2012
DOI: 10.1016/j.knosys.2011.10.002
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Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process

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Cited by 69 publications
(21 citation statements)
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“…Vundavilli, P. R., et al [105] deals with identifying the depth of cut in AWJ cutting technology using the fuzzy logic (FL) system. Water pressure, abrasive mass flow rate, the diameter of focusing nozzle and jet transverse speed were proposed as dependent parameters for the depth of cut.…”
Section: Mathematical Modeling Approachmentioning
confidence: 99%
“…Vundavilli, P. R., et al [105] deals with identifying the depth of cut in AWJ cutting technology using the fuzzy logic (FL) system. Water pressure, abrasive mass flow rate, the diameter of focusing nozzle and jet transverse speed were proposed as dependent parameters for the depth of cut.…”
Section: Mathematical Modeling Approachmentioning
confidence: 99%
“…For production industries and also for other civil and mining engineering fields this process is an assured tool due to its exceptional properties of inflexible cutting, and acceptable surface roughness, minor kerf widths, expanded tool life, trouble freeform cutting, flexible process, dust free, ergonomic working conditions, and environment. These highlights make the process an ecofriendly process compared to other conventional machining processes like sawing (circular) in raw stone cutting and figuring areas (Assarzadeh et al, 2010;Vundavilli et al, 2012;Myers et al, 2016). There are abundant parameters associated with this process. The connected parameters of AWH machining process are figured out in Fig.…”
Section: Introductionmentioning
confidence: 99%
“…Kong et al [27] developed a mathematical model to predict the jet foot prints for arbitrarily moving jets in single straight paths. Vundavilli et al [28] used fuzzy logic based expert system such as simulated annealing and genetic algorithm to optimize process parameters and develop a mathematical model to predict depth of cut. Zain et al [29] also used soft computing techniques to optimize the process parameters that produced low surface roughness.…”
Section: Introductionmentioning
confidence: 99%