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

Jing-Hua Xu; Lin-Xuan Wang; Shu-You Zhang; Jian-Rong Tan

doi:10.1007/s40436-023-00446-0

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (3) : 407 -427. DOI: 10.1007/s40436-023-00446-0

Article

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

Jing-Hua Xu ¹^,²^,³^,⁴^,^a
, Lin-Xuan Wang ³
, Shu-You Zhang ¹^,²^,³
, Jian-Rong Tan ¹^,²^,³

Author information +

¹ State Key Lab of Fluid Power and Mechatronic Systems, Zhejiang University, 310058, Hangzhou, People’s Republic of China

² Zhejiang Key Lab of Advanced Manufacturing Technology, Zhejiang University, 310058, Hangzhou, People’s Republic of China

³ Engineering Research Centre for Design Engineering and Digital Twin of Zhejiang Province, 310058, Hangzhou, People’s Republic of China

⁴ Zhejiang-Singapore Innovation and AI Joint Research Lab, 310027, Hangzhou, People’s Republic of China

^a xujh@zju.edu.cn

Jing-Hua Xu born in 1979, is currently an associate professor at Zhejiang University, China. He received his Ph.D. from Zhejiang University, China, in 2009. His research interests include intelligent design.

[graphic not available: see fulltext]

Lin-Xuan Wang born in 1998, is currently a Ph.D. candidate in mechanical engineering college, Zhejiang University, China. His research interests include additive manufacturing.

[graphic not available: see fulltext]

Shu-You Zhang born in 1963, is currently a professor at Zhejiang University, China. He received his Ph.D. from Zhejiang University, China, in 1999. His research interests include CAD.

[graphic not available: see fulltext]

Jian-Rong Tan born in 1954, is currently an academician of Chinese Academy of Engineering (CAE) and a professor of mechanical engineering college, Zhejiang University, China. He received his Ph.D. from Zhejiang University, China, in 1992. His research interests include design methodology.

[graphic not available: see fulltext]

Show less

History +

PDF

Abstract

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 knowledge. The 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 technology. In 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 possibility. Driven 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 defects. The 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 profilometer. This 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 machine vision.

Keywords

Predictive defects detection / Additive manufacturing (AM) / Convex hull theory / Finite element method (FEM) / Multi-layer susceptibility discrimination (MSD)

Cite this article

Download citation ▾

Jing-Hua Xu ,Lin-Xuan Wang ,Shu-You Zhang ,Jian-Rong Tan . Predictive defect detection for prototype additive manufacturing based on multi-layer susceptibility discrimination. Advances in Manufacturing, 2023, 11(3): 407-427 DOI:10.1007/s40436-023-00446-0