Multi-regression prediction model for surface roughness and tool wear in turning novel aluminum alloy (LM6)/fly ash composite using response surface and central composite design methodology

Abstract

Turning experiments were conducted on a novel aluminum alloy (LM6)/fly ash composite based on the response surface and face centered central composite design methodology. The effects of cutting parameters on surface roughness and tool wear were investigated. Multiple regression models were developed for the responses and the adequacies of the developed models were tested at 95% confidence interval using the analysis of variance (ANOVA) technique. Carbide inserts (Model: CNMG 120408-M5) were used for turning the specimens in a CNC turning machine (model: LT-16). The test for significance of the regression models, the test for significance on individual model coefficients and the lack-of-fit tests were performed using the statistical Design-Expert7.0v software environments. R2 indicated the model significance and the value was more than 97%, revealed that the relation between cutting responses and input parameters held good for more than 97% and the model was adequate.

Comparative Analysis of Multiple Linear Regression and Artificial Neural Network for Predicting Friction and Wear of Automotive Brake Pads Produced from Palm Kernel Shell

“…However, prior to training of the neural network, the inputs such as brake interface temperature and workdone by brake applications were scaled within the value of 0-1 using equation ( 7) [14]. While the experimental output wear rate (Table 3) was normalized using the relation in equation (8) reported by [15]. The reason for this is that input and output data set are measured in different units and needed to be normalized into the dimensionless units to remove the arbitrary effect of similarity among the data.…”

“…The results show that force-wear equation derived from MRA was fairly accurate way of predicting the attainment of prescribed tool wear. The effects of cutting parameters on surface roughness and tool wear were investigated in turning novel aluminum alloy ash composite by [8]. The authors concluded that the relationship between cutting responses and input parameters held good for more than 97 % and the model was adequate.…”

Machining performance of aluminium matrix composite and use of WPCA based Taguchi technique for multiple response optimization

“…Aluminium matrix composites (AMCs) impregnated with hard ceramic particulates are replacing the conventional materials in automobile, aerospace, marine and military applications for their significant properties; like high temperature resistance, high stiffness and high strength to weight ratio (Manna & Bhattacharyya, 2003;Yingfei et al, 2010;Mishra et al, 2015;. Existence of hard SiC particulates in the composites causes difficulty in machining with carbide tip tools, leading to inferior surface quality and too much tool wear (Manna & Bhattacharyya, 2003;Panda et al, 2017). Surface roughness influences the product quality (Ramezani et al, 2015) and tool wear is one of the predominant component of product cost.…”