Journal ArticleDOI
Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.
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
Hybrid sparse convolutional neural networks for predicting manufacturability of visual defects of laser powder bed fusion processes
Ying Zhang,Yaoyao Fiona Zhao +1 more
TL;DR: A novel solution to predict the visual defects, one of the major criteria for analyzing manufacturability for the Laser-based Powder Bed Fusion (LPBF) process, and the computational costs decrease sharply compared to voxel-based CNN, which offers the advantage of performing with high resolutions.
Journal ArticleDOI
The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing
TL;DR: Manufacturing is undergoing a paradigmatic shift as it assimilates and is transformed by machine learning and other cognitive technologies.
Journal ArticleDOI
Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
TL;DR: In this article , the authors reviewed recent advances in Mechanistic-AI, defined as a methodology that combines the raw mathematical power of AI methods with mechanism-driven principles and engineering insights, with a focus on approaches that can improve data requirements, generalizability, explainability and capability to handle challenging and heterogeneous manufacturing data.
Journal ArticleDOI
Development of a defect-detection platform using photodiode signals collected from the melt pool of laser powder-bed fusion
TL;DR: In this paper, a defect-detection platform using in-situ monitoring of light intensity emitted from the melt pool of laser powder bed fusion (LPBF) to detect pores initiated from the lack of fusion phenomenon was developed.
Journal ArticleDOI
Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion
TL;DR: In this article, the effects of process parameters and energy density on the areal surface texture have been identified by applying machine learning methods to measured data to establish input and output relationships between process parameters with predictive capabilities.
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