Metaheuristic approach in machinability evaluation of silicon carbide particle/glass fiber-reinforced polymer matrix composites during electrochemical discharge machining process

被引:28
作者
Antil, Parvesh [1 ]
Singh, Sarbjit [2 ]
Singh, Sunpreet [3 ]
Prakash, Chander [3 ]
Pruncu, Catalin Iulian [4 ,5 ]
机构
[1] Chaudhary Charan Singh Haryana Agr Univ, Coll Agr Engn & Technol, Hisar, Hisar, India
[2] Punjab Engn Coll, Chandigarh, India
[3] Lovely Profess Univ, Sch Mech Engn, Phagwara, India
[4] Univ Birmingham, Sch Engn, Dept Mech Engn, Birmingham, W Midlands, England
[5] Imperial Coll London, Dept Mech Engn, Exhibit Rd, London SW7 2AZ, England
关键词
Bees algorithm; differential evolution; electrochemical discharge machining; Taguchi's methodology; DIFFERENTIAL EVOLUTION; BEES ALGORITHM; PROCESS PARAMETERS; NEURAL-NETWORK; OPTIMIZATION; DESIGN; WEAR; ECDM;
D O I
10.1177/0020294019858216
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The advanced manufacturing and machining techniques are adopting a population-based metaheuristic algorithm for production, predicting and decision-making. Using the same approach, this paper deals with the application of bees algorithm and differential evolution to forecast the optimal parametric values aiming to obtain maximum material removal rate during electrochemical discharge machining of silicon carbide particle/glass fiber-reinforced polymer matrix composite. The bees algorithm follows swarm-based approach, while differential evolution works on a population-based approach. The experimental design was prepared on the basis of Taguchi's methodology using an L-16 orthogonal array. For the experimental analysis, the main variables in the process, that is, electrolyte concentration (g/L), inter-electrode gap (mm), duty factor and voltage (volts), were selected as main input parameters, and material removal rate (mg/min) was adjudged as output quality characteristic. A comparative investigation reveals that the maximum material removal rate was obtained by the parametric value proposed by differential evolution that follows the bees algorithm and Taguchi's methodology. Furthermore, the results prove that the differential evolution algorithm has better collective assessment capability with a rapid converging rate.
引用
收藏
页码:1167 / 1176
页数:10
相关论文
共 41 条
[21]   The Use of the Taguchi Method and a Neural-Genetic Approach to Optimize the Quality of a Pulsed Nd:YAG Laser Welding Process [J].
Lin, H. -L. .
EXPERIMENTAL TECHNIQUES, 2015, 39 (04) :21-29
[22]   An approach to optimize the EDM process parameters using desirability-based multi-objective PSO [J].
Majumder, Arindam ;
Das, Pankaj Kumar ;
Majumder, Abhishek ;
Debnath, Moutushee .
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL, 2014, 2 (01) :228-240
[23]   Analysis the effect of process parameters of ECDM during micro-machining on glass using genetic algorithm [J].
Mallick, B. ;
Sarkar, B. R. ;
Doloi, B. ;
Bhattacharyya, B. .
JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES, 2018, 12 (03) :3942-3960
[24]   Micro Machining of Nonconductive Al2O3 Ceramic on Developed TW-ECSM Setup [J].
Manna, Alakesh ;
Kundal, Amandeep .
INTERNATIONAL JOURNAL OF MANUFACTURING MATERIALS AND MECHANICAL ENGINEERING, 2011, 1 (02) :46-55
[25]   Application of Experimental Design and Analysis of Mathematical Models for Turning Inconel 718 Using Coated Carbide Tools [J].
Manohar, M. ;
Selvaraj, T. ;
Sivakumar, D. ;
Jeyapaul, R. ;
Jomy, J. .
EXPERIMENTAL TECHNIQUES, 2014, 38 (06) :61-71
[26]   Optimization of the directivity of a monopulse antenna with a subarray weighting by a hybrid differential evolution method [J].
Massa, Andrea ;
Pastorino, Matteo ;
Randazzo, Andrea .
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2006, 5 (155-158) :155-158
[27]   Modeling of Surface Roughness Using RSM, FL and SA in Dry Hard Turning [J].
Mia, Mozammel ;
Dhar, Nikhil Ranjan .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (03) :1125-1136
[28]  
Omran MGH, 2006, CONF CYBERN INTELL S, P80
[29]   Experimental Investigation and Analysis on Thrust Force in Drilling of Wood Composite Medium Density Fiberboard Panels [J].
Palanikumar, K. ;
Valarmathi, T. N. .
EXPERIMENTAL TECHNIQUES, 2016, 40 (01) :391-400
[30]   Using the bees algorithm with Kalman filtering to train an artificial neural network for pattern classification [J].
Pham, D. T. ;
Darwish, A. Haj .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2010, 224 (I7) :885-892