Geometrical Identification of Defect in Parts Using Imaging and Photogrammetry Using Intelligent Methods

Document Type : Research Article

Authors

Faculty of Material and Manufacturing Technologies, Malek Ashtar University of Technology

Abstract

This paper presents a damage identification method for components using intelligent techniques and tools. In this method, the damaged area on the point cloud of the affected component is first determined using photogrammetry, followed by clustering. The damage volume is calculated by fitting a plane to the damage boundary, enclosing the volume between the internal damaged surface and the fitted plane. The point cloud clustering is performed using the K-means method, while plane fitting and point cloud alignment are achieved using features from the segmented image of the component, including the damage center and the upper edge line of the model. Image segmentation is carried out using Mask R-CNN to isolate different objects. After completing the above steps, the toolpath for deposition is generated within the resulting volume and fed into a simulated robotic arm mechanism to guide the laser deposition system. This method is particularly useful for small-scale damage, especially in cases where an undamaged reference sample of the component is unavailable.

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References

[1] D.-G. Ahn, Directed energy deposition (DED) process: state of the art, International Journal of Precision Engineering and Manufacturing-Green Technology, 8(2) (2021) 703–742.
[2] G. Piscopo, L. Iuliano, Current research and industrial application of laser powder directed energy deposition, The International Journal of Advanced Manufacturing Technology, 119(11) (2022) 6893–6917.
[3] C. Silbernagel, A. Aremu, I. Ashcroft, Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing, Rapid Prototyping Journal, 26(4) (2020) 625–637.
[4] A. Rout, B. Deepak, B. Biswal, Advances in weld seam tracking techniques for robotic welding: A review, robotics and computer-integrated manufacturing, 56 (2019) 12–37.
[5] G. Tapia, A. Elwany, A review on process monitoring and control in metal-based additive manufacturing, Journal of Manufacturing Science and Engineering, 136(6) (2014) 060801.
[6] A. Emamian, M.H. Farshidianfar, A. Khajepour, Thermal monitoring of microstructure and carbide morphology in direct metal deposition of Fe-Ti-C metal matrix composites, Journal of Alloys and Compounds, 710 (2017) 20–28.
[7] P.M. Sammons, M.L. Gegel, D.A. Bristow, R.G. Landers, Repetitive process control of additive manufacturing with application to laser metal deposition, IEEE Transactions on Control Systems Technology, 27(2) (2018) 566–575.
[8] F. Wirth, S. Arpagaus, K. Wegener, Analysis of melt pool dynamics in laser cladding and direct metal deposition by automated high-speed camera image evaluation, Additive Manufacturing, 21 (2018) 369–382.
[9] T. Shi, B. Lu, T. Shen, R. Zhang, S. Shi, G. Fu, Closed-loop control of variable width deposition in laser metal deposition, The International Journal of Advanced Manufacturing Technology, 97(9) (2018) 4167–4178.
[10] W. He, W. Shi, J. Li, H. Xie, In-situ monitoring and deformation characterization by optical techniques; part I: Laser-aided direct metal deposition for additive manufacturing, Optics and Lasers in Engineering, 122 (2019) 74–88.
[11] D. Eisenbarth, P.M.B. Esteves, F. Wirth, K. Wegener, Spatial powder flow measurement and efficiency prediction for laser direct metal deposition, Surface and Coatings Technology, 362 (2019) 397–408.
[12] Z.-j. Tang, W.-w. Liu, Y.-w. Wang, K.M. Saleheen, Z.-c. Liu, S.-t. Peng, Z. Zhang, H.-c. Zhang, A review on in situ monitoring technology for directed energy deposition of metals, The International Journal of Advanced Manufacturing Technology, 108(11) (2020) 3437–3463.
[13] M. Jeddi, A.R. Khoogar, A New Visual Servoing Method for Grasping and Assembling Objects using Stereo Image Based Feedback, International Journal of Advanced Design & Manufacturing Technology, 16(1) (2023).
[14] J. Pan, L. Luo, T. Lu, E. Qian, C. Liu, Y. Guo, Q. Wu, In-Situ visual reconstruction of strut profiles in pulsed wire arc additive manufacturing of lattice structures, Virtual and Physical Prototyping, 19(1) (2024) e2425822.
[15] A.K. Mishra, S.Y. Goh, B. Ganapathysubramanian, A. Krishnamurthy, Real time 3D reconstruction for enhanced cybersecurity of additive manufacturing processes, Journal of Manufacturing Processes, 145 (2025) 274–285.
[16] B.M. Colosimo, F. Garghetti, M. Grasso, L. Pagani, On-line inspection of lattice structures and metamaterials via in-situ imaging in additive manufacturing, Additive Manufacturing, 95 (2024) 104538.
[17] S.J. Altenburg, A. Straße, A. Gumenyuk, C. Maierhofer, In-situ monitoring of a laser metal deposition (LMD) process: Comparison of MWIR, SWIR and high-speed NIR thermography, Quantitative InfraRed Thermography Journal, 19(2) (2022) 97–114.
[18] G.D. Goh, S.L. Sing, W.Y. Yeong, A review on machine learning in 3D printing: applications, potential, and challenges, Artificial Intelligence Review, 54(1) (2021) 63–94.
[19] M. Jeddi, A.R. Khoogar, A.M. Omrani, Reducing image size and noise removal in fast object detection using wavelet transform neural network, ADMT J, 13(2) (2020) 13–21.
[20] K. Zhu, J.Y.H. Fuh, X. Lin, Metal-based additive manufacturing condition monitoring: A review on machine learning based approaches, IEEE/ASME Transactions on Mechatronics, 27(5) (2021) 2495–2510.
[21] I. Jeon, P. Liu, H. Sohn, Real-time melt pool depth estimation and control during metal-directed energy deposition for porosity reduction, The International Journal of Advanced Manufacturing Technology,  (2023) 1–16.
[22] A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.-Y. Lo, Segment anything, in:  Proceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 4015–4026.
Amirkabir Journal of Mechanical Engineering
Volume 57, Issue 3
June 2025
Pages 361-378

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