Selection of optimal conditions in the surface grinding process using the quantum based optimisation method

A novel optimisation technique based on quantum computing principles, namely the quantum based optimisation method (QBOM), is proposed to solve the surface grinding process problem optimisation. In grinding process there is a trade-off between faster material removal rates, with a reduction in cutting time and its associated cost and shorter tool life or higher tool cost. The objective of the surface grinding optimisation problem is to determine the optimal machining conditions, which will minimize the unit production cost and unit production time with the finest possible surface finish but without violating any imposed constraints. The performance of QBOM is investigated against two test cases, one of a rough grinding process and the other of a finished grinding process and the computational results show that the proposed optimisation technique obtained better results than most of the methods presented in the literatures.

[1]  Xiankun Lin,et al.  Enhanced Pareto Particle Swarm Approach for Multi-Objective Optimization of Surface Grinding Process , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[2]  Kiran Solanki,et al.  Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach , 2012 .

[3]  nbspProf.Dhaval P Patel,et al.  Parametric Optimization of Face Milling Using Harmony Search Algorithm , 2014 .

[4]  Dilip Kumar Pratihar,et al.  Prediction of grinding power and surface finish - a GA-fuzzy approach , 2004, Integr. Comput. Aided Eng..

[5]  A. Gopala Krishna.  Selection of optimal conditions in the surface grinding process using a differential evolution approach , 2007 .

[6]  Jyh-Horng Chou,et al.  Improved differential evolution approach for optimization of surface grinding process , 2011, Expert Syst. Appl..

[7]  Alluru Gopala Krishna,et al.  Multi-objective optimisation of surface grinding operations using scatter search approach , 2006 .

[8]  Yusuf Kaynak,et al.  Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes , 2009 .

[9]  Miroslav Radovanović,et al.  USING GENETIC ALGORITHMS FOR OPTIMIZATION OF TURNING MACHINING PROCESS , 2016 .

[10]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[11]  R. Saravanan,et al.  A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations , 2002 .

[12]  Lov K. Grover.  A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[13]  Gexiang Zhang,et al.  Quantum-inspired evolutionary algorithms: a survey and empirical study , 2011, J. Heuristics.

[14]  Ali R. Yildiz,et al.  Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations , 2013, Appl. Soft Comput..

[15]  Ichiro Inasaki,et al.  Monitoring Systems for Grinding Processes , 2006 .

[16]  Ming Liang,et al.  Optimization of hole-making operations: a tabu-search approach , 2000 .

[17]  Ali Rıza Yıldız,et al.  A novel particle swarm optimization approach for product design and manufacturing , 2008 .

[18]  Ibrahim I. Esat,et al.  Real-Coded Quantum Inspired Evolution Algorithm Applied to Engineering Optimization Problems , 2006, Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (isola 2006).

[19]  Kalyanmoy Deb,et al.  Multi-Objective Optimization of Surface Grinding Process Using NSGA II , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[20]  R. Saravanan,et al.  Ants colony algorithm approach for multi-objective optimisation of surface grinding operations , 2004 .

[21]  Andrew Y. C. Nee,et al.  Micro-computer-based optimization of the surface grinding process , 1992 .

[22]  R. Venkata Rao,et al.  Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm , 2016, Journal of Intelligent Manufacturing.

[23]  P. J. Pawar,et al.  Grinding process parameter optimization using non-traditional optimization algorithms , 2010 .

[24]  Zhonghua Ni,et al.  Application of ant colony optimization algorithm in process planning optimization , 2013, J. Intell. Manuf..