Polarimetric imaging for quality control in injection molding

P. Nagorny; T. Lacombe; T. Muller; H. Favreliere; E. Pairel; M. Pillet

doi:10.1117/12.2522062

16 July 2019 Polarimetric imaging for quality control in injection molding

P. Nagorny, T. Lacombe, T. Muller, H. Favreliere, E. Pairel, M. Pillet

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 1117217 (2019) https://doi.org/10.1117/12.2522062
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

Quality control is a key requirement of the Fourth Industrial Revolution, especially in manufacturing. For high value technical parts, geometrical and appearance quality need to be guaranteed. The quality control of the part, directly on the manufacturing line, can be achieved with non-contact sensors. With a robust quality sensor, the manufacturing process could be adjusted in real time to always achieve the optimal quality. For quality control, visible spectrum imaging is commonly used. However, more information could be obtained from broader spectrum imaging and also from the degree of linear polarization of the light from the part. We have developed a simple polarimetric imaging system to verify whether linear polarization is capable of amplifying geometric and appearance defects. We evaluate the use of this polarimetric imager given the industrial constraints of injection molding. We compare supervised classification performances on an injection molded parts dataset, using polarimetry and non-polarized images. We compare the performances of different machine learning pipelines with hand-crafted feature extraction and Deep Transfer Learning. With its industrial robustness, polarimetry could be a valuable addition to non-contact imagers for geometric and appearance quality control.

Citation Download Citation

P. Nagorny, T. Lacombe, T. Muller, H. Favreliere, E. Pairel, and M. Pillet "Polarimetric imaging for quality control in injection molding", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117217 (16 July 2019); https://doi.org/10.1117/12.2522062

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