Optimal cutting conditions of abrasive waterjet cutting for Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy using the Aquila algorithm method

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Published: Oct 2, 2025
DOI: https://doi.org/10.69598/sehs.19.25040001
Keywords:
optimization efficiency waterjet machining parameters

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Ugochukwu Sixtus Nwankiti
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria
Adeyinka Oluwo
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria
Bayo Yemisi Ogunmola
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria
John Rajan
Department of Manufacturing Engineering, Vellore Institute of Technology, Chennai, India
Swaminathan Jose
School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India
Sunday Ayoola Oke
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria. Corresponding author's e-mail: sa_oke@yahoo.com
Boluvar Lathashankar
Department of Industrial Engineering and Management, Siddaganga Institute of Technology, Karnataka, India
Ayomide Sunday Ibitoye
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria

Abstract

This article introduces a novel method, namely the Aquila algorithm for optimizing the abrasive waterjet cutting process for machining Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy. The main parameters of the process are the waterjet pressure (WJP), traverse speed (TS) and the stand-off distance (SOD), while material removal rate (MRR) and surface roughness (Ra) are its responses. The Aquila algorithm optimizes the process parameters, thereby declaring the optimal thresholds for improved efficiency. Unlike previous studies, this work accounts for the optimization of the abrasive waterjet cutting parameters, providing direction on how much of each parameter to utilize for optimal performance of the system. Experimental data from the published literature was used to validate the proposed model. After performing the analysis, the optimal parameters at the convergence of the results after 300 iterations were WJP, TS, SOD, and MRR of 260 bar, 40 mm/min, 3 mm, and 164.74 mm3/min, respectively. This is when the response considered is the material removal rate. Considering the surface roughness as the output, the optimal solutions for WJP, TS, SOD, and SR were 256.27 bar, 24.54 mm3/min, 1.00 mm, and 2.54 mm3/min, respectively. The outcomes will assist process engineers in using optimal results for efficient decision-making in the abrasive waterjet machining process for the Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy.

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How to Cite
Nwankiti, U. S., Oluwo, A., Ogunmola, B. Y., Rajan, J., Jose, S., Oke, S. A., Lathashankar, B., & Ibitoye, A. S. (2025). Optimal cutting conditions of abrasive waterjet cutting for Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy using the Aquila algorithm method. Science, Engineering and Health Studies, 19, 25040001. https://doi.org/10.69598/sehs.19.25040001
Issue
Volume 19, 2025
Section
Engineering
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness M. A. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, Article 107250. https://doi.org/10.1016/j.cie.2021.107250

Ay, M., Çaydaş, U., & Hasçalik, A. (2010). Effect of traverse speed on abrasive waterjet machining of age hardened Inconel 718 nickel-based superalloy. Materials and Manufacturing Processes, 25(10), 1160–1165. https://doi.org/10.1080/10426914.2010.502953

Boud, F., Murray, J. W., Loo, L. F., Clare, A. T., & Kinnell, P. K. (2014). Soluble abrasives for waterjet machining. Materials and Manufacturing Processes, 29(11–12), 1346–1352. https://doi.org/10.1080/10426914.2014.930949

Chakraborty, S., & Mitra, A. (2018). Parametric optimization of abrasive water-jet machining processes using grey wolf optimizer. Materials and Manufacturing Processes, 33(13),1471–1482. https://doi.org/10.1080/10426914.2018.1453158

Fan, C., Wang, W., & Tian, J. (2024). Flexible job shop scheduling with stochastic machine breakdowns by an improved tuna swarm optimization algorithm. Journal of Manufacturing Systems, 74, 180–197. https://doi.org/10.1016/j.jmsy.2024.03.002

Halteh, K., AlKhoury, R., Ziadat, S. A., Gepp, A., & Kumar, K. (2024). Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry. Transportation Research Interdisciplinary Perspectives, 24, Article 101043. https://doi.org/10.1016/j.trip.2024.101043

Hussain, A., Janjua, T. A. M., Malik, A. N., Najib, A., & Khan, S. A. (2024). Health monitoring of CNC machining processes using machine learning and wavelet packet transform. Mechanical Systems and Signal Processing, 212, Article 111326. https://doi.org/10.1016/j.ymssp.2024.111326

Ishfaq, K., Mufti, N. A., Ahmed, N., & Pervaiz, S. (2019). Abrasive waterjet cutting of cladded material: Kerf taper and MRR analysis. Materials and Manufacturing Processes, 34(5), 554–553. https://doi.org/10.1080/10426914.2018.1544710

Kalirasu, S., Rajini, N., Rajesh, S., Seingchin, S., & Ramaswamy, S. N. (2018). AWJ machinability performance of CS/UPR composites with the effect of chemical treatment. Materials and Manufacturing Processes, 33(4), 452–461. https://doi.org/10.1080/10426914.2017

Kant, R., & Dhami, S. S. (2021). Investigating process parameters of abrasive water jet machine using EN31. Materials and Manufacturing Processes, 36(14), 1597–1603. https://doi.org/10.1080/10426914.2021.1914849

Karakurt, I., Aydin, G., & Aydiner, K. (2012). An experimental study on the depth of cut of granite in abrasive waterjet cutting. Materials and Manufacturing Processes, 27(5), 538–544. https://doi.org/10.1080/10426914.2011.593231

Li, W., Zhu, H., Wang, J., & Huang, C. (2016). Radial-mode abrasive waterjet turning of short carbon-fibre-reinforced plastics. Machining Science and Technology, 20(2), 231–248. https://doi.org/10.1080/10910344.2016.1165836

Mezgebe, T. T., Gebreslassie, M. G., Sibhato, H., & Bahta, S. T. (2023). Intelligent manufacturing eco-system: A post COVID-19 recovery and growth opportunity for manufacturing industry in Sub-Saharan countries. Scientific African, 19, Article e01547. https://doi.org/10.1016/j.sciaf.2023.e01547

Nakandala, D. (2024). Impact of industry 4.0 technologies on operations resilience: Adverse effects of COVID-19 as a moderator. Procedia Computer Science, 232, 3258–3267. https://doi.org/10.1016/j.procs.2024.02.141

Naresh Babu, M., & Muthukrishnan, N. (2015). Investigation of multiple process parameters in abrasive water jet machining of tiles. Journal of the Chinese Institute of Engineers, 38(6), 692–700. https://doi.org/10.1080/02533839.2015.1010944

Nwankiti, U. S., & Oke, S. A. (2022). Optimal cutting conditions of abrasive waterjet cutting for Ti-6Al-2Sn-2Mo alpha-beta alloy using EDAS and DFA methods. Gazi University Journal of Science Part A: Engineering and Innovation, 9(3), 233–250. https://doi.org/10.54287/gujsa.1135609

Pancaldi, F., Rubini, R., & Cocconcelli, M. (2023). On the performance comparison of diagnostic techniques in machine monitoring. Mechanical Systems and Signal Processing, 205, Article 110872. https://doi.org/10.1016/j.ymssp.2023.110872

Perumal, A., Azhagurajan, A., Kumar, S. S., Kailasanathan, C., Rajan, R. P., Rajan, A. J., Venkatesan, G., & Rajkumar, P. R. (2020). Experimental investigation on surface morphology and parametric optimization of Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy through AWJM. Tierarztliche Praxis, 40, 1681–1702.

Sadasivam, B., & Arola, D. (2012). An examination of abrasive waterjet peening with elastic pre-stress and the effects of boundary conditions. Machining Science and Technology, 16(1), 71–95. https://doi.org/10.1080/10910344.2012.648565

Srinivas, S., & Ramesh Babu, N. (2012). Penetration ability of abrasive waterjets in cutting of aluminum-silicon carbide particulate metal matrix composites. Machining Science and Technology, 16(3), 337–354. https://doi.org/10.1080/10910344.2012.698935

Thakur, R. K., Singh, K. K., & Ramkumar, J. (2021). Impact of nanoclay filler reinforcement on CFRP composite performance during abrasive water jet machining. Materials and Manufacturing Processes, 36(11), 1264–1273. https://doi.org/10.1080/10426914.2021.1906896

Tripathi, D. R., Vachhani, K. H., Bandhu, D., Kumari, S., Kuma, V. R., & Abhishek, K. (2021). Experimental investigation and optimization of abrasive waterjet machining parameters for GFRP composites using metaphor-less algorithms. Materials and Manufacturing Processes, 36(7), 803–813. https://doi.org/10.1080/10426914.2020.1866193

Wang, J., Kuriyagawa, T., & Huang, C. Z. (2003). An experimental study to enhance the cutting performance in abrasive waterjet machining. Machining Science and Technology, 7(2), 191–207. https://doi.org/10.1081/MST-120022777

Wang, J., & Zhong, Y. (2009). Enhancing the depth of cut in abrasive waterjet cutting of alumina ceramics by using multipass cutting with nozzle oscillation. Machining Science and Technology, 13(1), 76–91. https://doi.org/10.1080/10910340902776085

Xu, Y., Qamsane, Y., Puchala, S., Januszczak, A., Tilbury, D. M., & Barton, K. (2024). A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines. Computers in Industry, 157–158, Article 104086. https://doi.org/10.1016/j.compind.2024.104086

Yuvaraj, N., & Kumar, M. P. (2017). Study and evaluation of abrasive water jet cutting performance on AA5083-H32 aluminum alloy by varying the jet impingement angles with different abrasive mesh sizes. Machining Science and Technology, 21(3), 385–415. https://doi.org/10.1080/10910344.2017.1283958

Zhang, S., & Li, X. (2010). Theoretical analysis of piercing delicate materials with abrasive water-jet. Journal of the Chinese Institute of Engineers, 33(7), 1015–1019. https://doi.org/10.1080/02533839.2010.9671690

Zhang, Y., Du, G., Li, H., Yang, Y., Zhang, H., Xu, X., & Gong, Y. (2024). Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing. Journal of Manufacturing Systems, 74, 222–232. https://doi.org/10.1016/j.jmsy.2024.03.006

Zhong, Z. W., & Han, Z. Z. (2002). Turning of glass with abrasive waterjet. Materials and Manufacturing Processes, 17(3), 339–349. https://doi.org/10.1081/AMP-120005380