A quantitative framework to support the decision between traditional, selective, and hybrid assembly

Abstract

High-technological products often consist of several components, each with varying feature values that are critical to the assembly’s quality. As traditional manufacturing involves random selection of components, strict tolerance ranges are typically set to limit their feature variability, increasing scrap rates and lowering material efficiency. By smartly combining components based on their measured features, selective and hybrid assembly allow for more relaxed tolerances and more efficient use of raw materials without affecting the final product’s quality. However, the increased logistical complexity involves additional operational costs, complicating the choice for the best strategy.This study presents a decision-making model for selecting from traditional, selective, and hybrid assembly. The model is formulated by first defining the cost functions for the corresponding expected assembly cost, and then analytically determining the optimal choice boundaries between the three methods based on the involved process parameters. Estimation methods for the instance-specific process parameters are then discussed before validating the model with a fully elaborated case study.Results confirm the need for a quantitative framework to support an informed choice as this strongly depends on the involved parameter values. This study aims to pave the way towards more sustainable manufacturing by supporting practitioners to identify the conditions whereby intelligent assembly methods, in addition to allowing for scrap reduction and increased material efficiency, also represent the most cost-effective option.