To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics. For optimization of costs and benefits, key characteristics can be defined by which the product quality can be checked with sufficient accuracy. The manual selection of key characteristics requires substantial planning effort and becomes uneconomic if many product variants prevail. This paper, therefore, aims to show a method for the efficient determination of key characteristics.
The authors present a novel Algorithm for the Selection of Key Characteristics (ASKC) based on an auto-encoder and a risk analysis. Given historical measurement data and tolerances, the algorithm clusters characteristics with redundant information and selects key characteristics based on a risk assessment. The authors compare ASKC with the algorithm Principal Feature Analysis (PFA) using artificial and historical measurement data.
The authors find that ASKC delivers superior results than PFA. Findings show that the algorithms enable the cost-efficient selection of key characteristics while maintaining the informative value of the inspection concerning the quality.
This paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.
Funding: The research that has led to the results presented in this paper has been conducted within the IGF project No. 20618 N/11 “App-basierte Aufwandsreduzierung bei der adaptiven Prüfung in der Produktion von Varianten” (App-based effort reduction in adaptive inspection planning in the production of variants, APProVe). The IGF project No. 20618 N/11 of the Forschungsgemeinschaft Qualität e.V. (FQS Research Association), August-Schanz-Straße 21A, 60433 Frankfurt am Main, Germany, was funded by the Federal Ministry for Economic Affairs and Climate Action via the AiF within the framework of the program for the promotion of the Industrial Collective Research (IGF) based on a resolution of the German Bundestag.
Greipel, J.S., Frank, R.M., Huber, M., Steland, A. and Schmitt, R.H. (2022), "Auto-encoder-based algorithm for the selection of key characteristics for products to reduce inspection efforts", International Journal of Quality & Reliability Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJQRM-11-2021-0408
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