Application of TOPSIS an PIV Methods for Multi - Criteria Decision Making in Hard Turning Process
| 1 | Faculty of Mechanical Engineering, Hanoi University of Industry, Viet Nam |
CORRESPONDING AUTHOR
Do Duc Trung
Faculty of Mechanical Engineering, Hanoi University of Industry, Cau Dien, 100000, Hanoi, Viet Nam
Faculty of Mechanical Engineering, Hanoi University of Industry, Cau Dien, 100000, Hanoi, Viet Nam
Submission date: 2021-08-10
Final revision date: 2021-09-11
Acceptance date: 2021-09-26
Online publication date: 2021-09-29
Publication date: 2021-12-08
Journal of Machine Engineering 2021;21(4):57–71
KEYWORDS
TOPICS
ABSTRACT
In this study, TOPSIS and PIV methods were applied for multi-criteria decision making in hard turning process. Experiments have been conducted in accordance with an experimental matrix designed by the Taguchi method with a total of twenty-seven experiments. At each experiment, the values of coolant concentration, nose radius, coolant flow, cutting velocity, feed rate and depth of cut have been changed. Surface roughness, flank wear and roundess error have been selected as output criteria. The weights of criteria have been determined by three methods, inclusive of Equal weight, ROC weight and Entropy weight. The combination of multi-criteria decision-making methods with three weighting methods gives six ranking options of the experiments. The purpose of ranking the experiments is to find the experiment at which the three output parameters are ensured to have the minimum value simultaneously.
REFERENCES (38)
1.
TRUNG D.D., NGUYEN N. –T., DUC D.V., 2021, Study on Multi-Objective Optimization of the Turning Process of EN 10503 Steel by Combination of Teguchi Method and Moora Technique, EUREKA, Physics and Engineering, 2021/2, 52–65.
2.
DENKENA B., BERGMANN B., HANDRUP M., WITT M., 2020, Material Identification During Turning by Neural Network, Journal of Machine Engineering, 20/2, 65–76.
3.
OPRICOVIC S., TZENG G.-H., 2004, Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS, European Journal of Operational Research, 156/2, 445–455.
4.
HWANG C.–L., LAI Y.–J., LIU T.-Y., 1993, A New Approach for Multiple Objective Decision Making, Computers & Operations Research, 20/8, 889–899.
5.
MUFAZZAL S., MUZAKKIR S.M., 2018, A New Multi-Criterion Decision Making (MCDM) Method Based on Proximity Indexed Value for Minimizing Rank Reversals, Computers & Industrial Engineering, 1–38.
6.
YAKUP C., FATIH T., 2020, An in-Depth Review of Theory of The TOPSIS Method: An Experimental Analysis, Journal of Management Analytics, 7/2, 1–21.
7.
SINGH A., DATTA S., MAHAPATRA S.S., 2011, Application of TOPSIS in the Taguchi Method for Optimal Machining Parameter Selection, Journal for Manufacturing Science & Production, 11, 49-60.
8.
PARIDA A.K., ROUTARA B.C., 2014, Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method, International Scholarly Research Notices, 2014, 1–10.
9.
RAO C.M., RAO K.J., RAO K.L., 2016, Multi-Objective Optimization of MRR, Ra and Rz Using Topsis, International Journal of Engineering Sciences & Research Technology, 5/9, 376–384.
10.
PRAKASH D.B., KRISHNAIAH G., SHANKAR N.V.S., 2016, Optimization of Process Parameters Udding AHP and TOPSIS when Turning 1040 Steel with Coated Tools, International Journal of Mechanical Engineering and Technology, 7/6, 483–492.
11.
MAITY K., KHAN A., 2017, Application of MCDM-Based TOPSIS Method for the Selection of Optimal Process Parameter in Turning of Pure Titanium, Benchmarking, An International Journal, 24/7, 2009–2021.
12.
KHAN A., MAITY K., 2019, Application Potential Of Combined Fuzzy-TOPSIS Approach in Minimization of Surface Roughness, Cutting Force and Tool Wear During Machining of CP-Ti Grade II, Soft Computing, 23, 6667–6678.
13.
SINGH R., DUREJA J.S., DOGRA M., RANDHAWA J.S., 2019, Optimization of Machining Parameters Under MQL Turning of Ti-6Al-4V Alloy with Textured Tool Using Multi-Attribute Decision-Making Methods, World Journal of Engineering, 6/5, 648–659.
14.
MANE S.S., MULLA A.M., 2020, Relevant Optimization Method Selection in Turning of AISI D2 STEEL Steel Using Cryogenic Cooling, International Journal of Creative Research Thoughts, 8/10, 803–812.
15.
RAO S.R., JEELANI S.A.K., SWAMULU V., 2021, Multi-Objective Optimization Using TOPSIS in Turning of Al 6351 Alloy, IOP Conf. Series, Materials Science and Engineering, 1112/012010, 1–10.
16.
[16] DICH T.V., BINH N.T., DAT N.T., TIEP N.V., VIET T.X., 2003, Manufacturing Technology, Science and Technics Publishing House, Hanoi.
17.
TANABE I., YAMAGAMI Y., HOSHINO H., 2020, Development of a New High-Pressure Cooling System for Machining of Difficulut-to-Machine Materials, Journal of Machine Engineering, 20/1, 82–97.
19.
ROSZKOWSKA E., 2013, Rank Ordering Criteria Weighting Methods – A Comparative Overview, Journal Dedicated to the Needs of Science and Practice, 5/65, 1–168.
20.
KHAN N.Z., ANSARI T. S.A., SIDDIQUEE A.N., KHAN Z.A., 2019, Selection of E Learning Websites Using a Novel Proximity Indexed Value (PIV) MCDM Method, Journal of Computers in Education, 6, 241–256.
21.
WAKEEL S., BINGOL S., BASIR M.N., AHMAD S., 2020, Selection of Sustainable Material for the Manufacturing of Complex Automotive Products Using a New Hybrid Goal Programming Model For Best Worst Method–Proximity Indexed Value Method, Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 235/2, 1-15.
22.
ULUTAS A., KARAKOY C., 2019, An Analysis of the Logistics Performance Index of EU Countries with an Integrated MCDM Model, Economics and Business Review, 5/4, 49–69.
23.
RAIGAR J., SHARMA V.S., SRIVASTAVA S., CHAND R., SINGH J., 2020, A Decision Support System for the Selection of an Additive Manufacturing Process Using a New Hybrid MCDM Technique, Sadhana, 45/101, 1–14.
24.
DAWES R. M., COORIGAN B., 1974, Linear Models in Decision Malking, Psychological Bulletin, 81/2, 95–106.
25.
EINHORN H.J., MCCOACH W., 1997, A Symble Multiattribute Utility Procedure for Evaluation, Behavioral Scicence, 22/4, 270–282.
26.
YUXIN Z., DAZUO T., FENG Y., 2020, Effectiveness of Entropy Weight Method in Decision-Making, Mathematical Problems in Engineering, 2020, 1–5.
27.
KLOCKE F., BRINKSMEIER E., WEINERT K., 2005, Capability Profile of Hard Cutting and Grinding Processes, CIRP Annals – Manufacturing Technology, 54/2, 22–45.
28.
KO T.J., KIM H.S., 2001, Surface Integrity and Machineability in Intermittent Hard Turning, The International Journal of Advanced Manufacturing Technology, 18, 168–175.
30.
UYEN V.T.N., SON N.H., 2020, Improving Accuracy of Surface Roughness Model While Turning 9XC Steel Using a Titanium Nitride-Coated Cutting Tool with Johnson and Box-Cox Transformation, AIMS Mat. Science, 8/1, 1–17.
31.
RPADEEP A.V., SURYAM L.V., PRASAS S.V.S., VAHINI K., 2018, Experimental Investigation and Comparison of Flank Wear and Surface Roughness in Turning of AISI 4340 Steel Using Ceramic Coated and Uncoated Carbide Inserts, International Journal of Mechanical and Production Engineering Research and Development, 8/5, 337–346.
32.
GUPTA D.V.K., SHARMA V.S., DOGRA M., 2010, Wear Mechanisms of Tin-Coated CBN Tool During Finish Hard Turning of Hot Tool Die Steel, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224/4, 553–566.
33.
TRUNG D.D., 2021, Influence of Cutting Parameters on Surface Roughness in Grinding of 65G Steel, Tribology in Industry, 43/1, 167–176.
34.
GROOVER M.P., 1996, Fundamentals of Modern Manufacturing, Prentice Hall, Upper Saddle River, NJ.
35.
SILVA R.B.D., MACHADO A.R., EZUGWU E.O., BONNEY J., SALES W.F., 2013, Tool Life and Wear Mechanisms in High Speed Machining of Ti–6Al–4V Alloy with PCD Tools Under Various Coolant Pressures, Journal of Materials Processing Technology, 213/8, 1459–1464.
36.
GONZALEZ L.W.H., AHMED Y.S., RODRIGUEZ R.P., RODLEDO P.D.C.Z., MATA M.P.G., 2018, Selection of Machining Parameters Using a Correlative Study of Cutting Tool Wear in High-Speed Turning of AISI 1045 Steel, Journal of Manufacturing and Materials Processing, 2/66, 1–14.
37.
XU W., WU Y., SATO T., LIN W., 2010, Effects of Process Parameters on Workpiece Roundness in Tangential-Feed Centerless Grinding Using A Surface Grinder, Journal of Materials Processing Technology, 210/5, 759–766.
38.
MAJUMDER H., SAHA A., 2018, Application of MCDM Based Hybrid Optimization Tool During Turning of ASTM A588, Decision Science Letters, 7, 143–156.
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