Defect detection and repair algorithm for structures generated by topology optimization based on 3D hierarchical fully convolutional network

Abstract

Customized 3D-printed structural parts are widely used in surgical robotics. To satisfy the mechanical properties and kinematic functions of these structural parts, a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and objectives. However, topology optimization currently faces some practical challenges that must be addressed, such as ensuring that structures do not have significant defects when converted to additive manufacturing models. To address this problem, we designed a 3D hierarchical fully convolutional network (FCN) to predict the precise position of the defective structures. Based on the prediction results, an effective repair strategy is adopted to repair the defective structure. A series of experiments is conducted to demonstrate the effectiveness of our approach. Compared to the 2D fully convolutional network and the rule-based detection method, our approach can accurately capture most defect structures and achieve 89.88% precision and 95.59% recall. Furthermore, we investigate the impact of different ways to increase the receptive field of our model, as well as the trade-off between different defect-repairing strategies. The results of the experiment demonstrate that the hierarchical structure, which increases the receptive field, can substantially improve the defect detection performance. To the best of our knowledge, this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms, providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

Keywords

Topology optimization / Additive manufacturing / Deep learning / 3D semantic segmentation / Defect detection

Declaration of competing interest

The aut=hors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61973293), the Central Guidance on Local Science and Technology Development Fund of Fujian Province, China (2021L3047 and 2020L3028), the Fujian Provincial Science and Technology Plan Project, China (2021Y0048 and 2021J01388), and the Open Project Program of Fujian Key Laboratory of Special Intelligent Equipment Measurement and Control, Fujian Special Equipment Inspection and Research Institute, China (FJIES2023KF02).

References

[1] Theodosia Lourdes Thomas, Venkatasubramanian Kalpathy Venkiteswaran, GK Ananthasuresh, Sarthak Misra, Surgical applications of compliant mechanisms: A review, J. Mech. Robot. 13 (2) (2021) 020801.

[2] Jenna Seetohul, Mahmood Shafiee, Snake robots for surgical applications: A review, Robotics 11 (3) (2022) 57.

[3] Yilun Sun, Yuqing Liu, Lingji Xu, Tim C. Lueth, Design of a disposable compliant medical forceps using topology optimization techniques, in: 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO, IEEE, 2019, pp. 924-929.

[4] Abdulsalam A Al-Tamimi, Paulo Rui Alves Fernandes, Chris Peach, Glen Cooper, Carl Diver, Paulo Jorge Bartolo, Metallic bone fixation implants: A novel design approach for reducing the stress shielding phenomenon, Virtual Phys. Prototyp. 12 (2) (2017) 141-151.

[5] Hongying Zhang, A. Senthil Kumar, Feifei Chen, Jerry Y.H. Fuh, Michael Yu Wang, Topology optimized multimaterial soft fingers for applica-tions on grippers, rehabilitation, and artificial hands, IEEE/ASME Trans. Mechatronics 24 (1) (2019) 120-131.

[6] M. Zhou, G.I.N. Rozvany,The COC algorithm, part II: Topological, geomet-rical and generalized shape optimization, Comput. Methods Appl. Mech. Engrg. 89 (1) (1991) 309-336,Methods Appl. Mech. Engrg. 89 (1) (1991) 309-336, Second World Congress on Computational Mechanics.

[7] Martin P. Bendsø e, Ole Sigmund, Material interpolation schemes in topology optimization, Arch. Appl. Mech. 69 (1999) 635-654.

[8] Grégoire Allaire, François Jouve, Anca-Maria Toader, A level-set method for shape optimization, C. R. Math. 334 (2002) 1125-1130.

[9] Katsuyuki Suzuki, Noboru Kikuchi, A homogenization method for shape and topology optimization, Comput. Methods Appl. Mech. Engrg. 93 (3)(1991) 291-318.

[10] M. Bhuvanesh Kumar, P. Sathiya, Methods and materials for additive man-ufacturing: A critical review on advancements and challenges, Thin-Walled Struct. 159 (2021) 107228.

[11] Ferry P.W. Melchels, Jan Feijen, Dirk W. Grijpma, A review on stereolithog-raphy and its applications in biomedical engineering, Biomaterials 31 (24)(2010) 6121-6130.

[12] Jean-Pierre Kruth, X. Wang, Tahar Laoui, Ludo Froyen, Lasers and materials in selective laser sintering, Assem. Autom. 23 (4) (2003) 357-371.

[13] Lawrence E Murr, Sara M Gaytan, Diana A Ramirez, Edwin Martinez, Jennifer Hernandez, Krista N Amato, Patrick W Shindo, Francisco R Medina, Ryan B Wicker, Metal fabrication by additive manufacturing using laser and electron beam melting technologies, J. Mater. Sci. Technol. 28 (1) (2012) 1-14.

[14] Sachini Wickramasinghe, Truong Do, Phuong Tran, FDM-based 3D printing of polymer and associated composite: A review on mechanical properties, defects and treatments, Polymers 12 (7) (2020) 1529.

[15] Abishek Kafle, Eric Luis, Raman Silwal, Houwen Matthew Pan,Pratisthit Lal Shrestha, Anil Kumar Bastola, 3D/4D printing of polymers: Fused deposi-tion modelling (FDM), selective laser sintering (SLS), and stereolithography (SLA), Polymers 13 (18) (2021) 3101.

[16] ZHU Jihong, ZHOU Han, WANG Chuang, ZHOU Lu, YUAN Shangqin, Wei-hong Zhang, A review of topology optimization for additive manufacturing: Status and challenges, Chin. J. Aeronaut. 34 (1) (2021) 91-110.

[17] Yilun Sun, Yuqing Liu, Nandi Zhou, Tim C. Lueth, A matlab-based frame-work for designing 3d topology optimized soft robotic grippers, in: 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, IEEE, 2021, pp. 1283-1289.

[18] Shutian Liu, Quhao Li, Junhuan Liu, Wenjiong Chen, Yongcun Zhang, A realization method for transforming a topology optimization design into additive manufacturing structures, Engineering 4 (2) (2018) 277-285.

[19] Gorkem Can Ates, Recep M. Gorguluarslan, Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization, Struct. Multidiscip. Optim. 63 (4) (2021) 1927-1950.

[20] Kai Liu, Andres Tovar, An efficient 3D topology optimization code written in matlab, Struct. Multidiscip. Optim. 50 (2014).

[21] William E. Lorensen, Harvey E. Cline,Marching cubes: A high resolution 3D surface construction algorithm, in: Seminal Graphics: Pioneering Efforts that Shaped the Field, 1998, pp. 347-353.

[22] Geoffrey E. Hinton, Ruslan R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313 (5786) (2006) 504-507.

[23] Daniel Svozil, Vladimir Kvasnicka, Jiri Pospichal, Introduction to multi-layer feed-forward neural networks, Intell. Lab. Syst. 39 (1) (1997) 43-62.

[24] Jonathan Long, Evan Shelhamer, Trevor Darrell,Fully convolutional net-works for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431-3440.

[25] Olaf Ronneberger, Philipp Fischer, Thomas Brox, U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Comput-ing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer, 2015, pp. 234-241.

[26] Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla, Segnet: A deep convo-lutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (12) (2017) 2481-2495.