A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances) parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products.

[1]  Shuguang Gong,et al.  Sensitivity analysis and shape optimization based on FE–EFG coupled method , 2009 .

[2]  L. S. Li,et al.  A web services-based multidisciplinary design optimization framework for complex engineering systems with uncertainties , 2014, Comput. Ind..

[3]  LiuYusheng,et al.  Pattern-based integration of system optimization in mechatronic system design , 2016 .

[4]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[5]  Dae-Sung Bae,et al.  Sensitivity analysis of suspension characteristics for Korean high speed train , 2009 .

[6]  Silvana M. B. Afonso,et al.  Dynamic design optimization of an equivalent truncated mooring system , 2016 .

[7]  Jian Zhang,et al.  Identification of key design parameters of high-speed train for optimal design , 2014 .

[8]  Yalin Chen,et al.  A modified MOEA/D approach to the solution of multi-objective optimal power flow problem , 2016, Appl. Soft Comput..

[9]  Xiaoping Xie,et al.  Optimization of an implicit constrained multi-physics system for motor wheels of electric vehicle , 2016 .

[10]  X. X. Zhou,et al.  Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach , 2016 .

[11]  Weihua Zhang,et al.  Collaborative simulation method with spatiotemporal synchronization process control , 2016 .

[12]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[13]  Ninoslav Truhar,et al.  Damping optimization over the arbitrary time of the excited mechanical system , 2016, J. Comput. Appl. Math..

[14]  James E. Braun,et al.  A general multi-agent control approach for building energy system optimization , 2016 .

[15]  Ying Liao,et al.  Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization , 2016 .

[16]  Yuping He,et al.  Multidisciplinary Optimization of Multibody Systems with Application to the Design of Rail Vehicles , 2005 .

[17]  Carlos A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[18]  Yung-Chang Cheng,et al.  Integration of uniform design and Kriging interpolation to the optimization of suspension parameters of a high speed railway vehicle , 2010, Proceedings of the 2010 International Conference on Modelling, Identification and Control.

[19]  Yusheng Liu,et al.  Pattern-based integration of system optimization in mechatronic system design , 2016, Adv. Eng. Softw..

[20]  Yun Kyu Yi,et al.  Integrating neural network models with computational fluid dynamics (CFD) for site-specific wind condition , 2011 .

[21]  Peng Zhao,et al.  Optimization of high-pressure die-casting process parameters using artificial neural network , 2009 .

[22]  Li Liu,et al.  Metamodel-based global optimization using fuzzy clustering for design space reduction , 2013 .

[23]  Jeong-Oog Lee,et al.  Development of Web services-based Multidisciplinary Design Optimization framework , 2009, Adv. Eng. Softw..

[24]  Chengen Wang,et al.  Insights from developing a multidisciplinary design and analysis environment , 2014, Comput. Ind..

[25]  Azad M. Madni,et al.  Integrated Agent-based modeling and optimization in complex systems analysis , 2014, CSER.

[26]  Chan-Kyoung Park,et al.  Design Optimization for Suspension System of High Speed Train Using Neural Network , 2003 .

[27]  Amjad J. Aref,et al.  A genetic algorithm-based multi-objective optimization for hybrid fiber reinforced polymeric deck and cable system of cable-stayed bridges , 2015, Structural and Multidisciplinary Optimization.

[28]  Bin-bin Cheng,et al.  Multi-sources distinguishing of vector transducer via differential evolution , 2006 .

[29]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[30]  No-Cheol Park,et al.  Design and Optimization of Suspension with Optical Flying Head Using Integrated Optimization Frame , 2005 .

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

[32]  Weili Xu,et al.  Improving evolutionary algorithm performance for integer type multi-objective building system design optimization , 2016 .

[33]  Bernard Grossman,et al.  Polynomial Response Surface Approximations for the Multidisciplinary Design Optimization of a High Speed Civil Transport , 2001 .

[34]  Yang Zhang,et al.  Multi-parameter sensitivity analysis and application research in the robust optimization design for complex nonlinear system , 2015 .

[35]  W. Shyy,et al.  Surrogate-based modeling and dimension reduction techniques for multi-scale mechanics problems , 2011 .