Skip to main content
SpringerLink
Log in

Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

A non-dominated sorting modified teaching–learning-based optimization (NSMTLBO) is proposed to obtain the optimum solution for a multi-objective problem related to machining Polytetrafluoroethylene. Firstly, an experimental design is done and the L27 orthogonal array with three-level of cutting speed \( \left( {V_{c} } \right) \), feed rate (f), depth of cut (ap) and nose radius \( \left( {N_{r} } \right) \) is formulated. A CNC turning machine is used to perform experiments with cemented carbide tool at an insert angle of 80° and the response variables known as surface finish and material removal rate are measured. A response surface model is rendered from the experimental results to derive the minimization function of surface roughness \( \left( {R_{a} } \right) \) and maximization function of material removal rate (MRR). Both optimization functions are solved simultaneously using NSMTLBO. A fuzzy decision maker is also integrated with NSMTLBO to determine the preferred optimum machining parameters from Pareto-front based on the relative importance level of each objective function. The best responses Ra = 2.2347 µm and MRR = 96.835 cm3/min are predicted at the optimum machining parameters of Vc = 160 mm/min, f = 0.5 mm/rev, ap = 0.98 mm and Nr = 0.8 mm. The proposed NSMTLBO is reported to outperform other six peer algorithms due to its excellent capability in generating the Pareto-fronts which are more uniformly distributed and resulted higher percentage of non-dominated solutions. Furthermore, the prediction results of NSMTLBO are validated experimentally and it is reported that the performance deviations between the predicted and actual results are lower than 3.7%, implying the applicability of proposed work in real-world machining applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

CFRP:

Carbon fibre reinforced polymer

DOE:

Design of experiment

ECM:

Electrochemical machining

EDM:

Electric discharge machining

EMOTLBO:

Enhanced multi-objective teaching–learning-based optimization

FIB:

Focused ion beam

ICA:

Imperialist competitive algorithm

microEDM:

Micro-electric discharge machining

MOEA:

Multi-objective evolutionary algorithms

MOGWO:

Multi-objective grey wolf optimizer

MO-ITLBO:

Multi-objective improved teaching–learning based optimization

MOP:

Multi-objective optimization problem

MOPSO:

Multi-objective particle swarm optimization

MOTLBO:

Multi-objective teaching–learning-based optimization

NSGA-II:

Non-dominated sorting genetic algorithm II

NSMTLBO:

Non-dominated sorting modified teaching–learning-based optimization

NSTLBO:

Non-dominated sorting teaching–learning-based optimization

PSO:

Particle swarm optimization

PCD:

Polycrystalline diamond

PTFE:

Polytetrafluoroethylene

RSM:

Response surface model

TLBO:

Teaching-learning-based optimization

WEDM:

Wire-electric discharge machining

d :

Index of each dimension component of learner

m :

Index of objective function

n :

Index of learner

r :

Index to indicate the rank of a given front

s :

Index of learner that is randomly selected for comparison

\( F_{r} \) :

Set containing all learners with the r-th rank value

\( L_{m,r} \) :

Set containing all sorted members of the r-th front for the m-th objective

Q :

Set containing all learners to create the next front

\( S_{n} \) :

Set containing all solutions dominated by the n-th learner

\( {\mathbf{Rank}} \) :

Set containing all non-domination rank values of N learners

\( {\mathbf{F}} \) :

Set containing all members from R fronts

\( {\varvec{\Delta}} \) :

Set containing the crowding distances of all N learners

\( {\mathbf{P}} \) :

Set containing all population members

\( {\mathbf{P}}^{{{\mathbf{off}}}} \) :

Set containing all offspring members

\( {\mathbf{P}}^{{{\mathbf{comb}}}} \) :

Set containing the combination of both population and offspring members

\( {\varvec{\Psi}}^{{\mathbf{U}}} \) :

Set containing the utopia point of a multi-objective optimization problem with M objective functions

\( {\varvec{\Psi}}^{{{\mathbf{SN}}}} \) :

Set containing the pseudo nadir point of a multi-objective optimization problem with M objective functions

R P :

Set containing the Pareto non-dominated solution set of NSMTLBO

S P :

Set containing the Pareto non-dominated solution set of MOPSO

T P :

Set containing the Pareto non-dominated solution set of NSGA-II

U P :

Set containing the Pareto non-dominated solution set of MOGWO

V P :

Set containing the Pareto non-dominated solution set of MOTLBO

W P :

Set containing the Pareto non-dominated solution set of MO-ITLBO

X P :

Set containing the Pareto non-dominated solution set of NSTLBO

\( \psi \left( { \cdot , \cdot , \cdot , \cdot } \right) \) :

An operator that returns the response variable value based on the given control variables

\( \varPsi_{m} \left( \cdot \right) \) :

An operator that returns the value of the m-th objective function based on the given individual solution

\( Trunc\left( { \cdot , \cdot } \right) \) :

An operator that returns the best N members with the lowest ranking and highest crowding distance values

\( C\left( { \cdot , \cdot } \right) \) :

An operator that returns the percentage of solution set from one Pareto front that is dominated by solution set from another Pareto front

\( \prec_{cco} \) :

Crowding-comparison operator to compare the superiority of two solutions

\( V_{c} \) :

Cutting speed

f :

Feed rate

ap :

Depth of cut

\( N_{r} \) :

Nose radius

\( R_{a} \) :

Surface roughness

\( \Delta R_{a} \) :

Error rate of surface roughness

MRR :

Material removal rate

\( \Delta MRR \) :

Error rate of material removal rate

\( D_{initial} \) :

Initial diameter of PTFE sample before machining process

\( D_{final} \) :

Final diameter of PTFE sample after machining process

L :

Length of cut of PTFE sample

T :

Time taken to cut PTFE sample

Y :

Response term of regression equation

\( \alpha_{0} \) :

Free term of regression equation

\( X_{i} \) :

Control variable term of regression equation

\( \beta_{i} \) :

Linear coefficient term of regression equation

\( \beta_{ii} \) :

Quadratic coefficient term of regression equation

\( \beta_{ij} \) :

Interacting coefficient term of regression equation

D :

Number of decision variables to be optimized

N :

Population size

\( T_{f} \) :

Teaching factor that can be set as either 1 or 2

\( T_{f1} ,T_{f2} \) :

Teaching factors with the range of 1 to 2 generated from uniform distribution

\( C_{n} \) :

Domination count to indicate the number of solutions dominate the n-th learner

\( Rank_{n} \) :

Non-domination rank value of the n-th learner

\( \Delta_{a,r} \) :

Crowding distance of the a-th member in the r-th front

R :

Upper limit of front counter

\( \left| {F_{r} } \right| \) :

Number of members in the r-th front

\( X_{n,d} \) :

The d-th component of n-th candidate solution

\( X^{teacher} \) :

Solution vector that represents the best solution known as teacher

\( X^{mean} \) :

Solution vector that represents the average knowledge level of population

\( X_{n}^{new} \) :

New solution vector produced by the n-th learner during the teacher or learner phases

\( X_{d}^{U} \) :

Upper limit of the d-th dimensional component

\( X_{d}^{L} \) :

Lower limit of the d-th dimensional component

\( X_{a,d}^{Cand} \) :

Solution vector of the d-th dimensional component of a-th candidate teacher

\( X^{preferred} \) :

Solution vector of most preferred Pareto optimal solution

\( X_{n}^{teacher} \) :

Solution vector of teacher assigned to the n-th learner

\( \tilde{X}_{n}^{mean} \) :

Weighted mean position vector assigned to the n-th learner

\( E_{n,a} \) :

Normalized Euclidean distance between the n-th learner and the a-th candidate teacher

\( r_{1} ,r_{2} ,r_{3} ,r_{4} \) :

Random numbers with the range of 0 to 1 generated from uniform distribution

\( r_{5} \) :

Random numbers with the range of − 1 to 1 generated from uniform distribution

\( P_{cr} \) :

Crossover rate

\( P_{mut} \) :

Mutation probability

\( d_{r} \) :

Randomly selected dimensional component for mutation

\( \varPsi_{m}^{U} \) :

Utopia point of a multi-objective optimization problem in the m-th objective function

\( \varPsi_{m}^{SN} \) :

Pseudo nadir point of a multi-objective optimization problem in the m-th objective function

\( \mu_{a}^{m} \) :

Membership value of the a-th Pareto optimal solution in the m-th objective function

\( \mu_{a} \) :

Total degree of optimality of each a-th Pareto optimal solution

\( w_{m} \) :

Relative importance of each m-th objective function

\( w_{1} \) :

Relative importance level of minimizing surface finish

\( w_{2} \) :

Relative importance level of maximizing material removal rate

\( \gamma \) :

Counter of function evaluations

\( \varGamma \) :

Maximum fitness evaluation numbers

\( d_{a} \) :

Smallest Euclidean distance between the a-th and b-th Pareto optimal solutions

\( \bar{d} \) :

Average value of all smallest Euclidean distance

S :

Spacing measure

SD :

Standard deviation

R 2 :

Percentage of variation of data

P :

Significance of control variables

\( c_{1} ,c_{2} \) :

Acceleration coefficients

\( \alpha \) :

Grid inflation rate

\( nGrid \) :

Number of grid per dimension

\( nGroup \) :

Number of group created for multiple group learning

\( \varepsilon \) :

Parameter used for epsilon dominance method

\( \left| A \right| \) :

Archive size

References

  • Abbas, A. T., Aly, M., & Hamza, K. (2016). Multiobjective optimization under uncertainty in advanced abrasive machining processes via a fuzzy-evolutionary approach. Journal of Manufacturing Science and Engineering,138(7), 071003–071009. https://doi.org/10.1115/1.4032567.

    Article  Google Scholar 

  • Abhishek, K., Rakesh Kumar, V., Datta, S., & Mahapatra, S. S. (2017). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm). Journal of Intelligent Manufacturing,28(8), 1769–1785. https://doi.org/10.1007/s10845-015-1050-8.

    Article  Google Scholar 

  • Aghaei, J., Amjady, N., & Shayanfar, H. A. (2011). Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Applied Soft Computing,11(4), 3846–3858. https://doi.org/10.1016/j.asoc.2011.02.022.

    Article  Google Scholar 

  • Al-Omoush, A. A., Alsewari, A. A., Alamri, H. S., & Zamli, K. Z. (2019). Comprehensive review of the development of the harmony search algorithm and its applications. IEEE Access,7, 14233–14245. https://doi.org/10.1109/access.2019.2893662.

    Article  Google Scholar 

  • Chabbi, A., Yallese, M. A., Nouioua, M., Meddour, I., Mabrouki, T., & Girardin, F. (2017). Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methods. The International Journal of Advanced Manufacturing Technology,91(5), 2267–2290. https://doi.org/10.1007/s00170-016-9858-8.

    Article  Google Scholar 

  • Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation,8(3), 256–279. https://doi.org/10.1109/tevc.2004.826067.

    Article  Google Scholar 

  • Collette, Y., & Siarry, P. (2003). Multiobjective optimization: Principles and case studies. Berlin: Springer.

    Google Scholar 

  • Corne, D. W., Jerram, N. R., Knowles, J. D., & Oates, M. J. (2001). PESA-II: Region-based selection in evolutionary multiobjective optimization. In Paper presented at the proceedings of the 3rd annual conference on genetic and evolutionary computation, San Francisco, California.

  • Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation,18(4), 577–601. https://doi.org/10.1109/tevc.2013.2281535.

    Article  Google Scholar 

  • Deb, K., Mohan, M., & Mishra, S. (2005). Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evolutionary Computation,13(4), 501–525. https://doi.org/10.1162/106365605774666895.

    Article  Google Scholar 

  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation,6(2), 182–197. https://doi.org/10.1109/4235.996017.

    Article  Google Scholar 

  • Fan, Q., & Yan, X. (2016). Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Transactions on Cybernetics,46(1), 219–232. https://doi.org/10.1109/tcyb.2015.2399478.

    Article  Google Scholar 

  • Hu, P., Chen, S., Huang, H., Zhang, G., & Liu, L. (2019). Improved alpha-guided grey Wolf optimizer. IEEE Access,7, 5421–5437. https://doi.org/10.1109/access.2018.2889816.

    Article  Google Scholar 

  • Ji, J., Gao, S., Wang, S., Tang, Y., Yu, H., & Todo, Y. (2017). Self-adaptive gravitational search algorithm with a modified chaotic local search. IEEE Access,5, 17881–17895. https://doi.org/10.1109/access.2017.2748957.

    Article  Google Scholar 

  • Jiao, K., & Pan, Z. (2019). A novel method for image segmentation based on simplified pulse coupled neural network and gbest led gravitational search algorithm. IEEE Access,7, 21310–21330. https://doi.org/10.1109/access.2019.2894301.

    Article  Google Scholar 

  • Li, Y., Gong, H., Feng, D., & Zhang, Y. (2011). An adaptive method of speckle reduction and feature enhancement for SAR images based on curvelet transform and particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing,49(8), 3105–3116. https://doi.org/10.1109/tgrs.2011.2121072.

    Article  Google Scholar 

  • Li, D., Zhang, C., Shao, X., & Lin, W. (2016). A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. Journal of Intelligent Manufacturing,27(4), 725–739. https://doi.org/10.1007/s10845-014-0919-2.

    Article  Google Scholar 

  • Liang, J., Qu, B.-Y., Suganthan, P., & Hernandez-Diaz, A. (2013). Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. In Tech. Rep. Zhengzhou, China: Computational Intelligence Laboratory, Zhengzhou University.

  • Lim, W. H., & Isa, N. A. M. (2015). Particle swarm optimization with dual-level task allocation. Engineering Applications of Artificial Intelligence,38, 88–110. https://doi.org/10.1016/j.engappai.2014.10.022.

    Article  Google Scholar 

  • Lin, W., Yu, D. Y., Wang, S., Zhang, C., Zhang, S., Tian, H., et al. (2015). Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations. Engineering Optimization,47(7), 994–1007. https://doi.org/10.1080/0305215x.2014.928818.

    Article  Google Scholar 

  • Mathew, D., Rani, C., Kumar, M. R., Wang, Y., Binns, R., & Busawon, K. (2018). Wind-driven optimization technique for estimation of solar photovoltaic parameters. IEEE Journal of Photovoltaics,8(1), 248–256. https://doi.org/10.1109/jphotov.2017.2769000.

    Article  Google Scholar 

  • Mellal, M. A., & Williams, E. J. (2016). Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic. Journal of Intelligent Manufacturing,27(5), 927–942. https://doi.org/10.1007/s10845-014-0925-4.

    Article  Google Scholar 

  • Mia, M. (2018). Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement,121, 249–260.

    Article  Google Scholar 

  • Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications,47, 106–119. https://doi.org/10.1016/j.eswa.2015.10.039.

    Article  Google Scholar 

  • Mistry, K., Zhang, L., Neoh, S. C., Lim, C. P., & Fielding, B. (2017). A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Transactions on Cybernetics,47(6), 1496–1509. https://doi.org/10.1109/tcyb.2016.2549639.

    Article  Google Scholar 

  • Mohanty, C. P., Mahapatra, S. S., & Singh, M. R. (2016). A particle swarm approach for multi-objective optimization of electrical discharge machining process. Journal of Intelligent Manufacturing,27(6), 1171–1190. https://doi.org/10.1007/s10845-014-0942-3.

    Article  Google Scholar 

  • Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers and Industrial Engineering,50(1), 15–34. https://doi.org/10.1016/j.cie.2005.10.001.

    Article  Google Scholar 

  • Natarajan, E., Kaviarasan, V., Lim, W. H., Tiang, S. S., & Tan, T. H. (2018). Enhanced multi-objective teaching-learning-based optimization for machining of delrin. IEEE Access,6, 51528–51546. https://doi.org/10.1109/access.2018.2869040.

    Article  Google Scholar 

  • Ong, P., Chong, C. H., Bin Rahim, M. Z., Lee, W. K., Sia, C. K., & Bin Ahmad, M. A. H. (2018). Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1443-6.

    Article  Google Scholar 

  • Patel, V. K., & Savsani, V. J. (2016). A multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Information Sciences,357, 182–200. https://doi.org/10.1016/j.ins.2014.05.049.

    Article  Google Scholar 

  • Petković, D., Madić, M., Radovanović, M., & Gečevska, V. (2017). Application of the performance selection index method for solving machining MCDM problems. Petković,15(1), 97–106.

    Google Scholar 

  • Rao, R. V., More, K. C., Taler, J., & Ocion, P. (2016). Optimal design of stirling heat engine using an advanced optimization algorithm. Sadhana,41(11), 1321–1331.

    Article  Google Scholar 

  • Rao, R. V., More, K. C., Taler, J., & Ocłoń, P. (2017a). Multi-objective optimization of thermo-acoustic devices using teaching-learning-based optimization algorithm. Science and Technology for the Built Environment,23(8), 1244–1252. https://doi.org/10.1080/23744731.2017.1296319.

    Article  Google Scholar 

  • Rao, R. V., & Patel, V. (2013). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling,37(3), 1147–1162. https://doi.org/10.1016/j.apm.2012.03.043.

    Article  Google Scholar 

  • Rao, R. V., Rai, D. P., & Balic, J. (2017b). A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence,61, 103–125. https://doi.org/10.1016/j.engappai.2017.03.001.

    Article  Google Scholar 

  • Rao, R. V., Rai, D. P., & Balic, J. (2018). Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm. Journal of Intelligent Manufacturing,29(8), 1715–1737. https://doi.org/10.1007/s10845-016-1210-5.

    Article  Google Scholar 

  • Rao, R. V., Rai, D. P., & Balic, J. (2019). Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE method. Journal of Intelligent Manufacturing,30(5), 2101–2127. https://doi.org/10.1007/s10845-017-1373-8.

    Article  Google Scholar 

  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design,43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015.

    Article  Google Scholar 

  • Rao, R. V., & Waghmare, G. G. (2015). Multi-objective design optimization of a plate-fin heat sink using a teaching-learning-based optimization algorithm. Applied Thermal Engineering,76, 521–529. https://doi.org/10.1016/j.applthermaleng.2014.11.052.

    Article  Google Scholar 

  • Sathiyamoorthy, V., Sekar, T., & Elango, N. (2015a). Optimization of processing parameters in ECM of die tool steel using nanofluid by multiobjective genetic algorithm. The Scientific World Journal,2015, 6. https://doi.org/10.1155/2015/895696.

    Article  Google Scholar 

  • Sathiyamoorthy, V., Sekar, T., Suresh, P., Vijayan, R., & Elango, N. (2015b). Optimization of processing parameters in electrochemical machining of AISI 202 using response surface methodology. Journal of Engineering Science and Technology,10(6), 780–789.

    Google Scholar 

  • Senthilnath, J., Kulkarni, S., Benediktsson, J. A., & Yang, X. S. (2016). A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geoscience and Remote Sensing Letters,13(4), 599–603. https://doi.org/10.1109/lgrs.2016.2530724.

    Article  Google Scholar 

  • Solihin, M. I., Akmeliawati, R., Muhida, R., & Legowo, A. Guaranteed robust state feedback controller via constrained optimization using Differential Evolution. In 2010 6th International colloquium on signal processing and its applications, 2010 (pp. 1–6). https://doi.org/10.1109/cspa.2010.5545279.

  • Solihin, M. I., Wahyudi, & Akmeliawati, R. PSO-based optimization of state feedback tracking controller for a flexible link manipulator. In 2009 International conference of soft computing and pattern recognition, 2009 (pp. 72–76). https://doi.org/10.1109/socpar.2009.26.

  • Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y.-P., Auger, A., et al. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In Tech. Rep. Singapore: Nanyang Technological University.

  • Sultana, S., & Roy, P. K. (2014). Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. International Journal of Electrical Power and Energy Systems,63, 534–545. https://doi.org/10.1016/j.ijepes.2014.06.031.

    Article  Google Scholar 

  • Suresh, P., Venkatesan, R., Sekar, T., Elango, N., & Sathiyamoorthy, V. (2014). Optimization of intervening variables in MicroEDM of SS 316L using a genetic algorithm and response-surface methodology. Strojniski Vestnik/Journal of Mechanical Engineering,60(10), 9–10. https://doi.org/10.5545/sv-jme.2014.1665.

    Article  Google Scholar 

  • Tang, S. H., Ang, C. K., Ariffin, M. K. A. B. M., & Mashohor, S. B. (2014). Predicting the motion of a robot manipulator with unknown trajectories based on an artificial neural network. International Journal of Advanced Robotic Systems,11(10), 176. https://doi.org/10.5772/59278.

    Article  Google Scholar 

  • Tang, S. H., Ang, C. K., Nia, D. N., Ariffin, M. K. A. M., & Khaksar, W. (2013). Planning for redundant manipulator based on back-propagation neural network. Advanced Science Letters,19(11), 3307–3310. https://doi.org/10.1166/asl.2013.5151.

    Article  Google Scholar 

  • Tang, K., Li, X., Suganthan, P., Yan, Z., & Wiese, T. (2010). Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization. In Tech. Rep. Hefei, China: School of Computer Science and Technology, University of Science and Technology of China.

  • Teimouri, R., Baseri, H., & Moharami, R. (2015). Multi-responses optimization of ultrasonic machining process. Journal of Intelligent Manufacturing,26(4), 745–753. https://doi.org/10.1007/s10845-013-0831-1.

    Article  Google Scholar 

  • Van, M., & Kang, H. (2016). Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization. IEEE Transactions on Industrial Informatics,12(1), 124–135. https://doi.org/10.1109/tii.2015.2500098.

    Article  Google Scholar 

  • Viswanathan, R., Ramesh, S., Elango, N., & Kamesh Kumar, D. (2017). Temperature measurement and optimisation in machining magnesium alloy using RSM and ANOVA. Pertanika Journal of Science and Technology,25(1), 255–262.

    Google Scholar 

  • Yao, L., Damiran, Z., & Lim, W. H. Energy management optimization scheme for smart home considering different types of appliances. In 2017 IEEE international conference on environment and electrical engineering and 2017 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe), 2017 (pp. 1–6). https://doi.org/10.1109/eeeic.2017.7977565.

  • Yao, L., Lai, C., & Lim, W. H. Home energy management system based on photovoltaic system. In 2015 IEEE international conference on data science and data intensive systems, 2015 (pp. 644–650). https://doi.org/10.1109/dsdis.2015.42.

  • Yu, K., Wang, X., & Wang, Z. (2015). Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemometrics and Intelligent Laboratory Systems,146, 198–210. https://doi.org/10.1016/j.chemolab.2015.05.015.

    Article  Google Scholar 

  • Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation,11(6), 712–731. https://doi.org/10.1109/tevc.2007.892759.

    Article  Google Scholar 

  • Zhao, Z., Lee, W. C., Shin, Y., & Song, K. (2013). An optimal power scheduling method for demand response in home energy management system. IEEE Transactions on Smart Grid,4(3), 1391–1400. https://doi.org/10.1109/tsg.2013.2251018.

    Article  Google Scholar 

  • Zhao, J., Wen, F., Dong, Z. Y., Xue, Y., & Wong, K. P. (2012). Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization. IEEE Transactions on Industrial Informatics,8(4), 889–899. https://doi.org/10.1109/tii.2012.2205398.

    Article  Google Scholar 

  • Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation,8(2), 173–195. https://doi.org/10.1162/106365600568202.

    Article  Google Scholar 

  • Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In Proceedings of the evolutionary methods for design, optimization and control with applications to industrial problems, Athens. Greece.

Download references

Acknowledgements

This work is partially supported by UCSI University Pioneer Scientist Incentive Fund (PSIF) with Project Code of Proj-In-FETBE-34 and Proj-In-FETBE-50.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia

    Elango Natarajan, Wei Hong Lim & Sew Sun Tiang

  2. Department of Mechanical Engineering, Sona College of Technology, Salem, Tamil Nadu, 636005, India

    Varadaraju Kaviarasan

  3. School of Engineering (Mechatronics), Monash University, Subang Jaya, Malaysia

    S. Parasuraman

  4. FTMS College, Cyber Jaya, Malaysia

    Sangeetha Elango

Authors
  1. Elango Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Varadaraju Kaviarasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Hong Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sew Sun Tiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Parasuraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sangeetha Elango
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elango Natarajan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Natarajan, E., Kaviarasan, V., Lim, W.H. et al. Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE). J Intell Manuf 31, 911–935 (2020). https://doi.org/10.1007/s10845-019-01486-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-019-01486-9

Keywords

Search

Navigation