Multivariate Statistical Methods in Quality Management

5.5: Principal Component Analysis Case Studies

5.5 Principal Component Analysis Case Studies

5.5.1 Improving automotive dimensional quality by using principal component analysis

Dimensional quality is a measure of conformance of the actual geometry of manufactured products with their designed geometry. Many products are required to maintain dimensional consistency. In the real world, manufacturing and assembly processes are inherently imprecise, resulting in dimensional inconsistencies. It is very important to control the dimensional quality as it will affect both the function and appearance of the product. Some products, such as automotive body structures, have elaborate surface profiles and very complex manufacturing processes. Maintaining good dimensional quality is very difficult and critical to the success of the end product.

Dimensional variability must initially be accommodated for in design and then fully controlled in production. Dimensional variations at different measuring points are usually correlated with each other. The method presented in this case study is based on the use of principal component analysis (PCA) (Yang, 1996). PCA is able to extract major variation patterns in multivariate random variables. The pattern of variation may lead to clues for the causes of variation. automotive body-in-white (unpainted full autobody). Figure 5.13 shows a typical manufacturing process flow diagram for an automotive body assembly process.


Figure 5.13: Process flow diagram for automotive body assembly process.

Automotive body assembly process. An automotive body structure consists of many substructures such as the underbody, doors, and body-sides. Each substructure consists of numerous metal panels fabricated at stamping process. Figure 5.12 provides the substructure...

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