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Identifying combination of friction stir welding parameters to maximize strength of lap joints of AA2014-T6 aluminum alloy

Rajendrana C., Srinivasan K., Balasubramanian V., Balaji H., Selvaraj P.

Archives of Mechanical Technology and Materials

2017

Vol. 37

6--21

AA2014 aluminum alloy (Al-Cu alloy) has been widely utilized in fabrication of lightweight structures like aircraft structures, demanding high strength to weight ratio and good corrosion resistance. The fusion welding of these alloys will lead to solidification problems such as hot cracking. Friction stir welding is a new solid state welding process, in which the material being welded does not melt and recast. Lot of research works have been carried out by many researchers to optimize process parameters and establish empirical relationships to predict tensile strength of friction stir welded butt joints of aluminum alloys. However, very few investigations have been carried out on friction stir welded lap joints of aluminum alloys. Hence, in this investigation, an attempt has been made to optimize friction stir lap welding (FSLW) parameters to attain maximum tensile strength using statistical tools such as design of experiment (DoE), analysis of variance (ANOVA), response graph and contour plots. By this method, it is found that maximum tensile shear fracture load of 12.76 kN can be achieved if a joint is made using tool rotational speed of 900 rpm, welding speed of 110 mm/min, tool shoulder diameter of 12 mm and tool tilt angle of 1.5.

Application of response surface methodology and fuzzy logic based system for determining metal cutting temperature

Tanikić D., Marinković V., Manić M., Devedžić G., Ranđelović S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2016

Vol. 64, nr 2

435--445

The heat produced in metal cutting process has negative influence on the cutting tool and the machined part in many aspects. This paper deals with measurement of cutting temperature during single-point dry machining of the AISI 4140 steel, using an infrared camera. Various combinations of cutting parameters, i.e. cutting speed, feed rate and depth of cut lead to different values of the measured cutting temperature. Analysis of the measured data should explain the trends in temperature changes depending on changes in the cutting regimes. Furthermore, the temperature data is modelled using response surface methodology and fuzzy logic. The models obtained should determine the influence of cutting regimes on cutting temperature. The main objective is the reduction of cutting temperature, i.e. enabling metal cutting process in optimum conditions.