Autors: Abdullahu F., Zhujani F., Todorov, G. D., Kamberov, K. H.
Title: An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process
Keywords: DEAR, drilling, GRA, MCDM, multi, optimization, WGRA

Abstract: The goal of this research is to optimize the input parameters utilized in dry CNC drilling of forging steel to attain sustainable machining. Particular emphasis will be placed on achieving high productivity while minimizing the impact on surface quality. To achieve the aforementioned goal, three Taguchi-based multi-criteria decision-making (MCDM) approaches, such as traditional gray relational analysis (GRA), weighted gray relational analysis (WGRA), and data envelopment analysis ranking (DEAR), were used for simultaneous optimization of the MRR and Ra. In Taguchi’s L12 (24) orthogonal array design, the cutting mode parameters—such as cutting speed, depth of cut, feed rate, and point angle—have been chosen as the input parameters for the modeling and analysis of the drilling process characteristics. The process of determining the effect of the input parameters on the output parameters was carried out with the use of analysis of variance (ANOVA). The best results from the studies were Ra = 2.19 and MRR = 375 mm3/s, which corresponded to Taguchi’s single optimization levels, S2F1D1A2 and S2F2D2A1, respectively. In the next step, the performance values obtained for each MCDM technique were reoptimized using the Taguchi method, and the optimal levels were obtained: for traditional GRA, the level S2F1D2A1 (Ra = 2.52 µm, MRR = 125 mm3/s); for WGRA, the level S2F1D1A1 (Ra = 2.31 µm, MRR = 83 mm3/s); and for DEAR, the level S2F2D2A1 (Ra = 4.42 µm, MRR = 375 mm3/s), respectively. Lastly, in order to compare the experiments’ performance, validation tests were carried out. The results of the experiments using multi-objective optimization show that traditional GRA improved the overall quality response characteristics by 29.86% compared to the initial setup parameters, while weighted GRA improved them by 34.48%, with the DEAR method providing an improvement of 96%. Based on the findings of this investigation, the DEAR optimization method outperforms the GRA method. As a result, the proposed methods are useful tools for multi-objective optimization of cutting parameters.

References

  1. Korle T. Okwu I. Nkoi B. Manyie S. Optimization of Machining Parameters in Turning Operation of Grey Cast Iron Using ANSYS: A Case Study Glob. Sci. J. 2022 10 115 127
  2. Karthick K. Kannan V. Sivasubramaniam A. Optimization of Machining Parameters in Face Milling Int. J. Innov. Res. Sci. Eng. Technol. IJIRSET 2022 11 104 108 10.15680/IJIRSET
  3. Sujit J. Optimization of Process Parameters for Optimal MRR during Turning Steel Bar using Taguchi Method and ANOVA Int. J. Mech. Eng. Robot. Res. 2014 3 231 243
  4. Tanveer B. Imtiaz A. Optimization of Cutting Parameters in Turning Process SAE Int. J. Mater. Manuf. 2014 7 233 239
  5. Choudhury S. Bartarya G. Role of temperature and surface finish in predicting tool wear using neural network and design of experiments Int. J. Mach. Tools Manuf. 2004 43 747 753 10.1016/S0890-6955(02)00166-9
  6. Tsao C. Taguchi analysis of drilling quality associated with core drill in drilling of composite material Int. J. Adv. Manuf. Technol. 2004 32 877 884 10.1007/s00170-006-0414-9
  7. Sushil Sabari R. Thiruvenkataselvam K. Vanjinathan S. Sathish R. An optimization of drilling parameters on ss304 using various drill bits Int. Res. J. Eng. Technol. IRJET 2020 7 5831 5837
  8. Aamir M. Giasin K. Rad M. Din I. Hanif I. Kuklu U. Pimenov Y. Ikhlaq M. Effect of Cutting Parameters and Tool Geometry on the Performance Analysis of One-Shot Drilling Process of AA2024-T3 Metals 2021 11 854 10.3390/met11060854
  9. Manoj Modi M. Agarwal G. Patil V. Umesh B. Rishabh P. Parametric Optimization in Drilling of Al–SiC Composite Using Taguchi Method Int. J. Sci. Technol. Res. 2019 8 2019 2022
  10. Sumesh A.S. Shibu M. Optimization of Drilling Parameters for Minimum Surface Roughness Using Taguchi Method J. Mech. Civ. Eng. (IOSR-JMCE) 2016 12 20 Corpus ID: 120155885
  11. Krishnan Kovil, India, 28–30 March 2013 EN 10083-1.1191 Steels for Quenching and Tempering—Part 1: General Technical Delivery Conditions European Committee for Standardization (CEN) Brussels, Belgium 1966
  12. Black J. Kohser R. DeGarmo’s Materials and Processes in Manufacturing 11th ed. Wiley Hoboken, NJ, USA 2012 978-0-470-92467-9
  13. Iqbal M. Santhakumar J. Sharma G. Singh R. Multi Objective Optimization of Drilling Process Parameters on Aluminium 6061 Alloy using GRA and DEAR Technique AIP Conf. Proc. 2022 2460 020010 10.1063/5.0095655
  14. Roy R.K. A Primer on the Taguchi Method, Society of Manufacturing Engineers Society of Manufacturing Engineers Southfield, MI, USA 2010
  15. Ramakrishna A. Rawabawale N.A. Nagure S.M. Experimental Investigation to Optimize process Parameters in Drilling Operation for Composite Material Int. Res. J. Eng. Technol. 2020 7 9
  16. Mohapatraa K.D. Satpathya M.P. Sahoo S.K. Comparison of optimization techniques for MRR and surface roughness in wire EDM process for gear cutting Int. J. Ind. Eng. Comput. 2017 8 251 262 10.5267/j.ijiec.2016.9.002
  17. Gugulothu B. Rao G.K.M. Bezabih M. Grey relational analysis for multi-response optimization of process parameters in green electrical discharge machining of Ti-6Al-4V alloy Mater. Today Proc. 2020 46 89 98 10.1016/j.matpr.2020.06.135
  18. Solanki M. Jain A. Optimization of material removal rate and surface roughness using Taguchi based multi-criteria decision making (MCDM) technique for turning of Al-6082 Proc. Eng. Sci. 2021 3 303 318 10.24874/PES03.03.007
  19. Wang Z. Li L. Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis Adv. Mech. Eng. 2021 13 1 8 10.1177/1687814021996530
  20. Lukic D. Cep R. Vukman J. Antic A. Djurdjev M. Milosevic M. Multi-Criteria Selection of the Optimal Parameters for High-Speed Machining of Aluminum Alloy Al7075 Thin-Walled Parts Metals 2020 10 1570 10.3390/met10121570
  21. Köksalan M. Wallenius J. Zionts S. Multiple Criteria Decision Making: From Early History to the 21st Century World Scientific Singapore 2011
  22. Cui Y. Geng Z. Zhu Q. Han Y. Review: Multi-objective optimization methods and application in energy saving Energy 2017 125 681 704 10.1016/j.energy.2017.02.174
  23. Sathish R. Rodrigues L. Impact of process factors on twist drill wear in machining FRP composites by applying Taguchi design analysis and ANOVA technique Int. J. Adv. Technol. Eng. Res. 2014 4 12 17
  24. Balraj U. Krishna A. Multi-Objective Optimization of EDM Process Parameters using Taguchi Method, Principal Component Analysis and Grey Relational Analysis Int. J. Manuf. Mater. Mech. Eng. 2014 4 29 46 10.4018/ijmmme.2014040103
  25. Thangaraj M. Ahmadein M. Alsaleh N.A. Elsheikh A.H. Optimization of Abrasive Water Jet Machining of SiC Reinforced Aluminum Alloy Based Metal Matrix Composites Using Taguchi–DEAR Technique Materials 2021 14 6250 10.3390/ma14216250 34771777
  26. Deng J.-L. Control problems of grey systems Syst. Control Lett. 1982 1 288 294
  27. Liu S. Forrest J. Yang Y. A brief introduction to grey systems theory Grey Syst. Theory Appl. 2012 2 89 104 10.1108/20439371211260081
  28. Mahmoudi A. Javed S. Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach Group. Decis. Negot. 2022 31 1051 1096 10.1007/s10726-022-09790-1 36042813
  29. Deng L. Introduction to grey system theory J. Grey Syst. 1989 1 1 24
  30. Tebassi H. Yallese M. Belhadi S. Single and multiple quality characteristics optimization, expanded to the machinability assessment at the optimal cutting combinations across Taguchi OA, GRA and BBD: An overall view Res. Sq. 2022 10.21203/rs.3.rs-2019418/v1
  31. Gallih Bagus G. Bobby O.P. Soepangkat B. Krisnanto I. Multiple-performance optimization of drilling parameters and tool geometries in drilling GFRP composite stacks using Taguchi and grey relational analysis (GRA) method ARPN J. Eng. Appl. Sci. 2016 11 2
  32. Pandey R.K. Panda S.S. Optimization of multiple quality characteristics in bone drilling using grey relational analysis J. Orthop. 2015 12 39 45 10.1016/j.jor.2014.06.003 25829751
  33. Niu B. Shi M. Zhang Z. Li Y. Cao Y. Pan S. Multi-objective optimization of supply air jet enhancing airflow uniformity in data center with Taguchi-based grey relational analysis Build. Environ. 2022 208 108606 10.1016/j.buildenv.2021.108606
  34. Bademlioglu A.H. Canbolat A.S. Yamankaradeniz N. Kaynakli O. Investigation of parameters affecting Organic Rankine Cycle efficiency by using Taguchi and ANOVA methods Appl. Therm. Eng. 2018 145 221 228 10.1016/j.applthermaleng.2018.09.032
  35. Liu S. Lin Y. Grey Information: Theory and Practical Applications 1st ed. Springer Berlin, Germany 2006
  36. Yan Y. Li L. Multi-objective optimization of milling parameters—The trade-offs between energy, production rate and cutting quality J. Clean. Prod. 2013 52 462 471 10.1016/j.jclepro.2013.02.030
  37. Dolado J.J. On the problem of the software cost function Inf. Softw. Technol. 2001 43 61 72 10.1016/S0950-5849(00)00137-3
  38. Yuce B.E. Peter Vilhelm Nielsen P.V. Pawel Wargocki P. The use of Taguchi, ANOVA, and GRA methods to optimize CFD analyses of ventilation performance in buildings Build. Environ. 2022 225 109587 10.1016/j.buildenv.2022.109587
  39. Sylajakumari P. Ramakrishnasamy R. Palaniappan G. Taguchi Grey Relational Analysis for Multi-Response Optimization of Wear in Co-Continuous Composite Materials 2018 11 1743 10.3390/ma11091743
  40. Canbolat A.S. Bademlioglu A.H. Arslanoglu N. Kaynakli O. Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods J. Clean. Prod. 2019 229 874 885 10.1016/j.jclepro.2019.05.020
  41. Fedai Y. Optimization of Drilling Parameters in Drilling of MWCNT-Reinforced GFRP Nanocomposites Using Fuzzy AHP-Weighted Taguchi-Based MCDM Methods Processes 2023 11 2872 10.3390/pr11102872
  42. Vaddi V.R. Sridhar Reddy C. Pogaku V.K. Bushaboina S.K. Optimization of Electrical Discharge Machining of Titanium Alloy (Ti-6Al-4 V) Using Taguchi-DEAR Method SAE Technical Paper 2018-28-0032 SAE Warrendale, PA, USA 2018
  43. Vennela V.K. Lakshmi V.V.K. Subbaiah K.V. Kothapalli A.V. Suresh K. Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method Manuf. Rev. 2020 7 38
  44. Huang W.-T. Tasi Z.-Y. Ho W.-H. Chou J.-H. Integrating Taguchi Method and Gray Relational Analysis for Auto Locks by Using Multiobjective Design in Computer-Aided Engineering Polymers 2022 14 644 10.3390/polym14030644
  45. Abellán-Nebot J.V. Vila Pastor C. Siller H.R. A Review of the Factors Influencing Surface Roughness in Machining and Their Impact on Sustainability Sustainability 2024 16 1917 10.3390/su16051917
  46. Zhujani F. Abdullahu F. Todorov G. Kamberov K. Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance Metals 2024 14 186 10.3390/met14020186
  47. Rajguru R. Hari Vasudevan H. Deshpande N. Multi-criteria Optimization Using Taguchi and Grey Relational Analysis in CNC Drilling of GFRP/E Composite Material Proceedings of the 2nd International Conference on Advanced Manufacturing and Automation (INCAMA-2013) Krishnan Kovil, India 28–30 March 2013

Issue

Processes, vol. 12, 2025, Albania, https://doi.org/10.3390/pr12061212

Цитирания (Citation/s):
1. Chen C.-S., Pan P.-Y., Applied Internet of Things to Analyze Vibration, Workpiece Roughness, and Tool Wear: Case Study of Successive Milling, 2025, Processes, issue 4, vol. 13, DOI 10.3390/pr13040978, eissn 22279717 - 2025 - в издания, индексирани в Scopus и/или Web of Science
2. Kannan V., Mokshajna K., A Hybrid Algorithm for Optimizing Machining and Spraying Parameters in MQL-Turning of Inconel 800H, 2025, SAE Technical Papers, issue 0, DOI 10.4271/2025-28-0042, issn 01487191, eissn 26883627 - 2025 - в издания, индексирани в Scopus
3. Sandhu, N, Singh, R, Singh, N, Analysis of surface roughness and cutting force in WS2-Assisted MQL turning of H13 steel, WORLD JOURNAL OF ENGINEERING, 2025, issn: 1708-5284, doi: 10.1108/WJE-02-2025-0105 - 2025 - в издания, индексирани в Scopus и/или Web of Science
4. Sayyar N., Tucho W.M., Undheim E., Hansen V., Adaptive Control of Additive Manufacturing Parameters in Pulsed Laser-Based Directed Energy Deposition of Super Duplex Stainless Steel: Hybrid Taguchi-Grey Relational Approach, 2025, Metals and Materials International, issue 0, DOI 10.1007/s12540-025-02003-9, issn 15989623, eissn 20054149 - 2025 - в издания, индексирани в Scopus и/или Web of Science
5. Yadav G.P.K., Muvvala P., Reddy R.M., Optimization of injection parameters and low-reactive fuel share in CRDI engines with biodiesel-diesel-isoamyl alcohol ternary blends, 2025, Journal of Renewable and Sustainable Energy, issue 4, vol. 17, DOI 10.1063/5.0278062, eissn 19417012 - 2025 - в издания, индексирани в Scopus и/или Web of Science
6. Yilmaz F., Guvenc M.A., Mistikoglu S., Taguchi–Gray-Based Optimization of Hot Rough Rolling: Energy Efficiency and Dimensional Accuracy, 2025, Steel Research International, issue 0, DOI 10.1002/srin.202500623, issn 16113683, eissn 1869344X - 2025 - в издания, индексирани в Scopus и/или Web of Science
7. Sahbi M.O., Abdelhamid S., Yallese M.A., Belhadi S., Belamri A., Multi-Objective Optimization in Hard Turning of AISI 4140 Steel Using Taguchi-Based GRA and DEAR with Ceramic Tool, 2025, Tribology in Industry, issue 2, vol. 47, pp. 228-249, DOI 10.24874/ti.1842.12.24.03, issn 03548996 - 2025 - в издания, индексирани в Scopus
8. Showdho S.B., Mabey C.S., A SYSTEMATIC REVIEW OF SUSTAINABLE PRODUCT DESIGN USING MULTI-OBJECTIVE OPTIMIZATION AND MULTI-CRITERIA DECISION-MAKING METHODS, 2025, Proceedings of the ASME Design Engineering Technical Conference, issue 0, vol. 4, DOI 10.1115/DETC2025-168470 - 2025 - в издания, индексирани в Scopus
9. Yadav, GPK, Muvvala, P, Reddy, RM, Multi-objective optimization of injection timing, pressure, and alcohol share in CRDI engines and comparative analysis of ethanol and isoamyl alcohol blends using the Taguchi GRA method, ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY, 2025, issn: 1944-7442, eissn: 1944-7450, doi: 10.1002/ep.70272 - 2025 - в издания, индексирани в Scopus и/или Web of Science
10. Kaoud E., Heshmat M., Alenany E., Abdelrhman Y., Multi-objective optimization of slurry impact parameters to improve surface roughness in MEX-3D printed PLA using WGRA-Taguchi approach, 2025, Rapid Prototyping Journal, issue 0, pp. 1-12, DOI 10.1108/RPJ-11-2025-0566, issn 13552546 - 2025 - в издания, индексирани в Scopus и/или Web of Science
11. Shi J., He Y., Towards a Comprehensive Decision Framework With Linguistic Neutrosophic Data: GRA-Based Combined Approach to Technology-Enabled General High School Education Quality Evaluation, 2026, International Journal of Information System Modeling and Design, issue 1, vol. 17, DOI 10.4018/IJISMD.399502, issn 19478186, eissn 19478194 - 2026 - в издания, индексирани в Scopus и/или Web of Science
12. Kaoud E., Heshmat M., Hybrid artificial neural network and genetic algorithm-based grey relational analysis for bi-objective optimization of 3D-printed polylactic acid parts, 2026, Progress in Additive Manufacturing, issue 0, DOI 10.1007/s40964-025-01524-4, issn 23639512, eissn 23639520 - 2026 - в издания, индексирани в Scopus и/или Web of Science
13. Zeqiri, F, Zhujani, F, Optimization of multi-response characteristics in CNC turning of Inconel 625 using Taguchi-grey relational analysis, weighted grey relational analysis, and analysis of variance methods, ADVANCES IN SCIENCE AND TECHNOLOGY-RESEARCH JOURNAL, vol 20, 2026, issn: 2080-4075, eissn: 2299-8624, doi: 10.12913/22998624/215192 - 2026 - в издания, индексирани в Scopus и/или Web of Science
14. Zaib A., Tayyab H., Multi-Criteria Decision Analysis for Sustainable Design and Performance Optimization of Mechanical Engineering Systems, 2026, Reports in Mechanical Engineering, issue 1, vol. 7, pp. 241-252, DOI 10.31181/rme583, eissn 26835894 - 2026 - в издания, индексирани в Scopus
15. Chen, WX, Wang, LY, A Behavioral and Relational Integrated Decision-Making Method Using 2-Tuple Linguistic Neutrosophic Sets: College Music Majors Teaching Quality Evaluation, INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE, vol 20, 2026, issn: 1557-3958, eissn: 1557-3966, art_no: ARTN 405196, doi: 10.4018/IJCINI.405196 - 2026 - в издания, индексирани в Scopus и/или Web of Science
16. Suriadi I.G.A.K., Negara D.N.K.P., Nindia T.G.T., Atmika I.K.A., Dwijana I.G.K., Karohika I.M.G., Komaladewi A.A.I.A.S., Prasetiyo G., Taguchi-based optimization of activation parameters in activated carbon production for water pollutant adsorption, 2026, Sinergi Indonesia, issue 2, vol. 30, pp. 469-480, DOI 10.22441/sinergi.2026.2.013, issn 14102331, eissn 24601217 - 2026 - в издания, индексирани в Scopus
17. Ozcelik O., Selvi I.H., MCDM-Based Sustainability Evaluation in the Textile Industry, 2026, Sakarya University Journal of Computer and Information Sciences, issue 2, vol. 9, pp. 349-363, DOI 10.35377/saucis…1722432, eissn 26368129 - 2026 - в издания, индексирани в Scopus
18. Ithipri, E, Ashiedu, FI, Emovon, I, Samuel, OD, Chandrashekarappa, MPG, Chandran, D, Chate, GR, A Hybrid Momentum-Based Optimization and Gaussian Process Regression Modeling Framework with MEREC-CR Weighting for Sustainable Turning Operations, MODELLING, vol 7, 2026, eissn: 2673-3951, art_no: ARTN 169, doi: 10.3390/modelling7040169 - 2026 - в издания, индексирани в Scopus и/или Web of Science
19. Hasanzadeh R., Crashworthiness-driven infill optimization of additively manufactured wood-PLA biocomposite structures through robust multi-criteria decision-making framework with sensitivity analysis, 2026, Composites Part C Open Access, issue 0, vol. 21, DOI 10.1016/j.jcomc.2026.100827, eissn 26666820 - 2026 - в издания, индексирани в Scopus
20. Ganesha, BB, Kumar, KNA, Sachidananda, HK, Evaluation and optimization of process parameters on quality features of Al 7075 metal matrix composite during drilling with HSS tool, DISCOVER APPLIED SCIENCES, vol 8, 2026, eissn: 3004-9261, art_no: ARTN 1215, doi: 10.1007/s42452-026-09239-z - 2026 - в издания, индексирани в Scopus и/или Web of Science

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science