Statistical monitoring of multi-stage processes based on engineering models

Most manufacturing processes consist of a large number of stages. The modeling of multi-stage processes by considering physical and mechanical laws in a linear state space form is extensively reported in the literature. This type of modeling describes the quality linkage among stages. However, recent research on statistical monitoring of multi-stage processes usually makes no use of this type of approach. A Statistical Process Control (SPC) method is proposed for multi-stage processes described by an engineering state space model. As a part of Phase I SPC analysis, a maximum likelihood estimation procedure based on an EM algorithm is developed. The complex multi-stage monitoring problem is converted to a simple multi-stream monitoring problem by applying group exponential weighted moving average charts to the one-step ahead forecast errors of the model. Reported run length results show the efficiency of the proposed charting method. The effectiveness of the proposed monitoring method is illustrated by its application to data from automobile hood manufacturing and workpiece assembly.

[1]  Jionghua Jin,et al.  State Space Modeling of Sheet Metal Assembly for Dimensional Control , 1999 .

[2]  Qiang Huang,et al.  State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors , 2003, IEEE Trans. Robotics Autom..

[3]  Yu Ding,et al.  Process-oriented tolerancing for multi-station assembly systems , 2005 .

[4]  J. A. Robinson,et al.  Analysis of Variation Transmission in Manufacturing Processes—Part I , 1999 .

[5]  York Marcel Dekker.  The State of Statistical Process Control as We Proceed into the 21st Century , 2000 .

[6]  James M. Lucas,et al.  Exponentially weighted moving average control schemes: Properties and enhancements , 1990 .

[7]  Melvin J. Hinich,et al.  Time Series Analysis by State Space Methods , 2001 .

[8]  Lloyd S. Nelson,et al.  Control Chart for Multiple Stream Processes , 1986 .

[9]  Yu Ding,et al.  Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach , 2002 .

[10]  Qiang Huang,et al.  Diagnosis of multi-operational machining processes through variation propagation analysis , 2002 .

[11]  Frederick W. Faltin,et al.  Statistical Control by Monitoring and Feedback Adjustment , 1999, Technometrics.

[12]  Daniel W. Apley,et al.  Autocorrelated process monitoring using triggered cuscore charts , 2002 .

[13]  Douglas C. Montgomery,et al.  A review of multivariate control charts , 1995 .

[14]  D. Hawkins.  Multivariate quality control based on regression-adjusted variables , 1991 .

[15]  D. Montgomery,et al.  Multivariate statistical process monitoring and diagnosis with grouped regression‐adjusted variables , 1999 .

[16]  Douglas M. Hawkins,et al.  Regression Adjustment for Variables in Multivariate Quality Control , 1993 .

[17]  Andrzej J. Strojwas,et al.  Monitoring multistage integrated circuit fabrication processes , 1996 .

[18]  Yong Chen,et al.  Statistical estimation and testing for variation root-cause identification of multistage manufacturing Processes , 2004, IEEE Transactions on Automation Science and Engineering.

[19]  Kailash C. Kapur,et al.  Design of Multiple Cause-Selecting Charts for Multistage Processes with Model Uncertainty , 2004 .

[20]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[21]  Douglas C. Montgomery,et al.  Controlling multiple stream processes with principal components , 1996 .

[22]  Dariusz Ceglarek,et al.  Diagnosability Analysis of Multi-Station Manufacturing Processes , 2002 .

[23]  Gongxu Zhang,et al.  A new diagnosis theory with two kinds of quality , 1990 .

[24]  Robert D. Plante,et al.  Process and Product Improvement in Manufacturing Systems with Correlated Stages , 2002, Manag. Sci..

[25]  William H. Woodall,et al.  A review and analysis of cause-selecting control charts , 1993 .

[26]  Lianjie Shu,et al.  ON MULTISTAGE STATISTICAL PROCESS CONTROL , 2003 .

[27]  Lloyd S. Nelson,et al.  Column: Technical Aids: Control Chart for Multiple Stream Process , 1986 .

[28]  Douglas C. Montgomery,et al.  Research Issues and Ideas in Statistical Process Control , 1999 .

[29]  Yong Chen,et al.  Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models , 2003, Technometrics.

[30]  Charles W. Champ.  Introduction to Statistical Quality Control, Fourth Edition , 2001 .