Abstract
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.
Highlights
The unmatched ability of metal additive manufacturing (AM) to produce customized parts with complex geometries has led to increased demand for these processes
The tools and dies industries have used the advantages of the laser powder bed fusion (L-powder bed fusion (PBF)) process to manufacture dies with conformal cooling channels, which significantly improves the performance of these components [8]
The following sections are dedicated to discussing the process parameters, the process signatures, and the most common defects resulting from the L-PBF process
Summary
The unmatched ability of metal additive manufacturing (AM) to produce customized parts with complex geometries has led to increased demand for these processes. Significant research effort has been directed towards experimentally optimizing the process parameters by understanding the process–structure–property relationships [12,13,14,15], while other research studies mainly focused on physics-based numerical models to predict the properties of the manufactured parts and attempt to prevent defects [16,17,18] Both experimental and numerical efforts have laid a good foundation for enhancing the understanding of the process. As various research is published on the application of ML in monitoring the L-PBF process, there was a need to summarize and compare the relationships of different sensors and the most common ML algorithms used in this area. This study gives an overview of the need for in situ monitoring of L-PBF and how different sensors can monitor different process signatures It highlights how ML algorithms can be used to assess the collected data and generate corrective signals to the process. The gaps in the literature and future research recommendations are discussed
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