Typically, monitoring quality characteristics of very personalized products is a difficult task due to the lack of experimental data. This is the typical case of processes where the production volume continues to shrink due to the growing complexity and customization of products, thus requiring low-volume productions. This paper presents a novel approach to statistically monitor Defects-Per-Unit (DPU) of assembled products based on the use of defect prediction models. The innovative aspect of such DPU-chart is that, unlike conventional SPC charts requiring preliminary experimental data to estimate the control limits (phase I), it is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is based on the structural complexity of the assembled product. By avoiding phase I, the novel approach may be of interest to researchers and practitioners to speed up the chart’s construction phase, especially in low-volume productions. The description of the method is supported by a real industrial case study in the electromechanical field.

Defects-per-unit control chart for assembled products based on defect prediction models / Verna, Elisa; Genta, Gianfranco; Galetto, Maurizio; Franceschini, Fiorenzo. - In: INTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY. - ISSN 0268-3768. - ELETTRONICO. - 119:5-6(2022), pp. 2835-2846. [10.1007/s00170-021-08157-1]

Defects-per-unit control chart for assembled products based on defect prediction models

Verna, Elisa;Genta, Gianfranco;Galetto, Maurizio;Franceschini, Fiorenzo
2022

Abstract

Typically, monitoring quality characteristics of very personalized products is a difficult task due to the lack of experimental data. This is the typical case of processes where the production volume continues to shrink due to the growing complexity and customization of products, thus requiring low-volume productions. This paper presents a novel approach to statistically monitor Defects-Per-Unit (DPU) of assembled products based on the use of defect prediction models. The innovative aspect of such DPU-chart is that, unlike conventional SPC charts requiring preliminary experimental data to estimate the control limits (phase I), it is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is based on the structural complexity of the assembled product. By avoiding phase I, the novel approach may be of interest to researchers and practitioners to speed up the chart’s construction phase, especially in low-volume productions. The description of the method is supported by a real industrial case study in the electromechanical field.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2935099