Complexity is the main challenge for present and future manufacturers. Assembly complexity heavily affects a product’s final quality in the fully automated assembly system. This paper aims to propose a new method to assess the complexity of modern automated assembly system at the assembly design stage with respect to the characteristics of both manufacturing system and each single component to be mounted. Aiming at validating the predictive model, a regression model is additionally presented to estimate the statistic relationship between the real assembly defect rate and predicted complexity of the fully automated assembly system.Design/methodology/approachThe research herein extends the S. N. Samy and H. A. ElMaraghy’s model and seeks to redefine the predictive model using fuzzy evaluation against a fully automated assembly process at the assembly design stages. As the evaluation based on the deterministic scale with accurate crisp number can hardly reflect the uncertainty of the judgement, fuzzy linguistic variables are used to measure the interaction among influence factors. A dependency matrix is proposed to estimate the assembly complexity with respect to the interactions between mechanic design, electric design and process factors and main functions of assembly system. Furthermore, a complexity attributes matrix of single part is presented, to map the relationship between all individual parts to be mounted and three major factors mentioned in the dependency matrix.FindingsThe new proposed model presents a formal quantification to predict assembly complexity. It clarifies that how the attributes of assembly system and product components complicate the assembly process and in turn influence the manufacturing performance. A center bolt valve in the camshaft of continue variable valve timing is used to demonstrate the application of the developed methodology in this study.Originality/valueThis paper presents a developed method, which can be used to improve the design solution of assembly concept and optimize the process flow with the least complexity.
Assembly Automation – Emerald Publishing
Published: Oct 18, 2019
Keywords: Regression analysis; Assembly defect; Automated assembly system; Complexity model; Fuzzy arithmetic; Quality