Optimal design of fixture layout in a multi-station assembly using highly optimized tolerance inspired heuristic

Abstract The multi-station assembly (MSA) process requires auxiliary devices such as fixtures and clamps to accurately locate and firmly hold the workpiece in a desired position. Improper positioning of these fixtures and clamps affects the dimensional integrity of final product. This study determines the optimal design of fixture layout that minimizes the product dimensional variations caused by the manhandling and aging of auxiliaries. In order to model variation propagation from one assembly station to another in the MSA, a state space model is employed. Further, an E-optimality based sensitivity criterion is proposed to mathematically formulate and measure the quality of the fixture layout design. In order to solve the mathematical formulation, a highly optimized tolerance inspired heuristic is proposed. The proposed approach takes its governing traits from local incremental algorithm (LIA) which was initially exploited to maximize the design parameter (yield) in the percolation model. LIA analogous to the evolution by natural selection schema, assists in suitably exploring the search space of the underlying problem. The assembly of Sports Utility Vehicle side frame has been used to illustrate the concepts and test the performance the proposed solution methodology. Further, robustness of the proposed heuristic is demonstrated by comparing its results with that of obtained from Basic Exchange Algorithm used in the literature.

[1]  Heng Kuang,et al.  Fixture layout optimization in multi-station assembly processes using augmented ant colony algorithm , 2015 .

[2]  M. Y. Wang,et al.  An optimum design for 3-D fixture synthesis in a point set domain , 2000, IEEE Trans. Robotics Autom..

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

[4]  Warren R. DeVries,et al.  Optimization Methods Applied to Selecting Support Positions in Fixture Design , 1991 .

[5]  J. Doyle,et al.  Mutation, specialization, and hypersensitivity in highly optimized tolerance , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Kun Jiang,et al.  A multi-objective optimization and decision algorithm for locator layout continuous searching in checking fixture design , 2012, The International Journal of Advanced Manufacturing Technology.

[7]  H. Harry Asada,et al.  Kinematic analysis of workpart fixturing for flexible assembly with automatically reconfigurable fixtures , 1985, IEEE J. Robotics Autom..

[8]  Kai Yang,et al.  Non-discrete ant colony optimisation (NdACO) to optimise the development cycle time and cost in overlapped product development , 2013 .

[9]  Yiming Rong,et al.  Machining Accuracy Analysis for Computer-Aided Fixture Design , 1996 .

[10]  Marco E. Morais,et al.  Wildfires, complexity, and highly optimized tolerance. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[13]  Darek Ceglarek,et al.  Enhanced piecewise least squares approach for diagnosis of ill-conditioned multistation assembly with compliant parts , 2012 .

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

[15]  Yu Ding,et al.  Optimal design of fixture layout in multistation assembly processes , 2004, IEEE Trans Autom. Sci. Eng..

[16]  Manoj Kumar Tiwari,et al.  Optimal sensor distribution for multi-station assembly process using chaos-embedded fast-simulated annealing , 2009 .

[17]  J M Carlson,et al.  Highly optimized tolerance: a mechanism for power laws in designed systems. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[18]  Yu Ding,et al.  Optimal Engineering System Design Guided by Data-Mining Methods , 2005, Technometrics.

[19]  J. V. Abellán,et al.  Variation propagation modelling for multi-station machining processes with fixtures based on locating surfaces , 2013 .

[20]  Darek Ceglarek,et al.  Dimensional Fault Diagnosis for Compliant Beam Structure Assemblies , 1998, Manufacturing Science and Engineering.

[21]  J. S. Carlson,et al.  Quadratic Sensitivity Analysis of Fixtures and Locating Schemes for Rigid Parts , 2001 .

[22]  Manoj Kumar Tiwari,et al.  Key characteristics-based sensor distribution in multi-station assembly processes , 2015, J. Intell. Manuf..

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

[24]  Doyle,et al.  Highly optimized tolerance: robustness and design in complex systems , 2000, Physical review letters.

[25]  Daniel W. Apley,et al.  Diagnosis of Multiple Fixture Faults in Panel Assembly , 1996, Manufacturing Science and Engineering.

[26]  E. C. De Meter,et al.  The Application of Meta Functions to the Quasi-Static Analysis of Workpiece Displacement Within a Machining Fixture , 1996 .

[27]  C. R. Liu,et al.  AIFIX: AN EXPERT SYSTEM APPROACH TO FIXTURE DESIGN. , 1985 .

[28]  Yiming Rong,et al.  A unified point-by-point planning algorithm of machining fixture layout for complex workpiece , 2014 .

[29]  John Doyle,et al.  Complexity and robustness , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Daniel W. Apley,et al.  Singularity Issues in Fixture Fault Diagnosis for Multi-Station Assembly Processes , 2004 .

[31]  Kai Yang,et al.  A Fuzzy Goal Programming Approach for Optimal Product Family Design of Mobile Phones and Multiple-Platform Architecture , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[33]  Jian Liu,et al.  Quality prediction and compensation in multi-station machining processes using sensor-based fixtures , 2012 .