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predictive defect detection for prototype additive manufacturing based on multi-layer susceptibility discrimination

Advances in Manufacturing 2023年第3期11卷 407-427页

作者： Jing-Hua Xu Lin-Xuan Wang Shu-You Zhang Jian-Rong Tan State Key Lab of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310058People's Republic of China Zhejiang Key Lab of Advanced Manufacturing Technology Zhejiang UniversityHangzhou 310058People's Republic of China Engineering Research Centre for Design Engineering and Digital Twin of Zhejiang Province Hangzhou 310058People's Republic of China Zhejiang-Singapore Innovation and AI Joint Research Lab Hangzhou 310027People's Republic of China

This paper presents a predictive defect detection method for prototype additive manufacturing(AM)based on multilayer susceptibility discrimination(MSD).Most current methods are significantly limited by merely captured images,disregarding the differences between layer-by-layer manufacturing approaches,without combining transcendental *** visible parts,originating from the prototype of conceptual design,are determined based on spherical flipping and convex hull theory,on the basis of which theoretical template image(TTI)is rendered according to photorealistic *** addition,to jointly consider the differences in AM processes,the finite element method(FEM)of transient thermal-structure coupled analysis was conducted to probe susceptible regions where defects appeared with a higher *** by prior knowledge acquired from the FEM analysis,the MSD with an adaptive threshold,which discriminated the sensitivity and susceptibility of each layer,was implemented to determine *** anomalous regions were detected and refined by superimposing multiple-layer anomalous regions and comparing the structural features extracted using the Chan-Vese(CV)model.A physical experiment was performed via digital light processing(DLP)with photosensitive resin of a non-faceted scaled V-shaped engine block prototype with cylindrical holes using a non-contact *** MSD method is practical for detecting defects and is valuable for a deeper exploration of barely visible impact damage(BVID),thereby reducing the defect of prototypical mechanical parts in engineering machinery or process equipment via intellectualized machinevision.

关键词： predictive defects detection Additive manufacturing(AM) Convex hull theory Finite element method(FEM) Multi-layer susceptibility discrimination(MSD)

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