Author:
Altinisik Armagan,Yildirim Utku
Abstract
Purpose
Electrical defects cover an important part of assembly defects and strongly affect the vehicle system performance. Almost 40% of assembly defects are classified as human errors and electrical connection failures represent a significant part of them. Humans still remain a cost-effective solution for the flexible manufacturing systems with increasing product complexity. So, understanding human behaviors is still a challenging task. The purpose of this study is to define, prioritize and validate the critical factors for the complexity of electrical connector plugin process.
Design/methodology/approach
The critical variables were defined by the expert team members. The required number of measurements and variables were revised resulting preliminary analysis of binary logistic regression. After the revision of measurement plan, the list of critical input variables and the mathematical model were defined. The model has been validated by the fitted values of the residuals (FITS analysis).
Findings
To the best of the authors’ knowledge, this is one of the limited studies, which defines the critical factors for electrical connection process complexity. Female connector harness length, connector width/height/length differences, operator sense of correct connector matching and ergonomy were defined as the factors with the highest impact on the failure occurrence. The obtained regression equation strongly correlates the failure probability.
Practical implications
The obtained mathematical model can be used in new model development processes both for the product and assembly process design (ergonomy, accessibility and lay-out).
Originality/value
The obtained risk factors demonstrated a strong correlation with assembly process complexity and failure rates. The output of this study would be used as an important guide for process (assembly line ergonomy, accessibility and lay-out) and product design in new model development and assembly ramp-up phases.
Subject
Industrial and Manufacturing Engineering,Control and Systems Engineering
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