Journal ArticleDOI
Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.
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TLDR
In this article, an in- situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning is described, where multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera.Abstract:
Process monitoring in additive manufacturing (AM) is a crucial component in the mission of broadening AM industrialization. However, conventional part evaluation and qualification techniques, such as computed tomography (CT), can only be utilized after the build is complete, and thus eliminate any potential to correct defects during the build process. In contrast to post-build CT, in situ defect detection based on in situ sensing, such as layerwise visual inspection, enables the potential for in-process re-melting and correction of detected defects and thus facilitates in-process part qualification. This paper describes the development and implementation of such an in situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning. During the build process, multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera. For each neighborhood in the resulting layerwise image stack, multi-dimensional visual features were extracted and evaluated using binary classification techniques, i.e. a linear support vector machine (SVM). Through binary classification, neighborhoods are then categorized as either a flaw, i.e. an undesirable interruption in the typical structure of the material, or a nominal build condition. Ground truth labels, i.e. the true location of flaws and nominal build areas, which are needed to train the binary classifiers, were obtained from post-build high-resolution 3D CT scan data. In CT scans, discontinuities, e.g. incomplete fusion, porosity, cracks, or inclusions, were identified using automated analysis tools or manual inspection. The xyz locations of the CT data were transferred into the layerwise image domain using an affine transformation, which was estimated using reference points embedded in the part. After the classifier had been properly trained, in situ defect detection accuracies greater than 80% were demonstrated during cross-validation experiments.read more
Citations
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Journal ArticleDOI
An overview of residual stresses in metal powder bed fusion
Jamison L. Bartlett,Xiaodong Li +1 more
TL;DR: In this paper, the authors survey and assemble the knowledge existing in the literature regarding residual stresses in powder bed fusion (PBF) processes, highlighting the anisotropic nature of the stress fields.
Journal ArticleDOI
Machine learning in additive manufacturing: State-of-the-art and perspectives
TL;DR: A comprehensive review on the state-of-the-art of ML applications in a variety of additive manufacturing domains can be found in this paper, where the authors provide a section summarizing the main findings from the literature and provide perspectives on some selected interesting applications.
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A review on machine learning in 3D printing: applications, potential, and challenges
TL;DR: In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML, and data sharing of AM would enable faster adoption of ML in AM.
Journal ArticleDOI
A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
Frederic E. Bock,Roland C. Aydin,Christian J. Cyron,Christian J. Cyron,Norbert Huber,Norbert Huber,Surya R. Kalidindi,Benjamin Klusemann,Benjamin Klusemann +8 more
TL;DR: It is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner.
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Metallurgy, mechanistic models and machine learning in metal printing
TL;DR: In this paper, the authors examined advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals.
References
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Journal ArticleDOI
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing
Sarah K. Everton,Sarah K. Everton,Matthias Hirsch,Petros Stravroulakis,Richard Leach,Adam T. Clare +5 more
TL;DR: In this paper, the state-of-the-art with respect to inspection methodologies compatible with additively manufactured (AM) processes is explored with the intention of identifying new avenues for research and proposing approaches to integration into future generations of AM systems.
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Influence of Defects on Mechanical Properties of Ti-6Al-4V Components Produced by Selective Laser Melting and Electron Beam Melting
TL;DR: In this article, the mechanical properties of Ti-6Al-4V samples produced by selective laser melting (SLM) and electron beam melting (EBM) were evaluated for hardness, tensile, and fatigue tests.
Journal ArticleDOI
A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing
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Journal ArticleDOI
Comparison of density measurement techniques for additive manufactured metallic parts
TL;DR: In this article, the accuracies of the three measurement principles: Archimedes method, microscopic analysis of cross sections and X-ray scanning were investigated for additive manufactured parts using selective laser melting (SLM) or electron beam melting (EBM).
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Fusion of Support Vector Machines for Classification of Multisensor Data
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