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.
Highlights
Nowadays, control charts have become an essential part of the quality control activities of most organizations to detect the presence of special causes of variation in a variety of manufacturing processes [1,2,3,4].Traditional control charts require a preliminary set of experimental data related to the process to be constructed [5]
The purpose of this paper is to propose a novel approach to statistically control the process of assembled products through the use of a control chart for nonconformities per unit (DPU-chart) based on defect prediction models
Considering the framework described in “Sect. 2,” the approach proposed in this study aims to statistically control the process by designing a control chart based on a defect prediction model
Summary
Control charts have become an essential part of the quality control activities of most organizations to detect the presence of special causes of variation in a variety of manufacturing processes [1,2,3,4]. Traditional control charts require a preliminary set of experimental data related to the process to be constructed (phase I) [5]. A defect prediction model developed in a recent study by the authors [9] is used to set up a control chart to monitor DPU detected in each workstation of a manufacturing process. This model relies on the relationship between DPU and product structural complexity. The DPU-chart, despite the conceptual similarity with traditional u-chart, totally differs in its design This control chart does not require a process analysis and a preliminary experimental data collection (phase I). “Sect. 6” summarizes the original contributions of this research, focusing on its implications, limitations, and possible future developments
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