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Optimization of machining parameters in turning to steel using grey relational analysis
Authors:
Wisam Naji Hasan1
, Dhyai H. Jawad Aljashaami1
1Department of Automobile Engineering, Al-Musayyib College of Engineering, University of Babylon, Hilla, Al-Musayab, Babylon, Iraq
Received: 27 April 2025
Revised: 11 June 2025
Accepted: 19 June 2025
Published: 30 June 2025
Abstract:
The present study investigated the multi-response optimization of turning using nanofluids as coolants to determine the best parametric combination for surface roughness, flank wear, and material removal rate (MRR) by employing the Taguchi method and Grey relational analysis. Eighteen experimental runs were carried out using an orthogonal array of the Taguchi method within the defined experimental domain to derive and optimize the goal functions. The selected objective functions related to the turning process parameters included the volume fraction of nanoparticles (0.04%, 0.08%), cutting speed (110, 170, and 230 m/min), feed rate (0.125, 0.15, and 0.175 mm/rev), type of nanoparticles (MoS2, multi-walled carbon nanotubes (MWCNT), and SiO2), and depth of cut (0.3, 0.6, and 0.9 mm). The multi-response optimization problem was addressed using the Taguchi approach in conjunction with Grey relational analysis. The significance of the factors affecting the overall quality characteristics in the Minimum Quantity Lubrication (MQL) turning of AISI 4340 with nanofluid was quantitatively evaluated through Signal-to-Noise ratio (S/N) analysis and Analysis of Variance (ANOVA) to determine the contribution of each parameter to performance outcomes. The cutting speed was identified as the most significant parameter. Verification experiments were conducted to validate the optimal results. These findings demonstrated the effectiveness of the Taguchi technique and Grey relational analysis in continuously improving product quality in the manufacturing sector.
Keywords:
Turning process, Nanofluid, Material removal rate, Grey relation analysis, Taguchi method
References:
[1] M. Sayuti, O.M. Erh, A.A.D. Sarhan, M. Hamdi, Investigation on the morphology of the machined surface in end milling of aerospace AL6061-T6 for novel uses of SiO₂ nanolubrication system. Journal of Cleaner Production, 66, 2014: 655–663. https://doi.org/10.1016/j.jclepro.2013.11.058
[2] A.K. Sharma, A.K. Tiwari, A.R. Dixit, R.K. Singh, Investigation into performance of SiO₂ nanoparticle based cutting fluid in machining process. Materials Today: Proceedings, 4(2), 2017: 133–141.
https://doi.org/10.1016/j.matpr.2017.01.006
[3] D.G. Subhedar, B.M. Ramani, A. Gupta, Experimental investigation of heat transfer potential of Al₂O₃/water-mono ethylene glycol nanofluids as a car radiator coolant. Case Studies in Thermal Engineering, 11, 2018: 26–34. https://doi.org/10.1016/j.csite.2017.11.009
[4] F. Puh, Z. Jurković, M. Perinić, M. Brezočnik, S. Buljan, Optimization of machining parameters for turning operation with multiple quality characteristics using grey relational analysis. Tehnički Vjesnik, 23(2), 2016: 377–382. https://doi.org/10.17559/TV-20150526131717
[5] M. Nawaz, U. Nazir, An enhancement in thermal performance of partially ionized fluid due to hybrid nano-structures exposed to magnetic field. AIP Advances, 9(8), 2019: 085024.
https://doi.org/10.1063/1.5120455
[6] L.B. Abhang, M. Hameedullah, Optimization of machining parameters in steel turning operation by Taguchi method. Procedia Engineering, 38, 2012: 40–48. https://doi.org/10.1016/j.proeng.2012.06.007
[7] E. Nas, N.A. Özbek, Optimization of the machining parameters in turning of hardened hot work tool steel using cryogenically treated tools. Surface Review and Letters, 27(5), 2019: 1950177.
https://doi.org/10.1142/S0218625X19501774
[8] S. Roy, A. Ghosh, High speed turning of AISI 4140 steel using nanofluid through twin jet SQL system. Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference, Vol.2, 10–14 June 2013, Madison, Wisconsin, USA, V002T04A002. https://doi.org/10.1115/MSEC2013-1067
[9] S.K. Nayak, J.K. Patro, S. Dewangan, S. Gangopadhyay, Multi-objective optimization of machining parameters during dry turning of AISI 304 austenitic stainless steel using grey relational analysis. Procedia Materials Science, 6, 2014: 701–708. https://doi.org/10.1016/j.mspro.2014.07.086
[10] M.S. Surya, M. Shalini, A. Sridhar, Multi-Response Optimization on EN19 Steel Using Grey Relational Analysis Through Dry & Wet Machining. Materials Today: Proceedings, 4(2), 2017: 2157–2166.
https://doi.org/10.1016/j.matpr.2017.02.062
[11] N.M.I. Elsiti, M.H.S. Elmunafi, Optimization of machining parameters for turning process by using grey relational analysis. World Journal of Advanced Research and Reviews, 17(1), 2023: 755-761.
https://doi.org/10.30574/wjarr.2023.17.1.0080
[12] P. Jadhav, S. Kumar, A. Bongale, Optimization of cutting forces by cryogenic treatment on tungsten carbide inserts during dry turning of the P 20 tool steel. Materials Today: Proceedings, 28, 2020: 2485–2493. https://doi.org/10.1016/j.matpr.2020.04.798
[13] M.S. Surya, Optimization of machining parameters while turning AISI316 stainless steel using response surface methodology. Scientific Reports, 14, 2024: 30083. https://doi.org/10.1038/s41598-024-78657-z
[14] M. Mia, N.R. Dhar, Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method. The International Journal of Advanced Manufacturing Technology, 88, 2017: 739–753. https://doi.org/10.1007/s00170-016-8810-2
[15] I. Ramu, P. Srinivas, K. Venkatesh, Taguchi based grey relational analysis for optimization of machining parameters of CNC turning steel 316. IOP Conference Series: Materials Science and Engineering, 377, 2018: 012078. https://doi.org/10.1088/1757-899X/377/1/012078
[16] S.S. Mulugundam, G. Prasanthi, A.K. Kumar, V.K. Sridhar, S.K. Gugulothu, Optimization of cutting parameters while turning Ti-6Al-4V using response surface methodology and machine learning technique. International Journal on Interactive Design and Manufacturing, 15(3), 2021: 453–462.
https://doi.org/10.1007/s12008-021-00774-0
[17] M. Fnides, S. Amroune, B. Mohamad, B. Fnides, B. Toufik, Optimization of surface roughness and MRR when milling 25CrMo4-25 steel using Taguchi design. Academic Journal of Manufacturing Engineering, 22(4), 2024: 118-125.
[18] C.Y. Chang, R. Huang, P.C. Lee, T.L. Weng, Application of a weighted Grey-Taguchi method for optimizing recycled aggregate concrete mixtures. Cement and Concrete Composites, 33(10), 2011: 1038–1049. https://doi.org/10.1016/j.cemconcomp.2011.06.005
[19] H. Ha, S. Müller, R.-P. Baumann, B. Hwang, Peakforce quantitative nanomechanical mapping for surface energy characterization on the nanoscale: A mini-review. Facta Universitatis, Series: Mechanical Engineering,
21(1), 2024: 001–012. https://doi.org/10.22190/FUME221126001H
[20] H. Makri, C. Saib, S. Amroune, S. Zergane, B. Mohamad, M. Benarioua, K. Necib, Elaboration and characterization of Cu–Zn–Al shape memory alloys. Pollack Periodica, 20(1), 2025: 117–124.
https://doi.org/10.1556/606.2024.01122
© 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 2
June 2025
Last Edition
Volume 10
Number 2
June 2025
How to Cite
W.N. Hasan, D.H.J. Aljashaami, Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis. Applied Engineering Letters, 10(2), 2025: 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
More Citation Formats
Hasan, W.N., & Aljashaami, D.H.J. (2025). Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis. Applied Engineering Letters, 10(2), 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
Naji Hasan, Wisam and Dhyai H. Jawad Aljashaami. “Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis.“ Applied Engineering Letters, vol. 10, no. 2, 2025, pp. 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
Naji Hasan, Wisam and Dhyai H. Jawad Aljashaami. “2025. Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis.“ Applied Engineering Letters, 10 (2): 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
Hasan, W.N., and Aljashaami, D.H.J. (2025). Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis. Applied Engineering Letters, 10(2), pp. 90-99.
doi: 10.46793/aeletters.2025.10.2.3.
Using lean manufacturing to improve process efficiency in a fabrication company
Authors:
Andra Maria Popa1
, Kapil Gupta1
1University of Johannesburg, Mechanical and Industrial Engineering Technology, Johannesburg, South Africa
Received: 29 June 2024
Revised: 20 September 2024
Accepted: 26 September 2024
Published: 30 September 2024
Abstract:
This article presents a case study on improving process efficiency in a mining equipment part fabrication company. The company was facing issues concerning communication, organisation, and workflow processes. This study investigated that ineffective communication among departments was the major weakness which was responsible for the long lead or idle time. This lead time was a waste that affected the company’s productivity. A great amount of time was spent on non-value-added processes. The Kanban Centralised Communication System was implemented. Time study and value stream mapping were also used. A significant improvement in process efficiency from 34% to 85% was achieved by reducing lead time from 4200 minutes to 1680 minutes after streamlining the communication in the company using Kanban.
Keywords:
Lean manufacturing, Kanban, Optimization, Process efficiency, Production lead time, Value stream mapping
References:
[1] A. Belhadi, F.E. Touriki, S. Elfezazi, Evaluation of critical success factors (CSFs) to implement Lean implementation in SMES using AHP: A case study. International Journal of Lean Six Sigma, 10(3), 2019: 803-829. https://doi.org/10.1108/IJLSS-12-2016-0078
[2] K.S. Minh, S. Zailani, M. Iranmanesh, S. Heidari, Do lean manufacturing practices have a negative impact on job satisfaction. International Journal of Lean Six Sigma, 10(1), 2019: 257-274. https://doi.org/10.1108/IJLSS-11-2016-0072
[3] K. Das, M. Dixon, Lean manufacturing and service. CRC Press, Boca Raton, 2024. https://doi.org/10.1201/9781003121688
[4] S. Gupta, P. Chanda, A case study concerning the 5S Lean technique in a scientific equipment manufacturing company. Grey Systems: Theory and Application, 10(3), 2020:339-357. https://doi.org/10.1108/GS-01-2020-0004
[5] J.P. Davim, Progress in Lean Manufacturing. Springer Cham, 2018. https://doi.org/10.1007/978-3-319-73648-8
[6] L. Dubey, K. Gupta, Lean manufacturing based space utilization and motion waste reduction for efficiency enhancement in a machining shop: A case study. Applied Engineering Letters, 8(3), 2023: 121-130. https://doi.org/10.18485/aeletters.2023.8.3.4
[7] Y. Shi, X. Wang, X. Zhu, Lean manufacturing and productivity changes: the moderating role of R&D. International Journal of Productivity and Performance Management, 69(1), 2019:169-191. https://doi.org/10.1108/IJPPM-03-2018-0117
[8] S. Sahoo, S. Yadav, Lean implementation in small- and medium-sized enterprise. Benchmarking: An International Journal, 25(4), 2018: 1121-1147. https://doi.org/10.1108/BIJ-02-2017-0033
[9] S. Caceres-Gelvez, M.D. Arango-Serna, J.A. Zapata-Cortes, Evaluating the performance of a cellular manufacturing system proposal for sewing department of a sportswear manufacturing company: A simulation approach. Journal of Applied Research and Technology, 20(1), 2022: 68-83. https://doi.org/10.22201/icat.24486736e.2022.20.1.1335
[10] H.H. Berhe, Application of Kaizen philosophy for enhancing manufacturing industries’ performance: exploratory study of Ethiopian chemical industries. International Journal of Quality & Reliability Management, 39(1),2022: 204-235. https://doi.org/10.1108/IJQRM-09-2020-0328
[11] C. Hemalatha, K. Sankaranarayanasamy, N. Durairaaj, Lean and agile manufacturing for work-in-process (WIP) control. Materials Today Proceedings, 46(20), 2021: 10334-10338. https://doi.org/10.1016/j.matpr.2020.12.473
[12] J. Singh, H. Singh, A. Singh, J. Singh, Managing industrial operations by Lean thinking using value stream mapping and six sigma in manufacturing unit. Management Decision, 58(6), 2019: 1118-1148. https://doi.org/10.1108/MD-04-2017-0332
[13] C. Veres, L. Marian, M.S. Moica, K. Al-Akel, Case study concerning 5S method impact in an automotive company. Procedia Manufacturing, 22, 2018: 900-905. https://doi.org/10.1016/j.promfg.2018.03.127
[14] J.C-C. Chen, C.-Y. Cheng, Solving social loafing phenomenon through Lean-Kanban: A case study in non-profit organization. Journal of Organizational Change Management, 31(5), 2017: 984-1000. https://doi.org/10.1108/JOCM-12-2016-0299
[15] T. Bandoophanit, S. Pumprasert, The paradoxes of just-in-time system: an abductive analysis of a public food manufacturing and exporting company in Thailand. Management Research Review, 45(8), 2022: 1019-1043 https://doi.org/10.1108/MRR-04-2021-0262
[16] S. Gawande, J.S. Karajgikar, Implementation of Kanban, a Lean tool, In Switchgear Manufacturing Industry – A Case Study. Proceedings of the International Conference on Industrial Engineering and Operations Management, July 26-27, 2018, Paris, France, 2335-2348.
[17] M.A. Habib, R. Rizvan, S. Ahmed, Implementing Lean manufacturing for improvement of operational performance in a labeling and packaging plant: A case study in Bangladesh. Results in Engineering, 17, 2023:100818. https://doi.org/10.1016/j.rineng.2022.100818
[18] A.K. Das, M.C. Das, Productivity improvement using different Lean approaches in small and medium enterprises (SMEs). Management Science Letters, 13, 2023: 51-64. https://doi.org/10.5267/j.msl.2022.9.002
[19] P.A. Marques, D. Jorge, J. Reis, Using Lean to Improve Operational Performance in a Retail Store and E-Commerce Service: A Portuguese Case Study. Sustainability, 14(10), 2022: 5913. https://doi.org/10.3390/su14105913
[20] F. Khair, M. A. S. Putra, I. Rizkia, Improvement and analysis of aircraft maintenance flow process using Lean manufacturing, PDCA, PICA, and VSM for sustainable operation system. IOP Conf. Series: Earth and Environmental Science, 1324, 2024: 012071. https://doi.org/10.1088/17551315/1324/1/012071
[21] I. Rizkya, K. Syahputri, R.M. Sari, D.S. Situmorang, Lean Manufacturing: Waste Analysis in Crude Palm Oil Process. IOP Conference Series: Materials Science and Engineering, 851, 2020: 012058. https://doi.org/10.1088/1757-899X/851/1/012058
[22] A. Pradeep, K. Balaji, Reduction of lead time in an automobile rubber component manufacturing process through value stream mapping. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 236(6), 2022:2470-2479. https://doi.org/10.1177/09544089221094094
[23] D. Cabezas, I. Muelle, E. Avalos-Ortecho, Implementation of Lean Manufacturing to Increase the Machine’s Availability of a Metalworking Company. 7 th North American International Conference on Industrial Engineering and Operations Management, June 12-14, 2022, Orlando, Florida, USA.
[24] W. Kosasih, I.K. Sriwana, E.C. Sari, C.O. Doaly, Applying value stream mapping tools and kanban system for waste identification and reduction (case study: a basic chemical company). IOP Conference Series: Materials Science and Engineering, 528, 2019: 012050. https://doi.org/10.1088/1757-899X/528/1/012050
[25] B.S. Patel, M. Sambasivan, R. Panimalar, R. Krishna, A relationship analysis of drivers and barriers of Lean manufacturing. The TQM Journal, 34(5), 2022: 845-876. https://doi.org/10.1108/TQM-12-2020-0296
© 2024 by the author. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 2
June 2025
Last Edition
Volume 10
Number 2
June 2025
How to Cite
V.H. Quan, Research and Optimization of Sport Utility Vehicle Aerodynamic Design. Applied Engineering Letters, 9(2), 2024: 105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
More Citation Formats
Quan, V.H. (2024). Research and Optimization of Sport Utility Vehicle Aerodynamic Design. Applied Engineering Letters, 9(2), 105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
Quan, Vu Hai, “Research and Optimization of Sport Utility Vehicle Aerodynamic Design.“ Applied Engineering Letters, vol. 9, no. 2, pp. 2024, 105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
Quan, Vu Hai, 2024. “Research and Optimization of Sport Utility Vehicle Aerodynamic Design.“ Applied Engineering Letters, 9 (2):105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
Quan, V.H. (2024). Research and Optimization of Sport Utility Vehicle Aerodynamic Design. Applied Engineering Letters, 9(2), pp. 105-115.
doi: 10.46793/aeletters.2024.9.2.5.