Abstract
The increasing competition forces manufacturing companies striving for Zero Defect Manufacturing to constantly improve their products and processes. This vision cannot be realized completely however, so cost-efficient inspection of quality is of high importance: While no defects should remain undetected, this always comes at the expense of pseudo defects. As this effect is common knowledge, the automatically generated inspection results have to be verified by human process experts. As this manual verification leads to tremendous inspection costs, reducing pseudo defects is a major business case nowadays. This paper presents an approach to reduce pseudo defects by applying Machine Learning (ML). A decision support system based on recorded inspection data and ML techniques has been developed to reduce manual verification efforts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gittler, T., Relea, E., Corti, D., Corani, G., Weiss, L., Cannizzaro, D., Wegener, K.: Towards predictive quality management in assembly systems with low quality low quantity data – a methodological approach. Procedia CIRP 79, 125–130 (2019)
Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. Int. J. Inf. Manag. 48, 63–71 (2019)
Park, J., Jeong, H., Park, J., Lee, B.C.: Relationships between cognitive workload and physiological response under reliable and unreliable automation. In: Nunes, I.L. (ed.) Advances in Human Factors and Systems Interaction, pp. 3–8. Springer, Cham (2019)
Denhof, D., Staar, B., Lütjen, M., Freitag, M.: Automatic optical surface inspection of wind turbine rotor blades using convolutional neural networks. Procedia CIRP 81, 1166–1170 (2019)
Cielo, P., Cole, K., Favis, B.D.: Optical inspection for industrial quality and process control. IFAC Proc. Volumes 20, 161–170 (1987)
Tabassian, M., Ghaderi, R., Ebrahimpour, R.: Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence. Expert Syst. Appl. 38, 5259–5267 (2011)
Yang, H.-Y., Yang, J., Pan, Y., Cao, K., Song, Q., Gao, F., Yin, Y.: Learn to be uncertain: leveraging uncertain labels in chest X-rays with Bayesian neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 5–8 (2019)
Wickstrøm, K., Kampffmeyer, M., Jenssen, R.: Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Med. Image Anal. 60, 101619 (2019)
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 335, 34–45 (2019)
Hagenah, J., Leymann, S., Ernst, F.: Integrating label uncertainty in ultrasound image classification using weighted support vector machines. Curr. Dir. Biomed. Eng. 5, 285–287 (2019)
Chen, Y., Zhou, X.S., Huang, T.S.: One-class SVM for learning in image retrieval. In: ICIP, vol. 1, pp. 34–37 (2001)
Michael, D.J., Koljonen, J., Nichani, S., Roberts, P.: Method and apparatus for in-line solder paste inspection. Google Patents (1999)
Duda, R.O., Hartand, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012)
Kulkarni, A.V., Kanal, L.N.: An optimization approach to hierarchical classifier design. In: Proceedings of the 3rd International Joint Conference on Pattern Recognition (1976)
Safavian, R.S., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21, 660–674 (1991)
Acknowledgments
Part of this work has received funding within the project QUALITY from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825030.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Schulte, L., Schmitt, J., Meierhofer, F., Deuse, J. (2020). Optimizing Inspection Process Severity by Machine Learning Under Label Uncertainty. In: Nunes, I. (eds) Advances in Human Factors and Systems Interaction. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1207. Springer, Cham. https://doi.org/10.1007/978-3-030-51369-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-51369-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51368-9
Online ISBN: 978-3-030-51369-6
eBook Packages: EngineeringEngineering (R0)