Multi-Objective Optimization of Input Machining Parameters to Machined AISI D2 Tool Steel Material
DOI:
https://doi.org/10.24203/ijcit.v11i5.296Keywords:
Optimization; Rotary Ultrasonic Machining; Regression Analysis; Pareto-Front.Abstract
Poor surface finish on die and mould transfers the bad quality to processed parts. High surface roughness is an example of bad surface finish that is normally reduced by manual polishing after conventional milling machining process. Therefore, in order to avoid disadvantages by manual polishing and disadvantage by the machining, a sequence of two machining operations is proposed. The main operation is run by the machining and followed by Rotary Ultrasonic Machining Assisted Milling (RUMAM). However, this sequence operation requires optimum input parameters to generate the lowest surface roughness. Hence, this paper aims to optimize the input parameters for both machining operations by three soft-computing approaches – Genetic Algorithm, Tabu Search, and Particle Swarm Optimization. The method adopted in this paper begins with a fitness function development, optimization approach usage and ends up with result evaluation and validation. The soft-computing approaches result outperforms the experiment result in having minimum surface roughness. Based on the findings, the conclusion suggests that the lower surface roughness can be obtained by applying the input parameters at maximum for the cutting speed and vibration frequency, and at minimum for machining feed rate. This finding assists manufacturers to apply proper input values to obtain parts with minimum surface roughness.
References
G. Mennig, & K. Stoeckhert, (Eds.), Mold-making handbook. Carl Hanser Verlag GmbH Co KG, 2013.
R. Azlan, R. Izamshah, M. S. Kasim, M. Akmal, & M. A. H. M. Nawi, “Improvement of machining performance using hybrid rotary ultrasonic milling (HRUAM) for Hardened D2 tool steel materials,” Int. J. Appl. Eng. Res, 12(23), pp. 13506–13513, 2017.
S. Coromant, Application Guide: Die & Mould Making, Sandvik Coromant, 1999, pp. 41–42.
J. Kenda, F. Pusavec, G. Kermouche, & J. Kopac, “Surface integrity in abrasive flow machining of hardened tool steel AISI D2,” Procedia Engineering, 19, pp. 172−177, 2011.
R. Wdowik, J. Porzycki, & M. Magdziak, “Measurements of surface texture parameters after ultrasonic assisted and conventional grinding of ZrO2 based ceramic material characterized by different states of sintering,” Procedia CIRP, 62, pp. 293−298, 2017.
J. E. A. Qudeiri, A. Zaiout, A. H. I. Mourad, M. H. Abidi, & A. Elkaseer, “Principles and characteristics of different EDM processes in machining tool and die steels,” Applied Sciences, 10(6), p. 2082, 2020.
F. D. Ning, W. L. Cong, Z. J. Pei, & C. Treadwell, “Rotary ultrasonic machining of CFRP: a comparison with grinding,” Ultrasonics, 66, pp. 125−132, 2016.
M. Rafaqat, N. A. Mufti, N. Ahmed, A. M. Alahmari, & A. Hussain, “EDM of D2 Steel: Performance Comparison of EDM Die Sinking Electrode Designs,” Applied Sciences, 10(21), p. 7411, 2020.
D. Sudhakara, G. Prasanthi, & R. Sandeep, “Optimization of Parameters in Wire-EDM for Powder Metallurgical Cold Worked Tool Steel,” International Journal of Scientific Research in Science, vol. 4(1), pp. 2394−4099, 2018.
M. K. Pradhan, “Estimating the effect of process parameters on surface integrity of EDMed AISI D2 tool steel by response surface methodology coupled with grey relational analysis,” The International Journal of Advanced Manufacturing Technology, 67(9-12), pp. 2051−2062, 2013.
K. H. Park, Y. H. Hong, K. T. Kim, S. W. Lee, H. Z. Choi, & Y. J. Choi, “Understanding of ultrasonic assisted machining with diamond grinding tool,” Modern Mechanical Engineering, 4(1), pp. 1−7, 2014.
K. Ma, & G. Yang, “Kinematic design of a 3-DOF force-controlled end-effector module,” In 2016 IEEE 11th conference on industrial electronics and applications (ICIEA), pp. 1084−1089, June 2016.
S. Rebeggiani, & B. G. Rosén, “A step-by-step analysis of manual polishing sequences,” In Tool 2012-The 9th International Tooling Conference, 11-14 September, 2012, Leoben, Austria, pp. 317−324, 2012.
E. Kalt, R. Monfared, & M. Jackson, “Development of an intelligent automated polishing system,” 16th International Conference & Exhibition, May 2016.
R. E. Chinn, Ceramography: preparation and analysis of ceramic microstructures. ASM International, 2002.
P. Xu, C. F. Cheung, B. Li, L. T. Ho, & J. F. Zhang, “Kinematics analysis of a hybrid manipulator for computer controlled ultra-precision freeform polishing,” Robotics and Computer-Integrated Manufacturing, 44, pp. 44−56, 2017.
J. Li, Y. Guan, H. Chen, B. Wang, T. Zhang, X. Liu, ... & H. Zhang, “A high-bandwidth end-effector with active force control for robotic polishing,” IEEE Access, 8, pp. 169122−169135, 2020.
R. P. Singh, & S. Singhal, “An experimental study on rotary ultrasonic machining of macor ceramic,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(7), pp. 1221−1234, 2018.
R. Azlan, R. Izamshah, M. Hadzley, M. S. Kasim, M. Arfauz, & M. Akmal, “Improvement of cutting force using ultrasonic vibration for machining mold and die materials,” Proceeding Mech. Eng. Res. Day, 1, pp. 1−2, 2017.
W. L. Cong, Z. J. Pei, T. W. Deines, A. Srivastava, L. Riley, & C. Treadwell, “Rotary ultrasonic machining of CFRP composites: A study on power consumption,” Ultrasonics, 52(8), pp. 1030–1037, 2012.
G. Campatelli, A. Scippa, & L. Lorenzini, “Workpiece orientation and tooling selection to reduce the environmental impact of milling operations,” Procedia Cirp, 14, pp. 575−580, 2014.
P. H. Nguyen, T. L. Banh, K. A. Mashood, D. Q. Tran, T. Muthuramalingam, & D. T. Nguyen, “Application of TGRA-based Optimisation for machinability of high-Chromium tool steel in the EDM process,” Arabian Journal for Science and Engineering, 45(7), pp. 5555−5562, 2020.
M. Priyadarshini, C. K. Biswas, & A. Behera, “Machining of sub-cooled low carbon tool steel by wire-EDM,” Materials and Manufacturing Processes, 34(12), pp. 1316−1325, 2019.
M. Rehman, S. A. Khan, & R. Naveed, “Parametric optimization in wire electric discharge machining of DC53 steel using gamma phase coated wire,” Journal of Mechanical Science and Technology, 34(7), pp. 2767−2773, 2020.
G. Selvakumar, V. Balasubramanian, & N. Lenin, “Investigation on corner accuracy in wire cut EDM of AISI D3 tool steel,” International Journal of Rapid Manufacturing, 9(1), pp. 58−70, 2020.
Y. Nawaz, S. Maqsood, K. Naeem, R. Nawaz, M. Omair, & T. Habib, “Parametric optimization of material removal rate, surface roughness, and kerf width in high-speed wire electric discharge machining (HS-WEDM) of DC53 die steel,” The International Journal of Advanced Manufacturing Technology, 107(7), pp. 3231−3245, 2020.
S. K. Chaubey, S. Singh, & A. Singh, “Some investigations into machining of AISI D2 tool steel using wire electro discharge machining (WEDM) process”, Materials Today: Proceedings, 5(11), pp. 24347−24357, 2018.
M. Niamat, S. Sarfraz, W. Ahmad, E. Shehab, & K. Salonitis, “Parametric modelling and multi-objective optimization of electro discharge machining process parameters for sustainable production,” Energies, 13(1), p. 38, 2020.
D. Teixidor, I. Ferrer, J. Ciurana, & T. Özel, “Optimization of process parameters for pulsed laser milling of micro-channels on AISI H13 tool steel,” Robotics and Computer-Integrated Manufacturing, 29(1), pp. 209−218, 2013.
P. Kumar, S. R. Chauhan, C. I. Pruncu, M. K. Gupta, D. Y. Pimenov, M. Mia, & H. S. Gill, “Influence of different grades of CBN inserts on cutting force and surface roughness of AISI H13 die tool steel during hard turning operation,” Materials, 12(1), p. 177, 2019.
P. Sahoo, K. Patra, T. Szalay, & A. A. Dyakonov, “Determination of minimum uncut chip thickness and size effects in micro-milling of P-20 die steel using surface quality and process signal parameters,” The International Journal of Advanced Manufacturing Technology, 106(11), pp. 4675−4691, 2020.
C. Bin, G. Dingjie, Q. I. Jiyan, & J. Bingxue, “Sequence optimization of machining elements for process model based on the genetic algorithm of matrix constrained,” In Journal of Physics: Conference Series, Vol. 1678, No. 1, IOP Publishing, November 2020, p. 012072.
S. Kumar, P. Chandna, & G. Bhushan, “Prediction and optimization of work-piece temperature during 2.5-D milling of Inconel 625 using regression and Genetic Algorithm,” Cogent Engineering, 7(1), p. 1731199, 2020.
A. Wibowo, “Hybrid kernel principal component regression and penalty strategy of multiple adaptive genetic algorithms for estimating optimum parameters in abrasive waterjet machining,” Applied Soft Computing, 62, pp. 1102−1112, 2018.
P. K. Shrivastava, & A. K. Pandey, “Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm,” Infrared Physics & Technology, 91, pp. 220−232, 2018.
T. Ameur, “Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning,” Journal of King Saud University-Engineering Sciences, 33(4), pp. 259−265, 2020.
M. Azadi Moghaddam, & F. Kolahan, “Modeling and optimization of the electrical discharge machining process based on a combined artificial neural network and particle swarm optimization algorithm,” Scientia Iranica, 27(3), pp. 1206−1217, 2020.
P. K. Shrivastava, B. Singh, & Y. Shrivastava, “Prediction of optimal cut quality characteristic of Inconel 718 sheet by genetic algorithm and particle swarm optimization,” Journal of Laser Applications, 31(2), p. 022016, 2019.
S. Mohanty, A. Mishra, B. K. Nanda, & B. C. Routara, “Multi-objective parametric optimization of nano powder mixed electrical discharge machining of AlSiCp using response surface methodology and particle swarm optimization,” Alexandria Engineering Journal, 57(2), pp. 609−619, 2018.
H. Dai, W. Cheng, & P. Guo, “An Improved Tabu Search for Multi-skill Resource-Constrained Project Scheduling Problems Under Step-Deterioration,” Arabian Journal for Science and Engineering, 43(6), pp. 3279−3290, 2018.
Y. Lu, B. Cao, C. Rego, & F. Glover, “A Tabu Search based clustering algorithm and its parallel implementation on Spark,” Applied Soft Computing, 63, pp. 97−109, 2018.
V. S. Sai, K. G. Sundari, P. G. Rao, & B. Surekha, “Improvement of Machining Characteristics by EDM with Graphite Powder-Mixed Dielectric Medium,” In Advances in Manufacturing Technology, Springer, Singapore, 2019, pp. 41−48.
C. J. Lin, J. Y. Jhang, & K. Y. Young, “Parameter Selection and Optimization of an Intelligent Ultrasonic-Assisted Grinding System for SiC Ceramics,” IEEE Access, 8, pp. 195721-195732, 2020.
N. Srinivas, & K. Deb, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary computation 2(3), pp. 221−248, 1994.
A. M. Zain, H. Haron, & S. Sharif, “Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process,” Expert Systems with Applications, 37, pp. 4650–4659, 2010.
G. Ghosh, P. Mandal, & S. C. Mondal, “Modeling and Optimization of Surface Roughness in Keyway Milling using ANN, Genetic Algorithm, and Particle Swarm Optimization,” The International Journal of Advanced Manufacturing Technology, 100(5), pp. 1223−1242, 2019.
R. Kia, A. Baboli, N. Javadian, R. Tavakkoli-Moghaddam, M. Kazemi, & J. Khorrami, “Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing,” Computers & operations research, 39(11), pp. 2642−2658, 2012.
M. Sadeghi, H. Razavi, A. Esmaeilzadeh, & F. Kolahan, “Optimization of cutting conditions in WEDM process using regression modelling and Tabu-search algorithm,” Proceeding of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(10), pp. 1825−1834, 2011.
N. Ghosh, P. K. Pal, & G. Nandi, “Parametric Optimization of MIG Welding on 316L Austenitic Stainless Steel by Grey-Based Taguchi Method,” Procedia Technology, 25, pp. 1038−1048, 2016.
A. M. Zain, H. Haron, & S. Sharif, “Simulated annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling Ti-6Al-4V,” Machining Science and Technology, 14(1), pp. 43-62, 2010.
T. Sathish, “BCCS Approach for the Parametric Optimization in Machining of Nimonic-263 alloy using RSM,” Materials Today: Proceedings, 5(6), pp. 14416−14422, 2018.
J. Mirapeix, P. B. Garcia-Allende, O. M. Conde, J. M. Lopez-Higuera, & A. Cobo, “Welding diagnostics by means of particle swarm optimization and feature selection,” Journal of Sensors 2012, 2012.
R. V. Rao, & V. Patel, “Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm” Applied Mathematical Modelling, 37(3), pp. 1147−1162, 2013.
W. C. Chen, M. H. Nguyen, W. H. Chiu, T. N. Chen, & P. H. Tai, “Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO,” The International Journal of Advanced Manufacturing Technology, 83(9-12), pp. 1873−1886, 2016.
N. Zainal, A. M. Zain, N. H. M. Radzi, & M. R. Othman, “Glowworm Swarm Optimization (GSO) for Optimization of Machining Parameters,” Journal of Intelligent Manufacturing, 27(4), pp. 797−804, 2016.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Saipul Azmi Mohd Hashim, Norrizal Bin Abdul Razak, Jasni Bin Mohd Yusoff
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
