Journal Article•
Plasma arc cutting takes a slice at competition
About: This article is published in Welding and metal fabrication.The article was published on 1997-01-01 and is currently open access. It has received 7 citations till now. The article focuses on the topics: Plasma cutting.
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TL;DR: In this article, the effects and parametric optimization of process parameters for plasma arc cutting (PAC) of EN31 steel using grey relation analysis was investigated. But, it was not shown that the grey-Taguchi technique can be used as a multi-objective optimizer in the field of PAC.
39 citations
TL;DR: In this paper, the authors explored the characteristics of the HMC arc and the effects of secondary confinement on cutting quality, arc properties, and optimal process parameters, and achieved better cutting quality and higher cutting speeds.
32 citations
TL;DR: In this paper, fuzzy logic was used for the prediction of cutting parameters in the PAC process of AISI 4140 steel, including plasma arc current, cutting speed, and thickness of cut material.
Abstract: In the present study, fuzzy logic (a tool in artificial intelligence) was used for the prediction of cutting parameters in plasma arc cutting (PAC) process of AISI 4140 steel. The parameters considered in this study were plasma arc current, cutting speed, and thickness of cut material. Fuzzy rule–based modeling was employed for prediction of surface roughness. These models can be effectively used to estimate the surface roughness. The experimental results were compared with fuzzy logic, and good agreement between them was observed. Analysis of the influence of the individual important machining parameters on the surface roughness have been carried out and presented. Statistically, cutting speed was found the most important factor on surface roughness, while the plasma arc current had the least. The characteristics of machined surfaces and microstructural changes are also studied.
32 citations
Cites background from "Plasma arc cutting takes a slice at..."
...Amongst other thermal machining methods, plasma arc cutting (PAC) is a very important thermal cutting process and has been used succesfully in the cutting of stainless steel, high hardness metals, high melting point metals, and other difficult to machine alloys [1, 2]....
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Journal Article•
21 Feb 2012-World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering
TL;DR: In this paper, the surface roughness analysis was conducted using Mitutoyo CS-3100 to determine the average roughness value (Ra), and the Taguchi method was utilized to achieve optimum condition for both outputs studied.
Abstract: Industrial surveys shows that manufacturing companies define the qualities of thermal removing process based on the dimension and physical appearance of the cuttin g material surface. Therefore, the roughness of the surface ar ea of the material cut by the plasma arc cutting process and the rate of the removed material by the manual plasma arc cutting machine was importantly considered. Plasma arc cutter Selco Genesis 90 was used to cut Standard AISI 1017 Steel of 200 mm x100 mm x 6 mm manually based on the selected parameters setting. The mater ial removal rate (MRR) was measured by determining the weight of the specimens before and after the cutting process. The surface r oughness (SR) analysis was conducted using Mitutoyo CS-3100 to determine the average roughness value (Ra). Taguchi method was utilized to achieve optimum condition for both outputs studied. The microstructure analysis in the region of the cuttin g surface is performed using SEM. The results reveal that the SR values are inversely proportional to the MRR values. The quali ty of the surface roughness depends on the dross peak that occurred a fter the cutting process.
20 citations
TL;DR: In this article, an attempt has been made to optimize the process parameters for multi-responses (material removal rate, MRR and surface roughness) in plasma arc cutting (PAC) of EN 31 steel using weighted principal component analysis (WPCA).
18 citations