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

doi:10.1007/s40436-023-00446-0

Abstract

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00446-0

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

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[J]. Advances in Manufacturing, 2023, 11(3): 407-427.

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References

1. Rane K, Strano M (2019) A comprehensive review of extrusionbased additive manufacturing processes for rapid production of metallic and ceramic parts. Adv Manuf 7:155-173
2. Everton SK, Hirsch M, Stravroulakis P et al (2016) Review of insitu process monitoring and in-situ metrology for metal additive manufacturing. Mater Des 95:431-445
3. Oztan C, Karkkainen R, Fittipaldi M et al (2019) Microstructure and mechanical properties of three dimensional-printed continuous fiber composites. J Compos Mater 53:271-280
4. Muller MS, De Jean PD (2015) 3D microscopy for microfabrication quality control. In: Proceedings of emerging digital micromirror device based systems and applications VII, vol 9376. San Francisco. https://doi.org/10.1117/12.2077698
5. Clijsters S, Craeghs T, Buls S et al (2014) In situ quality control of the selective laser melting process using a high-speed, realtime melt pool monitoring system. Int J Adv Manuf Technol 75:1089-1101
6. Kousiatza C, Karalekas D (2016) In-situ monitoring of strain and temperature distributions during fused deposition modeling process. Mater Des 97:400-406
7. Shevchik SA, Masinelli G, Kenel C et al (2019) Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Trans Industr Inform 15:5194-5203
8. Egan DS, Ryan CM, Parnell AC et al (2021) Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials. J Manuf Process 64:1248-1254
9. Nascimento R, Martins I, Dutra TA et al (2023) Computer vision based quality control for additive manufacturing parts. Int J Adv Manuf Technol 124:3241-3256
10. du Plessis A, le Roux SG, Booysen G et al (2016) Quality control of a laser additive manufactured medical implant by X-ray tomography. 3D Print Addit Manuf 3:175-182
11. du Plessis A, Sperling P, Beerlink A et al (2018) Standard method for microCT-based additive manufacturing quality control 1: porosity analysis. MethodsX 5:1102-1110
12. Lozanovski B, Downing D, Tino R et al (2021) Image-based geometrical characterization of nodes in additively manufactured lattice structures. 3D Print Addit Manuf 8:51-68
13. Thompson A, Maskery I, Leach RK (2016) X-ray computed tomography for additive manufacturing: a review. Meas Sci Technol 27:072001. https://doi.org/10.1088/0957-0233/27/7/072001
14. Seifi M, Salem A, Satko D et al (2017) Defect distribution and microstructure heterogeneity effects on fracture resistance and fatigue behavior of EBM Ti-6Al-4V. Int J Fatigue 94:263-287
15. Sanaei N, Fatemi A, Phan N (2019) Defect characteristics and analysis of their variability in metal L-PBF additive manufacturing. Mater Des 182:108091. https://doi.org/10.1016/j.matdes.2019.108091
16. Virgillito E, Aversa A, Calignano F et al (2021) Failure mode analysis on compression of lattice structures with internal cooling channels produced by laser powder bed fusion. Adv Manuf 9:403-413
17. Delli U, Chang S (2018) Automated process monitoring in 3D printing using supervised machine learning. Procedia Manuf 26:865-870
18. Amini M, Chang SI (2018) MLCPM: a process monitoring framework for 3D metal printing in industrial scale. Comput Ind Eng 124:322-330
19. Holzmond O, Li X (2017) In situ real time defect detection of 3D printed parts. Addit Manuf 17:135-142
20. Bisheh NM, Chang SI, Lei S (2021) A layer-by-layer quality monitoring framework for 3D printing. Comput Ind Eng 157:107314. https://doi.org/10.1016/j.cie.2021.107314
21. Vasileska E, Demir AG, Colosimo BM et al (2022) A novel paradigm for feedback control in LPBF: layer-wise correction for overhang structures. Adv Manuf 10:326-344
22. Kong L, Peng X, Chen Y et al (2020) Multi-sensor measurement and data fusion technology for manufacturing process monitoring: a literature review. Int J Extrem Manuf 2:022001. https://doi.org/10.1088/2631-7990/ab7ae6
23. AbouelNour Y, Gupta N (2022) In-situ monitoring of sub-surface and internal defects in additive manufacturing: a review. Mater Des 222:111063. https://doi.org/10.1016/j.matdes.2022.111063
24. Satterlee N, Torresani E, Olevsky E et al (2022) Comparison of machine learning methods for automatic classification of porosities in powder-based additive manufactured metal parts. Int J Adv Manuf Technol 120:6761-6776
25. Charalampous P, Kostavelis I, Kopsacheilis C et al (2021) Visionbased real-time monitoring of extrusion additive manufacturing processes for automatic manufacturing error detection. Int J Adv Manuf Technol 115:3859-3872
26. Goh GD, Hamzah NMB, Yeong WY (2022) Anomaly detection in fused filament fabrication using machine learning. 3D Print Addit Manuf. https://doi.org/10.1089/3dp.2021.0231
27. Caggiano A, Zhang J, Alfieri V et al (2019) Machine learningbased image processing for on-line defect recognition in additive manufacturing. CIRP Ann 68:451-454
28. Al-Maharma AY, Patil SP, Markert B (2020) Effects of porosity on the mechanical properties of additively manufactured components: a critical review. Mater Res Express 7:122001. https://doi.org/10.1088/2053-1591/abcc5d
29. Duarte VR, Rodrigues TA, Machado MA et al (2021) Benchmarking of nondestructive testing for additive manufacturing. 3D Print Addit Manuf 8:263-270
30. Tauber Z, Li ZN, Drew MS (2007) Review and preview: disocclusion by inpainting for image-based rendering. IEEE Trans Syst Man Cybern Part C (Appl Rev) 37(4):527-540
31. Aittala M (2010) Inverse lighting and photorealistic rendering for augmented reality. Vis Comput 26:669-678
32. Brennan MC, Keist JS, Palmer TA (2021) Defects in metal additive manufacturing processes. J Mater Eng Perform 30:4808-4818
33. Katz S, Tal A, Basri R (2007) Direct visibility of point sets. ACM Trans Graph 26(3):24. https://doi.org/10.1145/1276377.1276407
34. Xu J, Wang K, Sheng H et al (2020) Energy efficiency optimization for ecological 3D printing based on adaptive multi-layer customization. J Clean Prod 245:118826. https://doi.org/10.1016/j.jclepro.2019.118826
35. Xu J, Gao M, Feng X et al (2021) Support diminution design for layered manufacturing of manifold surface based on variable orientation tracking. 3D Print Addit Manuf 8:149-167
36. Zhang ZY (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the 7th IEEE international conference on computer vision, 20-27 September. Kerkyra, Greece. https://doi.org/10.1109/ICCV.1999.791289
37. Blinn JF (1977) Models of light reflection for computer synthesized pictures. In: Proceedings of the 4th annual conference on computer graphics and interactive techniques. New York, USA, pp 192-198. https://doi.org/10.1145/563858.563893
38. Montevecchi F, Venturini G, Scippa A et al (2016) Finite element modelling of wire-arc-additive-manufacturing process. Procedia CIRP 55:109-114
39. Promoppatum P, Taprachareon K, Chayasombat B et al (2022) Understanding size-dependent thermal, microstructural, mechanical behaviors of additively manufactured Ti-6Al-4V from experiments and thermo-metallurgical simulation. J Manuf Process 75:1162-1174
40. Keprate A, Ratnayake RMC, Sankararaman S (2017) Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline. Int J Pres Ves Pip 153:45-58
41. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266-277

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