Evolutionary optimization of jet lag in the abrasive water jet machining
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One of the main characteristics of abrasive water jet machining (AWJM) is jet lag effect. In this case, by controlling basic machining parameters (water pressure, cutting speed, and abrasive mass flow rate), evolutionary algorithm is used for modeling and optimization of trajectory curvature in AWJM process. Based on experimental investigation, genetic programming was used as the main exploration tool for generating function of the jet lag depending on the machining conditions and as a potential way in the AWJM simulation. Judging by the output, the genetic programming yielded impressive results and has proven to be very practical for modeling of the abrasive water jet trajectory curvature. In the sequel, the optimization of AWJM process is used approximate optimal control problem which leads to the best solution for the jet lag considering the machining parameters. With this action, we can say that different access has been made to find precise dependence of AWJM process parameters with the trajectory curvature to obtain high productivity and quality results.
KeywordsAWJM process Trajectory curvature Modeling Genetic programming Optimal control problem
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The paper is the result of the research within the project TR 35015 financed by the Ministry of Education, Science and Technological Development of the Republic of Serbia.
- 9.Sutowski P, Sutowska M, Kapłonek W (2017) The use of high-frequency acoustic emission analysis for in-process assessment of the surface quality of aluminium alloy 5251 in abrasive waterjet machining. Proc Inst Mech Eng B J Eng Manuf 232:2547–2565. https://doi.org/10.1177/0954405417703428 CrossRefGoogle Scholar
- 14.Gudimetla P, Yarlagadda PKDV (2007) Finite element analysis of the interaction between an AWJ particle and a polycrystalline alumina ceramic. J Achiev Mater Manuf Eng 23(1):7–14Google Scholar
- 18.Yang G (2013) Forecast surface quality of abrasive water jet cutting based on neural network. J Theor Appl Inform Tech 47(3):1087–1091Google Scholar
- 34.Zeng J, Kim TJ, Wallace RJ (1992) Quantitative evaluation of machinability in abrasive waterjet machining. ASME PED 58:169–179Google Scholar
- 41.Hloch S, Valicek J, Samardzic I, Kozak D, Mullerova J, Gombar M (2007) Experimental study of surface topography created by abrasive waterjet cutting. Strojarstvo 49(4):303–309Google Scholar
- 43.Hashish M (1992) On the modeling of surface waviness produced by abrasive-waterjets. Fluid Mec A 13:17–34Google Scholar
- 44.Simon D (2013) Evolutionary optimization algorithms. John Wiley & Sons, HobokenGoogle Scholar
- 46.Koza JR (1988) Genetic programming. MIT Press, LondonGoogle Scholar