Surrogate modeling of dimensional variation propagation in multistage assembly processes

In assembly process control and design optimization, it is critical to establish a mathematical model that describes the relationship between the dimensional quality of the final product and the various process parameters (e.g., the fixture layout and locator position deviation). This article presents a surrogate modeling methodology for multistage assembly processes to characterize the relationship between fixture layout and product dimensional quality. The mathematical structure of the model is derived from a physical analysis based on first principles and then the parameters of the model are identified using data from computer experiments. The resulting surrogate model can enable fixture layout optimization in process planning. A comprehensive case study of a multistage assembly process is also presented to demonstrate the effectiveness and high fidelity of the developed method.

[1]  Darek Ceglarek,et al.  Sensor Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems With Distributed Sensing , 2000 .

[2]  S. Jack Hu,et al.  A Variational Method of Robust Fixture Configuration Design for 3-D Workpieces , 1997 .

[3]  Richard P. Paul,et al.  Robot manipulators : mathematics, programming, and control : the computer control of robot manipulators , 1981 .

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

[5]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[6]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

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

[8]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[9]  Timothy M. Mauery,et al.  COMPARISON OF RESPONSE SURFACE AND KRIGING MODELS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 1998 .

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

[11]  Yu Ding,et al.  MODELING AND DIAGNOSIS OF MULTISTAGE MANUFACTURING PROCESSES: PART I - STATE SPACE MODEL , 2000 .

[12]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox , 2002 .

[13]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[14]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[15]  Yu Ding,et al.  Pattern matching for variation-source identification in manufacturing processes in the presence of unstructured noise , 2007 .

[16]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[17]  Yu Ding,et al.  Optimal sensor distribution for variation diagnosis in multistation assembly processes , 2003, IEEE Trans. Robotics Autom..

[18]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[19]  Darek Ceglarek,et al.  Fixture Failure Diagnosis for Sheet Metal Assembly with Consideration of Measurement Noise , 1999 .

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

[21]  D. Ceglarek,et al.  Time-Based Competition in Multistage Manufacturing: Stream-of-Variation Analysis (SOVA) Methodology—Review , 2004 .

[22]  Shiyu Zhou,et al.  Robust Method of Multiple Variation Sources Identification in Manufacturing Processes For Quality Improvement , 2006 .

[23]  T. Simpson,et al.  Comparative studies of metamodeling techniques under multiple modeling criteria , 2000 .

[24]  Jianjun Shi,et al.  Multi-stations sheet metal assembly modeling and diagnostics , 1996 .

[25]  Darek Ceglarek,et al.  Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition , 1996 .

[26]  Jianjun Shi,et al.  Stream of Variation Modeling and Diagnosis of Multi-Station Machining Processes , 2000, Manufacturing Engineering.

[27]  Shiyu Zhou,et al.  Kinematic Analysis of Dimensional Variation Propagation for Multistage Machining Processes With General Fixture Layouts , 2007, IEEE Transactions on Automation Science and Engineering.

[28]  Ying Huang,et al.  Modeling Variation Propagation in Machining Systems With Different Configurations , 2002 .

[29]  Yu Ding,et al.  Design Evaluation of Multi-station Assembly Processes by Using State Space Approach , 2002 .

[30]  Yu Ding,et al.  A comparison of process variation estimators for in-process dimensional measurements and control , 2005 .

[31]  Russell R. Barton,et al.  A review on design, modeling and applications of computer experiments , 2006 .