Abstract
The remanufacturing repair of damaged parts provides great potential for restoring them to a like-new condition. Existing methods cannot build an effective connection between 3D point cloud reconstruction and laser metal deposition (LMD) repair, which results in a low degree of integration and automation through the entire remanufacturing repair process. In this paper, we propose a repair volume extraction method that integrates surface reconstruction and repair volume extraction. For the surface reconstruction, a few reference points reconstructed by a close-range photogrammetry system (XJTUDP) were used to perform the initial registration and coordinate system transformation. Then, the internal and external parameters of two cameras embedded into a 3D optical scanning system (XJTUOM) were estimated using an eight-step calibration method. After completing the reconstruction of local point clouds through XJTUOM, we utilized another initial registration strategy depending on reference points and fine registration via the ICP algorithm to determine a refined complete point cloud for the damaged part. For the repair volume extraction, the refined complete point cloud was converted from the XJTUDP coordinate system to the LMD coordinate system, and two types of initial registration approaches and the ICP algorithm were used to achieve a best-fit position between the refined complete point cloud and the nominal point cloud. Finally, a point cloud representation of the repair volume was extracted by a distance-based filtering operation. The proposed method is validated by two experiments designed to extract the repair volume of a damaged gear with both planar and non-planar broken surfaces and is proven to be effective, robust, and highly automated.
Similar content being viewed by others
References
Matsumoto M, Yang S, Martinsen K, Kainuma Y (2016) Trends and research challenges in remanufacturing. Int J Precis Eng Manuf-Green Technol 3(1):129–142
Despeisse M, Baumers M, Brown P, Charnley F, Ford SJ, Garmulewicz A, Knowles S, Minshall TH, Mortara L, Reed-Tsochas FP, Rowley J (2017) Unlocking value for a circular economy through 3D printing: a research agenda. Technol Forecast Soc Chang 115:75–84
Petrat T, Graf B, Gumenyuk A, Rethmeier M (2016) Laser metal deposition as repair technology for a gas turbine burner made of Inconel 718. Phys Procedia 83:761–768
Wilson JM, Piya C, Shin YC, Zhao F, Ramani K (2014) Remanufacturing of turbine blades by laser direct deposition with its energy and environmental impact analysis. J Clean Prod 80:170–178
Chen L, He Y, Yang YX, Niu SW, Ren HT (2017) The research status and development trend of additive manufacturing technology. Int J Adv Manuf Technol 89(9–12):3651–3660
Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23(6):1917–1928
Han D, Chimienti A, Menga G (2013) Improving calibration accuracy of structured light systems using plane-based residual error compensation. Opt Eng 52(10):104106–104106
Le MT, Chen LC, Lin CJ (2017) Reconstruction of accurate 3-D surfaces with sharp edges using digital structured light projection and multi-dimensional image fusion. Opt Lasers Eng 96:17–34
Tang S, Zhang X, Song Z, Song L, Zeng H (2017) Robust pattern decoding in shape-coded structured light. Opt Lasers Eng 96:50–62
Mertens B, De Leener B, Debeir O, Beumier C, Lambert P, Delchambre A (2013) Robust structured light pattern for use with a spatial light modulator in 3-D endoscopy. Int J Optomechatronics 7(2):105–121
Vandenhouten R, Hermerschmidt A, Fiebelkorn R (2017) Design and quality metrics of point patterns for coded structured light illumination with diffractive optical elements in optical 3D sensors. In Digital Optical Technologies. International Society for Optics and Photonics 10335:1033518
Zhang Y, Yilmaz A (2016) Structured light based 3d scanning for specular surface by the combination of gray code and phase shifting. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 14:41
Kersten TP, Przybilla HJ, Lindstaedt M, Tschirschwitz F, Misgaiski-Hass M (2016) Comparative geometrical investigations of hand-held scanning systems. Int Soc Photogram Remote Sens XLIB5:507–514
Jin P, Liu JH, Liu SL, Wang X (2017) A new multi-vision-based reconstruction algorithm for tube inspection. Int J Adv Manuf Technol 93(5–8):2021–2035
Potiwiput S, Praphant T, Mai-orn A, Benyajati CN, Sukjamsri C (2016) Cementless prosthesis for reconstructing shoulder with glenoid retroversion. BMEiCON:1–4
Fu SC, Yang LJ, Zhang HZ, Wang Y, Chi GX (2017) Laser forming of the panel with crossed reinforcing bars. Int J Adv Manuf Technol 92(9–12):3673–3692
Shi BQ, Liang J (2016) Guide to quickly build high-quality three-dimensional models with a structured light range scanner. Appl Opt 55(36):10158–10169
Luo H, Li W, Li C, Wan M (2017) Investigation of creep-age forming of aluminum lithium alloy stiffened panel with complex structures and variable curvature. Int J Adv Manuf Technol 91(9–12):3265–3271
Wang X, Zhao ZL, Capps AG, Hamann B (2017) An iterative closest point approach for the registration of volumetric human retina image data obtained by optical coherence tomography. Multimed Tools Appl 76(5):6843–6857
Li F, Stoddart D, Hitchens C (2017) Method to automatically register scattered point clouds based on principal pose estimation. Opt Eng 56(4):044107
Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3D registration. In Robotics and Automation, ICRA:3212–3217
Li S, Wang J, Liang Z, Su L (2016) Tree point clouds registration using an improved ICP algorithm based on kd-tree. IGARSS 10:4545–4548
Marani R, Renò V, Nitti M, D'Orazio T, Stella E (2016) A modified iterative closest point algorithm for 3D point cloud registration. Comput-Aided Civ Inf 31(7):515–534
Bergström P, Edlund O (2017) Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach. Numer Algorithms 74(3):755–779
Li L, Li C, Tang Y, Du Y (2017) An integrated approach of reverse engineering aided remanufacturing process for worn components. Robot Comput-Integr Manuf 48:39–50
Paris H, Mandil G (2017) Extracting features for manufacture of parts from existing components based on combining additive and subtractive technologies. IJIDeM:1–12
Um J, Rauch M, Hascoët JY, Stroud I (2017) STEP-NC compliant process planning of additive manufacturing: remanufacturing. Int J Adv Manuf Technol 88(5–8):1215–1230
Zhang DH, Liang J, Guo C, Zhang XQ (2010) Digital photogrammetry applying to reverse engineering. SOPO:1–5
Hu H, Liang J, Tang ZZ, Shi BQ, Guo X (2012) Global calibration for multi-camera videogrammetric system with large-scale field-of-view. Opt Precis Eng 20(2):369–378
Funding
This work is supported by the National Natural Science Foundation of China under grant no. 51675404 and by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under grant no. 51421004. We would like to thank the State Key Laboratory for Manufacturing System Engineering at Xi’an Jiaotong University and the Xi’an Xintuo 3D Optical Measurement Technology Co., Ltd. (XJTOP).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Feng, C., Liang, J., Gong, C. et al. Repair volume extraction method for damaged parts in remanufacturing repair. Int J Adv Manuf Technol 98, 1523–1536 (2018). https://doi.org/10.1007/s00170-018-2300-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-018-2300-7