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
More filters
Journal ArticleDOI
Energy simulation of the fused deposition modeling process using machine learning approach
TL;DR: The application of random forest algorithm, which is a typical inductive machine learning approach, is introduced for predicting the energy consumption as well as the power curve imitation of a desktop FDM system.
Journal ArticleDOI
Camera signal dependencies within coaxial melt pool monitoring in laser powder bed fusion
Tobias Kolb,Reza Elahi,Jan Seeger,Mathews Soris,Christian Scheitler,Oliver Hentschel,Jan Tremel,Michael Schmidt +7 more
TL;DR: In this article, the authors analyzed the signal dependency of the camera-based coaxial monitoring system QMMeltpool 3D for laser powder bed fusion (LPBF) under the variation of process parameters, position, direction and layer thickness to determine the capability of the system.
Journal ArticleDOI
In-situ monitoring laser based directed energy deposition process with deep convolutional neural network
Mi Jiqian,Yikai Zhang,Hui Li,Shengnan Shen,Yongqiang Yang,Changhui Song,Xin Zhou,Yucong Duan,Junwen Lu,Haibo Mai +9 more
TL;DR: A new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time and achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process.
Journal ArticleDOI
Can Potential Defects in LPBF Be Healed from the Laser Exposure of Subsequent Layers? A Quantitative Study
Alexander Ulbricht,Gunther Mohr,Simon J. Altenburg,Simon Oster,Christiane Maierhofer,Giovanni Bruno +5 more
TL;DR: In this paper, the authors quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen, and show that small pores and surface roughness of solidified material below a thick layer of unmolten material serve as seeding points for larger voids.
Journal ArticleDOI
Gaussian-process based modeling and optimal control of melt-pool geometry in laser powder bed fusion
TL;DR: Simulation results demonstrate the effectiveness of the proposed GPR modeling and model-based optimal control in regulating the melt-pool size during the scanning of multi-tracks using L-PBF.
References
More filters
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Data clustering: 50 years beyond K-means
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
Journal ArticleDOI
Metal Additive Manufacturing: A Review
TL;DR: The state-of-the-art of additive manufacturing (AM) can be classified into three categories: direct digital manufacturing, free-form fabrication, or 3D printing as discussed by the authors.
Journal ArticleDOI
The status, challenges, and future of additive manufacturing in engineering
Wei Gao,Yunbo Zhang,Devarajan Ramanujan,Karthik Ramani,Yong Chen,Christopher B. Williams,Charlie C. L. Wang,Yung C. Shin,Song Zhang,Pablo D. Zavattieri +9 more
TL;DR: Future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration are pointed out.
Journal ArticleDOI
The metallurgy and processing science of metal additive manufacturing
TL;DR: In this article, a review of additive manufacturing (AM) techniques for producing metal parts are explored, with a focus on the science of metal AM: processing defects, heat transfer, solidification, solid-state precipitation, mechanical properties and post-processing metallurgy.
Related Papers (5)
Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm
Luke Scime,Jack Beuth +1 more