Pareto optimization of multi-pass turning of grey cast iron with practical constraints using a deterministic approach

For a given workpiece material, in addition to the cutting tool material used and its geometry, a set of adopted cutting parameters significantly affect the actual turning process. With the ultimate aim of improving machining efficiency while ensuring the specified part quality and lowest possible energy consumption and costs, the optimization of the turning regimes is of great importance. Therefore, a multi-objective optimization model for the multi-pass turning of grey cast iron was developed in the present research. Part production costs and consumed energy were both considered as conflicting objective functions, while several practical constraints related to the part quality, machine tool limitations, and product specifications in finishing and roughing were also included in the optimization model. The formulation of the objective functions was based on the analytical model for the unit production time whose validation was performed using toolpath simulation in SinuTrain and FeatureCAM software. In order to obtain Pareto compromise solutions in a reasonable computational time as well as to avoid getting stuck in local optima in the optimization hyper-space, a deterministic approach based on the application of the brute force optimization algorithm is proposed to solve the developed multi-objective optimization problem with several constraints of the equality and inequality type. The obtained optimization results were validated experimentally and through a comparison with related analytical and empirical turning optimization studies.

[1]  Sunny Diyaley,et al.  OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS , 2019 .

[2]  Kusum Deep,et al.  Parameter optimization of multi-pass turning using chaotic PSO , 2015, Int. J. Mach. Learn. Cybern..

[3]  Hong-Seok Park,et al.  Multi-objective optimization of turning process of hardened material for energy efficiency , 2016, International Journal of Precision Engineering and Manufacturing.

[4]  Zhanqiang Liu,et al.  University of Birmingham Energy-based cost integrated modelling and sustainability assessment of Al-GnP hybrid nanofluid assisted turning of AISI52100 steel , 2020 .

[5]  Uwe Heisel,et al.  Modeling and optimization of turning duplex stainless steels , 2014 .

[6]  Yung C. Shin,et al.  Optimization of machining conditions with practical constraints , 1992 .

[7]  Singiresu S Rao,et al.  Determination of Optimum Machining Conditions—Deterministic and Probabilistic Approaches , 1976 .

[8]  Singaravel Balasubramaniyan,et al.  Application of integrated Taguchi and TOPSIS method for optimization of process parameters for dimensional accuracy in turning of EN25 steel , 2017 .

[9]  Tarek Mabrouki,et al.  Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function , 2017 .

[10]  Liang Gao,et al.  Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm , 2016 .

[11]  Peter Scallan.  Process Planning: The Design/Manufacture Interface , 2003 .

[12]  Congbo Li,et al.  Multi-objective parameter optimization of CNC machining for low carbon manufacturing , 2015 .

[13]  Raman Kumar,et al.  Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation , 2017 .

[14]  Joost Duflou,et al.  Optimization of energy consumption and surface quality in finish turning , 2012 .

[15]  F. Pušavec,et al.  Increasing machinability of grey cast iron using cubic boron nitride tools: Evaluation of wear mechanisms , 2016 .

[16]  Heinz Tschätsch.  Applied Machining Technology , 2009 .

[17]  H. Ghorbani,et al.  Multi-objective optimization of parameters in turning of N-155 iron-nickel-base superalloy using gray relational analysis , 2018 .

[18]  Tarek Mabrouki,et al.  Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method , 2017 .

[19]  Palanisamy Angappan,et al.  Taguchi-based grey relational analysis for modeling and optimizing machining parameters through dry turning of Incoloy 800H , 2017, Journal of Mechanical Science and Technology.

[20]  Balasubramaniyan Singaravel,et al.  Optimizacija parametara obrade u postupcima tokarenja primjenom kombinacije TOPSIS i AHP metode , 2015 .

[21]  Zhi-Jie Liu,et al.  Multi-objective optimization of the operating conditions in a cutting process based on low carbon emission costs , 2016 .

[22]  A. Khan,et al.  Sustainability-based performance evaluation of hybrid nanofluid assisted machining , 2020 .

[23]  Álisson Rocha Machado,et al.  A review on the machining of cast irons , 2018 .

[24]  Miroslav Radovanović,et al.  Multi-objective optimization of multi-pass turning AISI 1064 steel , 2018, The International Journal of Advanced Manufacturing Technology.

[25]  Indrajit Mukherjee,et al.  A review of optimization techniques in metal cutting processes , 2006, Comput. Ind. Eng..

[26]  Rolf Steinhilper,et al.  Resource Efficiency Optimization of Manufacturing Processes Using Evolutionary Computation: A Turning Case , 2015 .

[27]  Idir Belaidi,et al.  New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm , 2016 .

[28]  Idir Belaidi,et al.  Efficient genetic algorithm for multi-objective robust optimization of machining parameters with taking into account uncertainties , 2015 .

[29]  Edward J. Williams,et al.  Cuckoo optimization algorithm for unit production cost in multi-pass turning operations , 2015 .

[30]  Yong Wang,et al.  Optimization of multi-pass turning parameters through an improved flower pollination algorithm , 2017 .

[31]  Thanh-Phong Dao,et al.  An effective approach of adaptive neuro-fuzzy inference system-integrated teaching learning-based optimization for use in machining optimization of S45C CNC turning , 2018, Optimization and Engineering.

[33]  Fei Liu,et al.  Multi-objective optimization of machining parameters considering energy consumption , 2013, The International Journal of Advanced Manufacturing Technology.

[34]  Sunny Diyaley,et al.  Metaheuristics-based parametric optimization of multi-pass turning process: a comparative analysis , 2020, OPSEARCH.

[35]  Timothy G. Gutowski,et al.  An Environmental Analysis of Machining , 2004 .

[36]  Paul Mativenga,et al.  Sustainable machining: selection of optimum turning conditions based on minimum energy considerations , 2010 .

[37]  Yi-Chi Wang,et al.  Optimization of machining economics and energy consumption in face milling operations , 2018 .

[38]  F. Klocke.  Manufacturing Processes 1 , 2011 .

[39]  Peter Scallan.  2 – What is process planning? , 2003 .

[40]  Goran R. Miodragović,et al.  Optimization of multi-pass turning and multi-pass face milling using subpopulation firefly algorithm , 2019 .

[41]  Maher Barkallah,et al.  Multi Objective Optimization for Sustainable Manufacturing, Application in Turning , 2015 .

[42]  Carmita Camposeco-Negrete,et al.  Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum , 2015 .

[43]  Joze Balic,et al.  Particle swarm optimization approach for modelling a turning process , 2014 .

[44]  S. Bagaber,et al.  Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316 , 2017 .

[45]  Du-Ming Tsai,et al.  A simulated annealing approach for optimization of multi-pass turning operations , 1996 .

[46]  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.