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

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.

A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method

“…In the scientific literature regarding product assembly complexity, a model addressing the assessment of complexity from an objective standpoint was firstly proposed by Sinha et al (2012), and then used in several studies (Alkan et al 2017;Alkan 2019;Verna et al 2021aVerna et al , b, 2022a. In such a model, the molecular orbital theory developed by Huckel (1932) is applied to the engineering domain to analyze the complexity of cyber-physical systems.…”

Empirical comparison of assembly complexity assessment approaches in manufacturing

“…Referring to Sinha and de Weck's model, Alkan [7] and Verna et al [17] measured complexity of components through the time necessary to handle single parts, complexity of interfaces through the time for joining them and topological complexity through the energy of the product's adjacency matrix. Other researchers have recently presented statistical models to forecast product defects using product complexity indicators [1,5]. Differently, Mathieson [18] used graph theory (i.e., size, path length and decomposition)…”

“…A product consisting of many parts, and therefore with several tasks to assemble it, increases the risk of operator errors and thus defects. Many studies have shown that assembly complexity has a direct effect on the occurrence of product defects and on quality costs [1][2][3][4][5][6]. The aim of this paper is to provide an overview of the main methods used to assess assembly complexity in manufacturing contexts.…”

Effects of product complexity on human learning in assembly and disassembly operations

“…The use of molecular structures to represent real cyber-physical products is a well-established approach in the scientific literature. By using atoms to represent product components and bonds to represent connections, molecular models can effectively emulate real assembly and disassembly processes (Alkan et al ., 2017a,b; Alkan and Harrison, 2019; Sinha, 2014; Verna et al. , 2022a,b).…”

“…By investigating the effects of product complexity on operator learning in assembly and disassembly processes, the study makes a significant contribution to the understanding of manufacturing technology and management, providing practical implications for businesses operating in a dynamic and complex manufacturing landscape. Unlike previous research that has primarily focussed on process performance metrics, such as time and defects (Alkan, 2019;Galetto et al, 2020a;Verna et al, 2022a), this study specifically investigates the implications of product complexity on operator learning. The extensive experimental campaign involving 84 operators and products with varying levels of complexity allows for a comprehensive analysis of these effects.…”