Statistical Estimation and Testing for Variation Root-Cause Identification of Multistage Manufacturing Processes

Abstract

Abstract-Root-cause identification for quality-related problems is a key issue in quality and productivity improvement for a manufacturing process. Unfortunately, root-cause identification is also a very challenging engineering problem, particularly for a multistage manufacturing process. In this paper, root-cause identification is formulated as a problem of estimation and hypothesis testing of a general linear mixed model. First, a linear mixed fault-quality model is built to describe the cause-effect relationship between the process faults and product quality. Then, the estimation algorithms developed for a general linear mixed model are adapted to estimate the process mean and variance. Finally, a hypothesis testing method is developed to determine if process faults exist in terms of statistical significance. A detailed experimental study illustrated the effectiveness of the proposed methodology.Note to Practitioners-Economic globalization brings intense competition among manufacturing enterprises. The key to succeed in this competitive climate is to rapidly respond to fast-changing market demands with high-quality and competitively priced products. To achieve this, we need to quickly identify root causes of quality-related problems in a complicated manufacturing system. However, the current widely adopted quality-control techniques focus more on monitoring than on root-cause identification. These techniques can efficiently detect the changes in the process but the root cause identification is often left to the plant engineers or operators. In this paper, a systematic estimation and testing method is proposed to identify the variational root causes in multistage manufacturing processes. First, a linear model is built based on the design information to describe the cause-effect relationship between the process faults and product quality. Then, an algorithm is developed to estimate the mean and variance of the process faults from the quality measurements of products. Finally, a statistical testing method is developed to determine if process faults (i.e. root causes) exist in terms of statistical significance. A detailed experimental study illustrates the effectiveness of this method. The method presented in this paper is a new quality-control technique and can be used for quality improvement of multistage manufacturing processes.

A Change Point Approach for Phase I Analysis in Multistage Processes

“…The typical statistical estimation algorithms are ML estimation, restricted ML estimation (REML), and minimum norm quadratic unbiased estimation (MINQUE) (see Rao and Kleffe 1988). Zhou et al (2004) suggested using MINQUE as an approximation of the ML estimate for multistage models because the computation load of MINQUE is much lower than that of ML estimation or REML. But we do not believe that MINQUE is suitable for phase I analysis, for two reasons.…”

“…Xiang and Tsung (2006) and Zantek, Gordon, and Robert (2006) considered online monitoring and diagnosis of out-of-control (OC) conditions in the multistage process. Zhou, Chen, and Shi (2004) estimated the process parameters by applying the typical estimation methods of general mixed linear models. Xiang and Tsung (2006) alternatively derived an EM procedure for maximum likelihood (ML) estimates of the parameters.…”

“…The estimation methods proposed by Zhou et al (2004) and Xiang and Tsung (2006) rely on the assumption that the reference sample is statistically stable. If the collected samples are indeed not in control (IC) (i.e., change points or outliers exist), then the estimates of the parameters will be adversely affected.…”

Manufacturing variation models in multi-station machining systems

“…In the literature, the SoV model has been applied for a large number of applications such as part quality estimation and process planning [9][10][11], manufacturing fault identification [12][13][14][15][16][17][18][19][20], dimensional quality control [21][22][23][24][25][26] and process-oriented tolerancing [27,28].…”

“…Besides analyzing the diagnosis capability of the MMP, other research works have also studied how to identify a specific root fault cause when it is diagnosable. For this purpose, pattern recognition techniques [16] and direct estimation methods [15] have been tested.…”