• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.7-15
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• 2008

The dimensional measurement equipment, such as Coordinate Measurement Machine (CMM), Optical Coordinate Measurement Machine (OCMM), and Checking Fixture (CF), take multiple dimensional measurements for each part in an automobile industry. Measurements are also recorded under different measurement systems to see if the responses differ significantly over these systems. Each measurement system (CMM, OCMM, and CF) will be considered as different treatments. This set-up provides massive amounts of process data which are multivariate in nature. Therefore, the multivariate statistical analysis is required to analyze data that are dependent on each other. This research provides step by step methodology for the evaluation procedure of the compatibility of measurement systems and clarify a systematic analyzation among the different measurement system's compatibility followed by number of case studies for each methodologies provided.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.125-130
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• 2008

Multivariate analysis is a rapidly expanding approach to data analysis. One specific technique in multivariate analysis is Principal Component Analysis (PCA). PCA is a statistical technique that linearly transform a given set of variables into a new set of composite variables. These new variables are orthogonal to each other and capture most of the information in the original variables. PCA is used to reduce the number of control points to be checked by measurement system. Therefore, the structure of the data set is simplified significantly It is also shown that eigenvectors obtained by conducting principal component analysis on the basis of the covariance matrix can be used to physically interpret the pattern of relative deformation for the points. This case study reveals the twisting deformation pattern of the underbody which is the largest mode of the total variation.