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Metaheuristic-Based Parametric Optimization of Abrasive Water-Jet Machining Process—A Comparative Analysis

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Advances in Materials and Agile Manufacturing (CPIE 2023)

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Abstract

Hybrid machining (HM) processes are an effective means for increasing material removal rate, improving surface integrity, minimizing production time and tool wear. Determination of the optimal parametric settings is an important problem in hybrid machining process. The performance of these processes mainly depends on the optimal choice of its various input parameters which highly affect the responses, like material removal rate and surface roughness. In this paper, seven metaheuristic algorithms in the form of artificial bee colony (ABC), ant colony optimization (ACO), particle swarm optimization (ACO), firefly algorithm (FA), differential evolution (DE), teaching–learning-based optimization (TLBO), and elephant swarm water search algorithm (ESWSA) are employed to determine the optimal parametric settings of abrasive water-jet machining (AWJM) process, while satisfying their given sets of practical machining constraints. It is observed that ESWSA outperforms the others with respect to the derived optimal solution, consistency of the solution and convergence speed.

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References

  1. Saxena KK, Bellotti M, Qian J, Reynaerts D, Lauwers B, Luo X (2018) Overview of hybrid machining processes: hybrid machining-theory, methods, and case studies. Elsevier, USA

    Book  Google Scholar 

  2. Jain NK, Jain VK, Deb K (2007) Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms. Int J Mach Tools Manuf 47(6):900–919

    Article  Google Scholar 

  3. Pawar PJ, Rao RV (2013) Parameter optimization of machining processes using teaching-learning based optimization algorithm. Int J Adv Manuf Technol 67(5–8):995–1006

    Google Scholar 

  4. Kumar GK, Arunachalam M, Abinash B, Kumar BK (2018) Optimization of abrasive water jet machining process parameters for duplex stainless steel-2205 by using response surface methodology. Int J Sci Res Mech Mater Eng 2(3):17–28

    Google Scholar 

  5. Manoj M, **u GR, Muthuramalingam T (2018) Multi response optimization of AWJM process parameters on machining TiB2 particles reinforced Al7075 composite using Taguchi-DEAR methodology. SILICON 10(5):2287–2293

    Article  Google Scholar 

  6. Chakraborty S, Mitra A (2018) Parametric optimization of abrasive water-jet machining processes using grey wolf optimizer. Mater Manuf Processes 33(13):1471–1482

    Article  Google Scholar 

  7. Joel C, Joel L, Muthukumaran S, Shanthini PM (2021) Parametric optimization of abrasive water jet machining of C360 brass using MOTLBO. Mater Today Proc 37:1905–1910

    Article  Google Scholar 

  8. Jagadish, Patel GCM, Sibalija TV (2022) Abrasive water jet machining for a high-quality green composite: the soft computing strategy for modeling and optimization. J Braz Soc Mech Sci Eng 44:83

    Google Scholar 

  9. Bhoi NK, Singh H, Pratap S, Jain PK (2022) Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting. Opsearch 59(1):350–363

    Article  MATH  Google Scholar 

  10. Mandal S (2018) Elephant swarm water search algorithm for global optimization. Sādhanā 43(1):1–21

    Article  MathSciNet  MATH  Google Scholar 

  11. Khosravanian R, Mansouri V, Wood DA, Alipour MR (2018) A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. J Pet Explor Prod Technol 8(4):1487–1503

    Article  Google Scholar 

  12. Diyaley S, Chakraborty S (2019) Optimization of multi-pass face milling parameters using metaheuristic algorithms. Facta Universitatis Ser Mech Eng 17(3):365–383

    Article  Google Scholar 

  13. Yang XS (2009) Harmony search as a metaheuristic algorithm. Studies in Computational Intelligence, Springer, Berlin

    Google Scholar 

  14. Madić M, Marković D, Radovanović M (2013) Comparison of meta-heuristic algorithms for solving machining optimization problems. Facta Universitatis Ser Mech Eng 11(1):29–44

    Google Scholar 

  15. Jain NK, Jain VK, Jha S (2007) Parametric optimization of advanced fine-finishing processes. Int J Adv Manuf Technol 34(11–12):1191–1213

    Google Scholar 

  16. Hashish M (1989) A model for abrasive water jet (AWJ) machining. J Eng Mater Technol 111(2):154–162

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, 737136, India

    Sunny Diyaley & Partha Protim Das

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  1. Sunny Diyaley
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  2. Partha Protim Das
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Corresponding author

Correspondence to Sunny Diyaley .

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Editors and Affiliations

  1. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Narendra Kumar

  2. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Gurraj Singh

  3. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Rajiv Trehan

  4. Department of Mechanical Engineering, University of Aveiro, University Campus of Santiago, Aveiro, Portugal

    J. Paulo Davim

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Diyaley, S., Das, P.P. (2024). Metaheuristic-Based Parametric Optimization of Abrasive Water-Jet Machining Process—A Comparative Analysis. In: Kumar, N., Singh, G., Trehan, R., Davim, J.P. (eds) Advances in Materials and Agile Manufacturing. CPIE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6601-1_14

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  • DOI: https://doi.org/10.1007/978-981-99-6601-1_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6600-4

  • Online ISBN: 978-981-99-6601-1

  • eBook Packages: EngineeringEngineering (R0)

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