In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

Abstract

In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology.

Process Parameter Optimization Framework for the Selective Laser Melting of Hastelloy X Alloy Considering Defects and Solidification Crack Occurrence

“…Keyholing occurs under high laser power and low scan speed conditions. Balling is a phenomenon in which the melt pool solidifies into a spherical shape owing to the unstable flow of the melt pool [14,15], and a notch-like groove is formed at the outer edge of the melt pool. Balling occurs under high laser power and high scan speed conditions.…”

Data Treatment of In Situ Monitoring Systems in Selective Laser Melting Machines

“…In the literature, different means of measurement, such as infrared thermography, pyrometry, optical spectroscopy, ultrasonic sensors, and so on, are reported to monitor the L‐PBF process. [ 8,9 ] Considering the subject's vast scope, we focus mainly on reviewing the recent development in fault detection in the L‐PBF process using machine learning (ML) based on sensors’ responses.…”

“…ML techniques have recently gained much attention from many AM field researchers due to their easy applicability and versatile nature to solve the problems related to postprocessing of the in situ data in the AM process. The use of ML approaches in L‐PBF processes is summarized in the study by Yadav et al [ 8 ] Before proceeding further, it shall be essential to discuss the challenges associated with the ML approaches in the AM process, especially with L‐PBF, which are as follows: 1) The artificial neural networks (ANNs) predominately work on a large set of the labeled training dataset, which is a very challenging and laborious task in the L‐PBF process. It is very challenging to quantify and detect defects in the captured in situ data and often requires expensive and time‐consuming techniques such as CT for quantification of the defects.…”

Additive Manufacturing (AM) of Metallic Alloys

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The introduction of metal additive manufacturing (AM) processes in industrial sectors, such as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight and fewer potential failure points from joining features.This Special Issue on "Additive Manufacturing (AM) of Metallic Alloys" contains a mixture of review articles and original contributions on some problems that limit the wider uptake and exploitation of metal AM.The variation in the quality of the parts and to the repeatability of the laser powder bed fusion process (L-PBF) was reviewed by Yadav et al [1]. Their review focuses on the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection.One of the main causes that can lead to the poor quality of the components produced or the non-completion of the construction of the components is to be identified in the residual stresses.

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“…The variation in the quality of the parts and to the repeatability of the laser powder bed fusion process (L-PBF) was reviewed by Yadav et al [1]. Their review focuses on the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection.…”