A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly

The objective of this research is to test the hypothesis that manufacturing complexity can reliably predict product quality in mixed-model automotive assembly. Originally, assembly lines were developed for cost efficient mass-production of standardized products. Today, in order to respond to diversified customer needs, companies have to allow for an individualization of their products, leading to the development of the Flexible Manufacturing Systems (FMS). Assembly line balancing problems (ALBP) consist of assigning the total workload for manufacturing a product to stations of an assembly line as typically applied in the automotive industry. Precedence relationships among tasks are required to conduct partly or fully automated Assembly Line Balancing. Efforts associated with manual precedence graph generation at a major automotive manufacturer have highlighted a potential relationship between manufacturing complexity (driven by product design, assembly process, and human factors) and product quality, a potential link that is usually ignored during Assembly Line Balancing and one that has received very little research focus so far. The methodology used in this research will potentially help develop a new set of constraints for an optimization model that can be used to minimize manufacturing complexity and maximize product quality, while satisfying the precedence constraints. This research aims to validate the hypothesis that the contribution of design variables, process variables, and human-factors can be represented by a complexity metric that can be used to predict their contribution on product quality. The research will also identify how classes of defect prevention methods can be incorporated in the predictive model to prevent defects in applications that exhibit high level of complexity. The manufacturing complexity model is applied to mechanical fastening processes which are accountable for the top 28% of defects found in automotive assembly, according to statistical analysis of historical data collected over the course of one year of vehicle production at a major automotive assembly plant. The predictive model is validated using mechanical fastening processes at an independent automotive assembly plant. This complexity-based predictive model will be the first of its kind that will take into account design, process, and human factors to define complexity and validate it using a real-world automotive manufacturing process. The model will have the potential to be utilized by design and process engineers to evaluate the effect of manufacturing complexity on product quality before implementing the process in a real-world assembly environment

Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors

Assembly for automotive production represents a significant proportion of total manufacturing cost, manufacturing time, and overall product cost. Humans remain a cost effective solution to adapt to the requirements of increasing product complexity and variety present in today\u27s flexible manufacturing systems. The human element present in the manufacturing system necessitates a better understanding of the human role in manufacturing complexity. Presented herein is a framework for enumerating assembly variables correlated with the potential for quality defect, presented in the design, process, and human factors domain. A case study is offered that illustrates on a manual assembly process the effect that complexity variables have on assembly quality

Statistical Modeling of Defect Propensity in Manual Assembly as Applied to Automotive Electrical Connectors

Assembly represents a significant fraction of overall manufacturing time and total manufacturing cost in the automotive industry. With increasing product complexity and variety, humans remain a cost effective solution to meet the needs of flexible manufacturing systems. This element necessitates a better understanding of the human role in manufacturing complexity. Presented herein is a framework for enumerating assembly variables correlated with the potential for quality defects, presented in the design, process, and human factors domain. A case study is offered that illustrates a method to identify variables and their effect on assembly quality for a manual assembly process

Manual Precedence Mapping and Application of a Novel Precedence Relationship Learning Technique to Real-World Automotive Assembly Line Balancing

An assembly line is a flow-oriented production system where the work pieces visit stations successively as they are moved along the line. An important decision problem, called Assembly Line Balancing Problem (ALBP), arises and has to be solved when (re-) configuring an assembly line. It consists of distributing the total workload for manufacturing any product to be assembled among the work stations along the line. The assignment of tasks to stations is constrained by task sequence restrictions which can be expressed in a precedence graph, which most manufacturers do not have. As a consequence, the elaborate solution procedures for ALBP developed by more than 50 years of research are not applicable in practice. Unfortunately, the known approaches for precedence graph generation are not suitable for the automotive industry. Therefore, we describe a detailed application of a new graph generation approach that is based on learning from past feasible sequences. Experiments indicate that this procedure is able to approximate the real precedence graph sufficiently well to detect nearly optimal solutions for a real-world automotive assembly line. Thus, the new approach seems to be a major step to close the gap between theoretical research and real-world line balancing