Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

By C. Gobert, Edward W. Reutzel, J. Petrich, A. Nassar, S. Phoha. 2018

417 citations recorded by the source corpus.

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In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review (2021, methodology): “implemented a supervised ML algorithm to detect defects by correlating in-situ camera monitoring with post-build computed tomography (CT) [152].”
Metallurgy, mechanistic models and machine learning in metal printing (2020, background): “…locations in feature space; the features of the data fully determine their locations and there is no stochastic element involved Defect detection 212 ; real-time composition monitoring 213 ; surface roughness 153 ; tensile strength prediction 214 ; construction of process maps 151…”
A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system (2020, methodology): “[66], multiple surface images were collected at each layer during laser AM using a high resolution digital single-lens reflex (DSLR) camera.”
A review on machine learning in 3D printing: applications, potential, and challenges (2020, methodology): “For instance, a hybrid ML algorithm was devised which uses hierarchical clustering to classify AM design features and support vector machine (SVM) to enhance the hierarchical clustering result in pursuit of finding the recommended AM design features (Yao et al. 2017).”