Abstract
This manuscript presents the influencing parameters of CNC turning conditions to get high removal rate and minimal response of surface roughness in turning of AA7075-TiC-MoS2 composite by response surface method. These composites are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by RSM technique. Turning process is studied by response surface methodology design of experiment. The optimal parameters were predicted by RSM technique. The most influencing process parameter predicted from RSM techniques in cutting speed and depth of cut.
Highlights
A Light weight metal matrix composite (MMC) imparts several advantages over alloys
Ramanan et al have explained that RSM is a collection of mathematical and statistical techniques, which consist of experimental design for defining the range of independent input variables and empirical mathematical model [9]
The empirical mathematical model is used to explore an appropriate relationship between the output responses and the input variables
Summary
A Light weight metal matrix composite (MMC) imparts several advantages over alloys. The MMCs exhibit improved properties compared with monolithic alloy. Literatures reported that just lowering the body weight without reducing the weight of the power train would not alter the fuel economy Such that to enhance the fuel economy the engine material has to be replaced with lighter materials [4]. Nalbanth have observed that RSM is helpful in developing a suitable approximation for the true functional relationship between the independent variables and the response variable that may characterize the nature of the machining [7]. Author developed a numerical model to predict the abrasive wear rate of AA7075 alloy reinforced with SiC particles. This model was developed using RSM [10]. This research work is planned to predict the influencing parameters between various factors play a critical role especially for multivariable optimization in engineering problems and application of RSM in the prediction of characteristics process of composites
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