Higher-level innovization: A case study from Friction Stir Welding process optimization

The task of finding crucial design interdependencies in the form of mathematical relationships (empirical or otherwise) in an engineering design problem using the Pareto-optimal front is referred to as innovization. Past studies on the subject have limited themselves to a single front. In this paper we introduce the higher-level innovization task through an application of a manufacturing process simulation for the Friction Stir Welding (FSW) process where commonalities among two different Pareto-optimal fronts are analyzed. Multiple design rules are simultaneously deciphered from each front separately and compared. Important design aspects of the FSW problem are revealed in the process. The overall study aims at showing how some design principles can considerably ease the task of optimizing future enhancements to the design.

[1]  Jesper Henri Hattel,et al.  Thermal modelling of friction stir welding , 2008 .

[2]  Kalyanmoy Deb,et al.  Unveiling innovative design principles by means of multiple conflicting objectives , 2003 .

[3]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[4]  Kalyanmoy Deb.  MONOTONICITY ANALYSIS, EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION, AND DISCOVERY OF DESIGN PRINCIPLES , 2006 .

[5]  Kalyanmoy Deb,et al.  Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems , 2011, EMO.

[6]  John S. Gero,et al.  A LEARNING AND INFERENCE MECHANISM FOR DESIGN OPTIMIZATION PROBLEM (RE)- FORMULATION USING SINGULAR VALUE DECOMPOSITION , 2008 .

[7]  Amitabha Mukerjee,et al.  THE BIRTH OF SYMBOLS IN DESIGN , 2009 .

[8]  Kalyanmoy Deb,et al.  Hybrid Search for Faster Production and Safer Process Conditions in Friction Stir Welding , 2010, SEAL.

[9]  Akira Oyama,et al.  Data Mining of Pareto-Optimal Transonic Airfoil Shapes Using Proper Orthogonal Decomposition , 2009 .

[10]  Daisuke Sasaki,et al.  Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map , 2003, EMO.

[11]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[12]  Panos Y. Papalambros,et al.  An Automated Procedure for Local Monotonicity Analysis , 1984 .

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  Cem Celal Tutum,et al.  A multi-objective optimization application in Friction Stir Welding: Considering thermo-mechanical aspects , 2010, IEEE Congress on Evolutionary Computation.

[15]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[16]  Shigeru Obayashi,et al.  Multi-Objective Design Exploration for Aerodynamic Configurations , 2005 .

[17]  Kalyanmoy Deb,et al.  Automated discovery of vital knowledge from Pareto-optimal solutions: First results from engineering design , 2010, IEEE Congress on Evolutionary Computation.

[18]  Alice M. Agogino,et al.  Innovative design of mechanical structures from first principles , 1987, Artif. Intell. Eng. Des. Anal. Manuf..

[19]  Cem Celal Tutum,et al.  Optimisation of process parameters in friction stir welding based on residual stress analysis: A feasibility study , 2010 .

[20]  Kalyanmoy Deb,et al.  Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique , 2011 .

[21]  Shigeru Obayashi,et al.  Kriging-model-based multi-objective robust optimization and trade-off-rule mining using association rule with aspiration vector , 2009, 2009 IEEE Congress on Evolutionary Computation.

[22]  A. Agogino,et al.  MONOTONIC INFLUENCE DIAGRAMS: FOUNDATIONS AND APPLICATION TO OPTIMAL DESIGN , 1993 .

[23]  David W. Coit,et al.  Data mining techniques to facilitate the analysis of the pareto-optimal set for multiple objective problems , 2006 .