Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

Abstract

Direct metal laser sintering, an additive manufacturing technique, has a huge growing demand in industries like aerospace, biomedical, and tooling sector due to its capability to manufacture complex parts with ease. Despite many technological advancements, the reliability and repeatability of the process are still an issue. Therefore, there is a demand for inline automatic fault detection and postprocessing tools to analyze the acquired in situ monitoring data aiming to provide better-quality assurance to the user. Herein, the treatment of the data obtained using the EOSTATE optical tomography monitoring system is focused. A balanced dataset is obtained with the help of computer tomography of the certified part (Stainless Steel CX cylindrical samples), through which a feature matrix is prepared, and the layers of the part are classified either having "Drift" or "No-drift." The model is trained with the feature matrix and tested on benchmark parts (Maraging Steel) and on an industrial part (knuckle, automotive part) manufactured in AlSi10Mg. The proposed semisupervised approach shows promising results for presented case studies. Thus, the semisupervised machine learning approach, if adopted, could prove to be a cost effective and fast approach to postprocess the in situ monitoring data with much ease.

Performance optimization of shape memory epoxy polymers based on machine learning

“…Recently, machine learning (ML) has been widely leveraged in optimizing material properties 31–36 . Kokin et al efficiently predicted the physical properties of thermoset resins by ML method, 37 and the resin system components with the superior mechanical properties based on ML were acquired 38 .…”

Overheating Control in Additive Manufacturing Using a 3d Topology Optimization Method and Experimental Validation

“…It is a camera-based measurement technique for the observation of the thermal radiation over the process plane while the laser beam scans the powder bed surface [25]. Deviations in the measured signals can be derived from the OT images as implications of process anomalies where internal defects can be formed in the manufactured products [26,27]. The detection of hotspots is of particular interest in this work for the comparison of different TO strategies for avoiding overheating.…”

Development of a New Tooling Steel (L40) for Laser Powder Bed Fusion: Influence of Particle Size Distribution and Powder Atomization on Mechanical Performance

“…[ 10 ] One option to reduce the residual stresses is to heat the building plate above 200 °C [ 11 ] ; however, the heat treatment on the part will then not be homogenous throughout the part, which could lead to a reduction of the mechanical performance and higher porosity rates due to temperature variations. [ 12,13 ]…”

“…the building plate above 200 C [11] ; however, the heat treatment on the part will then not be homogenous throughout the part, which could lead to a reduction of the mechanical performance and higher porosity rates due to temperature variations. [12,13] In this study, the exposure parameters of a new tooling steel have been developed and optimized on an EOS M290 LPBF printer. The L40 steel is one of the first hard steel grades tailored for AM processes and tooling applications and has not yet been studied in the literature.…”