In order to improve engineering product designs and reduce the number of problems that occur during a new product launch, firms have focused on integrating downstream product development processes into the design phases. Unfortunately, this resource integration to solve problems has been less common in the back-end of product development, particularly for complex products involving many components such as an automotive body. Here, manufacturing firms use sequential validation procedures first to approve components, then subassemblies, and finally the end product. One trend has been to tighten component tolerances in efforts to avoid or minimize downstream assembly problems. These stricter component requirements, however, often result in timing delays and cost overruns due to unnecessary rework of components. For complex-assemblies, the use of "flexible criteria" and an approach called "functional build" can significantly reduce validation time and costs yet still meet end product quality objectives. This paper examines this functional build approach to manufacturing validation and demonstrates its effectiveness with an automotive case example.
Manufacturers using traditional process control charts to monitor their sheet metal stamping processes often encounter out-of-control signals indicating that the process mean has changed. Unfortunately, a sheet metal stamping process does not have the necessary adjustability in its process variable input settings to allow easily correcting the mean response in an out-of-control condition. Hence the signals often go ignored. Accordingly, manufacturers are unaware of how much these changes in the mean inflate the variance in the process output.We suggest using a designed experiment to quantify the variation in stamped panels attributable to changing means. Specifically, we suggest classifying stamping variation into three components: part-to-part, batch-to-batch, and within batch variation. The part-to-part variation represents the short run variability about a given stable or trending batch mean. The batch-to-batch variation represents the variability of the individual batch mean between die setups. The within batch variation represents any movement of the process mean during a given batch run. Using a two-factor nested analysis of variance model, a manufacturer may estimate the three components of variation. After partitioning the variation, the manufacturer may identify appropriate countermeasures in a variation reduction plan. In addition, identifying the part-to-part or short run variation allows the manufacturer to predict the potential process capability and the inherent variation of the process given a stable mean. We demonstrate the methodology using a case study of an automotive body side panel.
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