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:Â
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
Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing
TL;DR: An opportunity to fully automate the approach to process optimisation by applying labels to the data is indicated, an approach that could also potentially be suited for on-the-fly process Optimisation.
Journal ArticleDOI
Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review
TL;DR: Among several machine learning techniques reviewed in this study, application of artificial neural networks (ANN) in process modelling and optimization has become quite noticeable because of its ability to predict the output quickly and accurately.
Journal ArticleDOI
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing
De Jun Huang,Hua Li +1 more
TL;DR: In this article, the quality repeatability of laser powder bed fusion (L-PBF) technology, in terms of static mechanical properties of printed parts, was quantified using relative standard deviation, and a machine learning approach for root cause analysis was demonstrated.
Journal ArticleDOI
Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects
TL;DR: In this paper, a combination of physics-informed machine learning, mechanistic modeling, and experimental data is used to reduce the occurrence of common defects in additive manufacturing, such as balling, cracking, lack of fusion, porosity, and surface roughness.
Journal ArticleDOI
Metal vaporization and its influence during laser powder bed fusion process
Jing Liu,Pengyu Wen +1 more
TL;DR: In this article , a comprehensive review of metal vaporization during laser powder bed fusion was conducted, in terms of its influence and underlying mechanism, as well as numerical simulations, and the vaporization loss of elements could be quantitatively adjusted to regulate the compositional distribution.
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