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Recent years have seen considerable advances in the use of Finite Element (FE) modeling techniques, to the point where they can be used confidently to predict the output of the sheet metal forming system. The limiting factor in the use of FE analysis in the optimization process is now shifting from the accuracy of simulations, to the time required to optimize the system. This paper proposes a new approach aimed at reducing the time to optimize a draw die design, through a combination of Finite Element Modeling, semi-analytical models, and a knowledge based expert system. [DOI: 10.1115/1.1765138]

The pressures of modern manufacturing require that the quality–cost benefits are determined when evaluating new procedures or alternative operating policies. Traditionally, cost reports and other quality metrics have been used for this purpose. However, the interactions between the main quality cost drivers cannot be understood at the superficial level and the effect that a new process or an alternative operating policy may have on quality costs is difficult to determine. An alternative to the traditional costing methods is simulation. The current work uses simulation to evaluate quality costs in an automotive stamping plant where the quality control is determined by operator inspection of their own work. Self-inspection quality control provides instantaneous feedback of quality problems, allowing for quick rectification. However, the difficult nature of surface finish inspection of automotive panels can create inspection and control errors. A simulation model was developed to investigate the cost effects of inspection and control errors and it was found that inspection error had a significant effect in increasing total quality cost, with the magnitude of this increase dependent on the level of control. Further, the simulation found that the lowest cost quality control policy was that which allowed a number of defective panels to accumulate before resetting the press-line. [ABSTRACT FROM AUTHOR]