Abstract
Industrial manufacturing companies simultaneously work to improve the quality and reduce cost in their production processes. The level of quality of what is being produced is essential to succeed. Several types of techniques and applications are used to collect information from manufacturing processes and the quality of data is crucial. Today’s equipment has embedded systems, databases, sensors that gives both quantitative and qualitative information’s. For planning and control in a factory this is essential to give good visual control tools to the production processes.
However, the amount of structured data is growing fast, and goal-oriented performance indicators are key to measure success of produced smart products. With focus on planning and variant deviations of processes, performance indicators and enhancement of equipment combined with data analytics and AI techniques, we can outline a possibility of Autonomous Quality Control.
Autonomous quality control will therefore be a key to succeed in the digital era, implemented and used in the right way; it will give excellent quality control, planning and maintenance by use of Zero-Defect Manufacturing techniques.
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Acknowledgements
The work is supported by KPN CPS Plant, which is granted the Research Council of Norway (grant no. 267752).
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Eleftheriadis, R.J., Myklebust, O. (2020). The Importance of Key Performance Indicators that Can Contribute to Autonomous Quality Control. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation IX. IWAMA 2019. Lecture Notes in Electrical Engineering, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-15-2341-0_46
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DOI: https://doi.org/10.1007/978-981-15-2341-0_46
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