Design Evaluation of Multi-station Assembly Processes by Using State Space Approach

This paper considers the problem of evaluating and benchmarking process design configuration in a multi-station assembly process. We focus on the unique challenges brought by the multi-station system, namely, (1) a system level model to characterize the variation propagation in the entire process, and (2) the necessity to describe the system response to variation inputs at both global (system level) and local (station level and single fixture level) scales. State space representation is employed to recursively describe the propagation of variation in such a multi-station process, incorporating process design information such as fixture locating layout at individual stations and station-to-station locating layout change. Following the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given process design configuration. Implication of these indices with respect to variation control is discussed and a three-step procedure of applying the sensitivity indices for selecting a better design and prioritizing the critical station/fixture is presented. We illustrate the proposed method using the group of sensitivity indices in design evaluation of the assembly process of an SUV (Sport Utility Vehicle) side panel.

[1]  Darek Ceglarek,et al.  Dimensional variation reduction for automotive body assembly , 1995 .

[2]  Anna C. Thornton.  Quantitative selection of variation reduction plans , 2000 .

[3]  Darek Ceglarek,et al.  A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process , 1994 .

[4]  Kevin Otto,et al.  Variation Modeling for a Sheet Stretch Forming Manufacturing System , 1999 .

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

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

[7]  Kristin L. Wood,et al.  Computations with Imprecise Parameters in Engineering Design: Background and Theory , 1989 .

[8]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[9]  Alan R. Parkinson,et al.  Robust Mechanical Design Using Engineering Models , 1995 .

[10]  Kevin Otto,et al.  Process capability to guide tolerancing in manufacturing systems , 1999 .

[11]  Anna C. Thornton,et al.  A Mathematical Framework for the Key Characteristic Process , 1999 .

[12]  Erik K. Antonsson,et al.  Imprecision in Engineering Design , 1995 .

[13]  Daniel E. Whitney,et al.  Modeling and controlling variation propagation in mechanical assemblies using state transition models , 1999, IEEE Trans. Robotics Autom..

[14]  Farrokh Mistree,et al.  A procedure for robust design: Minimizing variations caused by noise factors and control factors , 1996 .

[15]  Daniel E. Whitney,et al.  Representation of geometric variations using matrix transforms for statistical tolerance analysis in assemblies , 1994 .

[16]  Christoph Roser,et al.  Evaluation of Product and Process Design Robustness , 1999 .

[17]  Anna C. Thornton.  Variation Risk Management Using Modeling and Simulation , 1999 .

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

[19]  Michael Yu Wang.  An Optimum Design Approach to Fixture Synthesis for 3D Workpieces , 1999 .

[20]  Carl D. Sorensen,et al.  A general approach for robust optimal design , 1993 .

[21]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[22]  Rikard Söderberg,et al.  Locating Scheme Analysis for Robust Assembly and Fixture Design , 1999, DAC 1999.

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

[24]  Darek Ceglarek,et al.  Design Evaluation of Sheet Metal Joints for Dimensional Integrity , 1998 .