufacturing, but defects and complex material behaviours often hinder consistent results. By using machine learning to predict optimal settings and detect flaws in real time, manufacturers can reliably produce high-quality stainless steel components, reducing waste and accelerating the certification of critical parts.
This work outlines applications in real-time defect detection, process parameter optimisation, and part qualification for manufacturers using additive manufacturing. Potential users include industrial producers seeking reliable production of stainless steel components. As a review examining predictive algorithms and quality control frameworks, the technology represents early-stage to applied research that still requires implementation and validation within production environments before reaching commercial deployment.
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In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates the confluence of LPBF and ML, specifically within the specific domain of stainless steel. Firstly, it delves into LPBF principles, including an overview of critical process parameters and associated defects. Secondly, the paper meticulously addresses the distinct challenges posed by steel in additive manufacturing (AM), highlighting factors such as chemical composition, anisotropic microstructure, and oxide film formation, all of which require specialized considerations. Thirdly, the spotlight shifts to the pivotal role of ML, covering predictive modeling for process parameters, real-time defect detection, and quality control. This paper highlights recent advances, revealing how data-driven approaches can accelerate process understanding and part qualification. Eventually, this review offers insights into the future integration of ML in LPBF for steel, providing valuable perspectives on potential advancements in the field of AM.
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DOI: 10.1016/j.jmrt.2024.04.130
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