2022
DOI: 10.1016/j.matpr.2022.05.361
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Taguchi-based grey relational analysis of abrasive water jet machining of Al-6061

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Cited by 6 publications
(1 citation statement)
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“…Radomska-Zalas et al [ 92 ] investigated the abrasive water jet cutting of aluminum using IT-supported TOPSIS method, optimized the process parameters (transverse speed, pressure, and abrasive flow rate) with width of kerf and one of the surface roughness parameters (quality) as the optimization objective levels, and obtained the optimum combination of the process parameters, i.e., transverse speed of 0.75 mm/s, pressure of 250 MPa, abrasive flow rate of 1 g/s, width of kerf 0.75 mm, and surface roughness of 14.49 mm. Akhai et al [ 93 ] used Taguchi’s gray relational (TGRA) analysis method in the processing of Al-6061 aluminum alloy by abrasive water jets to optimize the process parameters, with surface roughness, material removal rate, and edge width as the target-level characteristics for the process parameters (travel speed, spacing distance, and abrasive mass flow) to be optimized and obtained the optimum combination of process parameters travel speed of 100 mm/min, spacing distance of 1 mm, and abrasive mass flow of 300 g/min, indicating that the material removal rate was inversely proportional to surface roughness and edge width. Krenicky et al [ 94 ] used a modified photographic method to optimize the cutting process parameters (abrasive mass flow, pump pressure, and travel speed) for abrasive water jet cutting of wear-resistant steel using surface roughness and abrasive water jet deflection as the target level and obtained the optimum combination of process parameters with an abrasive mass flow rate of 270 g/min, pump pressure of 380 MPa, and travel speed of 10 mm/min.…”
Section: Influencing Factors In the Processing Removal Process Of Abr...mentioning
confidence: 99%
“…Radomska-Zalas et al [ 92 ] investigated the abrasive water jet cutting of aluminum using IT-supported TOPSIS method, optimized the process parameters (transverse speed, pressure, and abrasive flow rate) with width of kerf and one of the surface roughness parameters (quality) as the optimization objective levels, and obtained the optimum combination of the process parameters, i.e., transverse speed of 0.75 mm/s, pressure of 250 MPa, abrasive flow rate of 1 g/s, width of kerf 0.75 mm, and surface roughness of 14.49 mm. Akhai et al [ 93 ] used Taguchi’s gray relational (TGRA) analysis method in the processing of Al-6061 aluminum alloy by abrasive water jets to optimize the process parameters, with surface roughness, material removal rate, and edge width as the target-level characteristics for the process parameters (travel speed, spacing distance, and abrasive mass flow) to be optimized and obtained the optimum combination of process parameters travel speed of 100 mm/min, spacing distance of 1 mm, and abrasive mass flow of 300 g/min, indicating that the material removal rate was inversely proportional to surface roughness and edge width. Krenicky et al [ 94 ] used a modified photographic method to optimize the cutting process parameters (abrasive mass flow, pump pressure, and travel speed) for abrasive water jet cutting of wear-resistant steel using surface roughness and abrasive water jet deflection as the target level and obtained the optimum combination of process parameters with an abrasive mass flow rate of 270 g/min, pump pressure of 380 MPa, and travel speed of 10 mm/min.…”
Section: Influencing Factors In the Processing Removal Process Of Abr...mentioning
confidence: 99%