Abstract
This chapter presents a modern Artificial Intelligence (AI)-based computer vision technique for automated quality inspection in complex manufacturing assembly lines. Using automotive manufacturing as an example, the study covers the historical approaches and their challenges in industry. It describes the modern, deep learning AI approach to visual inspection and illustrates the reasons why this approach is now possible and uses IBM Maximo Visual Inspection™ as a reference implementation. We share how the application of AI-based computer vision to manufacturing quality inspection processes yields improvements in efficiency and reduction in cost, contrast with traditional machine vision techniques, and conclude with special considerations of application and system architecture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rosenfeld, A.: Picture processing by computer. ACM Comput. Surv. 1(3), 147–176 (1969)
Deng, J.E.A.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context (2015)
He, K., et al.: Mask R-CNN (2018)
Trunzer, E.C.A.L.P., et al.: System architectures for Industrie 4.0 applications. Prod. Eng. Res. Dev. 13, 247–257 (2019)
Hollinger, M., Taafe-Hedglin, C.: Use Maximo Visual Inspection to detect and correct quality defects in manufacturing, 11 06 2021. [Online]. Available: https://www.ibm.com/cloud/architecture/architectures/use-maximo-visual-inspection-to-detect-mfg-defects/
Skhmot, N.: The Lean Way, 5 August 2017. [Online]. Available: https://theleanway.net/The-8-Wastes-of-Lean
Ohno, T.: Toyota Production System: Beyond Large-Scale Production. Productivity Press, Cambridge, MA (1988)
See, J.E.: Visual inspection reliability for precision manufactured parts. Hum. Factors. 57(8), 1427–1442 (2015)
Bozinovski, S., Fulgosi, A.: The influence of pattern similarity and transfer learning upon the training of a base perceptron B2 (originally in Croatian). In: Proceedings of Symposium Informatica, p. 3–121–5 (1976)
Red Hat, Inc.: Red Hat, 05 June 2021. [Online]. Available: https://www.redhat.com/en/solutions/hybrid-cloud-infrastructure
Red Hat, Inc.: OpenShift Container Plaform. [Online]. Available: https://www.openshift.com/ (2021)
Vaish, R., Agrawal, A., Kapoor, S., Parkin, R.: AI maturity framework for enterprise applications. [Online]. Available: https://www.ibm.com/watson/supply-chain/resources/ai-maturity/ (2021)
Raina, R., Madhavan, A., Ng, A.Y.: Large-scale deep unsupervised learning using graphics processors. In: Proceedings of the 26th Annual International Conference on Machine Learning (ICML ‘09), pp. 873–880. Association for Computing Machinery, New York, NY, USA (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Vaish, R., Hollinger, M.C. (2023). Case Study: IBM – Automating Visual Inspection. In: Nof, S.Y. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-96729-1_69
Download citation
DOI: https://doi.org/10.1007/978-3-030-96729-1_69
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96728-4
Online ISBN: 978-3-030-96729-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)