Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling

Abstract This work presents the highly nonlinear relationships between processing conditions and the specific cutting energy, arithmetical mean roughness, and means roughness depth with the aid of the Kriging models in the dry milling of SKD61 material. Four processing conditions include the depth of cut, spindle speed, feed rate, and nose radius. The aim of this paper is to optimize machining factors for decreasing specific cutting energy and improving the material removal rate while the roughness properties are predefined as constraints. An evolutionary algorithm entitled archive-based micro-genetic algorithm (AMGA) is applied to generate the optimal inputs. The results show that a set of feasible optimal solutions can be determined to observe a low specific cutting energy coupled with a smooth surface and high material removal rate. Furthermore, the hybrid approach comprising the Kriging model and AMGA can be considered as an intelligent approach for optimization of the milling processes.

[1]  Byeongdo Kim,et al.  Comparison study on the accuracy of metamodeling technique for non-convex functions , 2009 .

[2]  Mats Ekevad,et al.  The effects of cutting parameters and tool geometry on cutting forces and tool wear in milling high-density fiberboard with ceramic cutting tools , 2017 .

[3]  Xu Han,et al.  An efficient multi-objective optimization approach based on the micro genetic algorithm and its application , 2011, International Journal of Mechanics and Materials in Design.

[4]  Ahmed A. D. Sarhan,et al.  Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling , 2014 .

[5]  Dinghua Zhang,et al.  Optimization of process parameters for surface roughness and microhardness in dry milling of magnesium alloy using Taguchi with grey relational analysis , 2015 .

[6]  Lin Li,et al.  Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality , 2013 .

[7]  A. Gopala Krishna,et al.  A hybrid approach to multi response optimization of micro milling process parameters using Taguchi method based graph theory and matrix approach (GTMA) and utility concept , 2018 .

[8]  A. Varun,et al.  Simultaneous optimization of WEDM responses using grey relational analysis coupled with genetic algorithm while machining EN 353 , 2015 .

[9]  Tarek Mabrouki,et al.  Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach , 2017 .

[10]  Girish Kant,et al.  Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining , 2014 .

[11]  İlhan Asiltürk,et al.  Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods , 2016 .

[12]  Mahmudur Rahman,et al.  Optimization of Surface Roughness in End Milling Using Potential Support Vector Machine , 2012, Arabian Journal for Science and Engineering.

[13]  Mozammel Mia,et al.  Multi-response optimization of end milling parameters under through-tool cryogenic cooling condition , 2017 .

[14]  M. Gupta,et al.  Sustainable machining of aerospace material – Ti (grade-2) alloy: Modeling and optimization , 2017 .

[15]  Mozammel Mia,et al.  Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method , 2017 .

[16]  K. Soorya Prakash,et al.  Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC , 2018 .

[17]  Abhijit Saha,et al.  Multi-objective optimization of manual metal arc welding process parameters for nano-structured hardfacing material using hybrid approach , 2017 .

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

[19]  K. S. Amirthagadeswaran,et al.  Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis , 2017 .

[20]  Kalyanmoy Deb,et al.  AMGA: an archive-based micro genetic algorithm for multi-objective optimization , 2008, GECCO '08.

[21]  Angelos P. Markopoulos,et al.  Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques , 2016 .

[22]  Tuncay Erzurumlu,et al.  A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces , 2006 .

[23]  S. Newman,et al.  Comparative investigation on using cryogenic machining in CNC milling of Ti-6Al-4V titanium alloy , 2016 .

[24]  Simon Barrans,et al.  Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations , 2017 .

[25]  E. A. Avellone,et al.  Marks' Standard Handbook for Mechanical Engineers , 1916 .

[26]  Anselmo Eduardo Diniz,et al.  Cutting conditions for finish turning process aiming: the use of dry cutting , 2002 .

[27]  Subhas Chandra Mondal,et al.  Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization , 2019 .

[28]  Rajesh Kumar,et al.  Effect of machining parameters on surface roughness in end milling of AISI 1019 steel , 2014 .

[29]  K. Venkata Rao.  Power consumption optimization strategy in micro ball-end milling of D2 steel via TLBO coupled with 3D FEM simulation , 2019 .

[30]  Ramezanali Mahdavinejad,et al.  Optimization of milling parameters using artificial neural network and artificial immune system , 2012 .

[31]  M. Elbah,et al.  Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology , 2014 .

[32]  Vishal S. Sharma,et al.  Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques , 2016 .

[33]  M. Sakthivel,et al.  Modeling and Analysis of Surface Roughness of AL7075-T6 in End Milling Process Using Response Surface Methodology , 2014 .

[34]  Mozammel Mia,et al.  Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method , 2018, Measurement.

[35]  J. A. Vilán,et al.  A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data , 2016 .

[36]  Jeehyun Jung,et al.  Modeling and parameter optimization for cutting energy reduction in MQL milling process , 2016 .

[37]  N. R. Dhar,et al.  Influence of single and dual cryogenic jets on machinability characteristics in turning of Ti-6Al-4V , 2019 .

[38]  S. P. L. Kumar.  Experimental investigations and empirical modeling for optimization of surface roughness and machining time parameters in micro end milling using Genetic Algorithm , 2018 .

[39]  Kishalay Mitra,et al.  Kriging Surrogate Based Multi-objective Optimization of Bulk Vinyl Acetate Polymerization with Branching , 2015 .

[40]  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.

[41]  M. Zako,et al.  Structural optimization using Kriging approximation , 2003 .

[42]  Ming-Chang Jeng,et al.  Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis , 2009 .

[43]  Mozammel Mia,et al.  Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques , 2018, The International Journal of Advanced Manufacturing Technology.

[44]  A. Çiçek,et al.  Optimization of process parameters for micro milling of Ti-6Al-4V alloy using Taguchi-based gray relational analysis , 2018, Measurement.

[45]  Mozammel Mia,et al.  Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network , 2016 .

[46]  M. Xiao,et al.  The effect of tool nose radius in ultrasonic vibration cutting of hard metal , 2003 .

[47]  Chee Peng Lim,et al.  Force and temperature modelling of bone milling using artificial neural networks , 2018 .

[48]  Junxue Ren,et al.  Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with RBF neural network and PSO algorithm , 2017 .

[49]  Grzegorz Krolczyk,et al.  Study on metrological relations between instant tool displacements and surface roughness during precise ball end milling , 2018, Measurement.

[50]  S. Mondal,et al.  Multi-objective optimization in WEDM process of nanostructured hardfacing materials through hybrid techniques , 2016 .

[51]  N. R. Dhar,et al.  Optimization of MQL flow rate for minimum cutting force and surface roughness in end milling of hardened steel (HRC 40) , 2017 .

[52]  N. Suresh Kumar Reddy,et al.  Performance Improvement of End Milling Using Graphite as a Solid Lubricant , 2005 .

[53]  Salim Belhadi,et al.  Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel , 2014 .

[54]  Abdulrahman Al-Ahmari,et al.  Multi-response optimization of machining parameters in micro milling of alumina ceramics using Nd:YAG laser , 2017 .

[55]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[56]  Guy Littlefair,et al.  Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach , 2018, Measurement.

[57]  Mozammel Mia,et al.  Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition , 2017, Neural Computing and Applications.

[58]  Ozgur Kisi,et al.  Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree , 2018 .

[59]  M. H. Abidi,et al.  Multi-objective optimization of micro-electrical discharge machining of nickel-titanium-based shape memory alloy using MOGA-II , 2018, Measurement.

[60]  N. R. Dhar,et al.  Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network , 2017 .