A 3D Vision-Based Quality Inspection Study for Molded Part with Multiple Geometry Shapes

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3D vision based quality inspection has been widely applied in manufacturing industry. Product quality is retrieved from the point cloud obtained using 3D vision methods. Generally, three sorts of quality inspection methods can be selected according to the specific requirements. This paper studied a combining quality inspection method for the quality inspection of a plastic molded part with multiple geometry shapes. Only incomplete point cloud is available because of the characteristics of the part material. Shape fitting and template matching methods are applied for deformation detection with respect to different shapes. Experiment result shows the proposed method can accomplish the quality inspection task for the part with multiple geometry shapes.

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Periodical:

Edited by:

Kesheng Wang, Jan Ola Strandhagen and Dawei Tu

Pages:

529-537

DOI:

10.4028/www.scientific.net/AMR.1039.529

Citation:

Q. Yu and K. S. Wang, "A 3D Vision-Based Quality Inspection Study for Molded Part with Multiple Geometry Shapes", Advanced Materials Research, Vol. 1039, pp. 529-537, 2014

Online since:

October 2014

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$38.00

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[1] E.N. Malamas, E.G.M. Petrakis, M. Zervakis, L. Petit, J. -D. Legat, A survey on industrial vision systems, applications and tools, Image and Vision Computing, 21 (2003) 171-188.

DOI: 10.1016/s0262-8856(02)00152-x

[2] G. Sansoni, M. Trebeschi, F. Docchio, State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation, Sensors, 9 (2009) 568-601.

DOI: 10.3390/s90100568

[3] J. Pazmino, V. Carvelli, S. Lomov, B. Van Mieghem, P. Lava, 3D digital image correlation measurements during shaping of a non-crimp 3D orthogonal woven E-glass reinforcement, Int J Mater Form, (2013) 1-8.

DOI: 10.1007/s12289-013-1139-6

[4] J. Molleda, R. Usamentiaga, D.F. García, F.G. Bulnes, A. Espina, B. Dieye, L.N. Smith, An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry, Computers in Industry, 64 (2013) 1186-1200.

DOI: 10.1016/j.compind.2013.05.002

[5] A. Paoli, A.V. Razionale, Large yacht hull measurement by integrating optical scanning with mechanical tracking-based methodologies, Robotics and Computer-Integrated Manufacturing, 28 (2012) 592-601.

DOI: 10.1016/j.rcim.2012.02.010

[6] A. Kuş, Implementation of 3D optical scanning technology for automotive applications, Sensors, 9 (2009) 1967-(1979).

[7] C. Shan, Gesture Control for Consumer Electronics, in: L. Shao, C. Shan, J. Luo, M. Etoh (Eds. ) Multimedia Interaction and Intelligent User Interfaces, Springer London2010, pp.107-128.

DOI: 10.1007/978-1-84996-507-1_5

[8] R. Quevedo, J.M. Aguilera, Computer Vision and Stereoscopy for Estimating Firmness in the Salmon (Salmon salar) Fillets, Food Bioprocess Technol, 3 (2010) 561-567.

DOI: 10.1007/s11947-008-0097-3

[9] H.J. Pahk, W.J. Ahn, A New Method of Non-Contact Measurement for Microtopography in Semiconductor Wafer Implementing a New Optical Probe Based on the Precision Defocus Measurement, The International Journal of Advanced Manufacturing Technology, 17 (2001).

DOI: 10.1007/s001700170180

[10] P.J. Chiang, G.H. Ping, Stereo vision for the measurement of turning tool size, Hiroshima, 2013, pp.173-177.

[11] S. Malassiotis, M. Strintzis, Stereo vision system for precision dimensional inspection of 3D holes, Machine Vision and Applications, 15 (2003) 101-113.

DOI: 10.1007/s00138-003-0132-3

[12] W. Huang, R. Kovacevic, A laser-based vision system for weld quality inspection, Sensors, 11 (2011) 506-521.

DOI: 10.3390/s110100506

[13] D. Vučina, M. Ćurković, T. Novković, Classification of 3D shape deviation using feature recognition operating on parameterization control points, Computers in Industry, 65 (2014) 1018-1031.

DOI: 10.1016/j.compind.2014.04.001

[14] L. Iuliano, P. Minetola, Enhancing moulds manufacturing by means of reverse engineering, International Journal of Advanced Manufacturing Technology, 43 (2009) 551-562.

DOI: 10.1007/s00170-008-1739-3

[15] R. Schnabel, R. Wahl, R. Klein, Efficient RANSAC for Point-Cloud Shape Detection, Computer Graphics Forum, 26 (2007) 214-226.

DOI: 10.1111/j.1467-8659.2007.01016.x

[16] A. Tellaeche, B. Robles, 3D machine vision and artificial neural networks for quality inspection in mass production pieces, Emerging Technologies and Factory Automation (ETFA), 2010, pp.1-4.

DOI: 10.1109/etfa.2010.5641069

[17] K. Wang, Q. Yu, Product Quality Inspection Combining with Structure Light System, Data Mining and RFID Technology, in: G. Kovács, D. Kochan (Eds. ) Digital Product and Process Development Systems, Springer Berlin Heidelberg2013, pp.205-220.

DOI: 10.1007/978-3-642-41329-2_22

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