الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Plasma Air Cutting (PAC) is a non-conventional process which can carry out numerous electrically conducting materials. Air as a cutting gas is inexpensive gas in contradiction of other cutting gases. For this reason, air used as cutting gas for mild steel is currently in trend. On the opposing, oxygen is an active gas which drops the cutting quality of the cuts. The purpose of this work is to investigate the most important parameters that affect the cutting quality of air plasma arc cutting process.
In this work different work piece geometries were suggested to study the kerf characteristics in the straight-slit cutting and profile characteristics. Cutting speed, arc current, and standoff distance were nominated as setting parameters. The PAC experiments were completed using air as a cutting gas. The full factorial design approach was used for selection of the experimental plan. The cuts were completed on 4 mm thickness of St 37 plate.
Single response optimization of kerf characteristics (kerf taper, dross, surface roughness, and metal removal rate) and profile characteristics (hole taper, hole starting point and taper of the external curvature) were discussed and the optimum value of each response and its optimum arrangement of the cutting parameters has been strong-minded using analysis of means (ANOM), response table and main effects plot with the help of Minitab 16 Software.
Multi- response optimization of kerf characteristics was discussed. In this work, cutting parameters were all together optimized by the use of standard deviation (SDV) concept, multi- objective optimization by ratio analysis (MOORA) method and genetic algorithm (GA). SDV concept was used to assign the weight of each criterion being considered. MOORA was used to transform multiple responses into a single response index famous as Multi Performance characteristic Index (MPCI). MPCI was modeled (power law function) using GA. With this act an attempt was made to find more accurate dependence of MPCI with cutting parameters. Finally, this was tailed by optimization of the MPCI in PAC using GA. Mat Lab Optimization Toolbox was used. It is, therefore, concluded that the SDV-MOORA-GA method has successfully optimized the PAC process and the cutting speed is the greatest factor affecting MPCI then followed by arc current and standoff distance. High values arc current and high values of cutting speed at constant level of standoff distance favor high value of MPCI. At constant value of arc current it can be deduced that MPCI value is less in the region of low cutting speed then it increases at high values of cutting speed while all values of standoff distance favor high values of MPCI. At constant value of cutting speed with upper values of arc current and greater values of standoff distance favor upper values of MPCI.