Tribological behaviour of ZA/ZrB₂ in situ composites using response surface methodology and artificial neural network

Abstract

Recent advancement in metal matrix composites shows the reduction in resource and energy consumption through improvement in tribological properties. However, statistical modelling helps to achieve the material efficiency through optimizing experimental parameters. This study focuses on developing a statistical modelling to predict the tribological behaviour of ZA/ZrB2 insitu composites. Analysis of variance (ANOVA) was conducted using Response surface methodology (RSM) by Design expert 13 software which was suggested the use of quadratic model and the regression equation was developed. Varying load, sliding distance, and volume % of ZrB2 as input parameters and wear and coefficient of friction (COF) as response parameters were utilized in RSM. Results indicate that the volume % of ZrB2 is the main contributing parameter for wear while for COF, load is the main contributing factor. Artificial neural network (ANN) developed using PYTHON was also employed to validate the optimized parameters for wear and COF suggested by RSM. Experimental results and statistical analysis results obtained from RSM and ANN show the close tolerance and thus suggest a significant model that envisages the tribological characteristics of alloy and composites. Regression equation could be successfully utilized to predict the wear and COF at any given set of input variables based on applications.

Research on self-healing characteristic and state prediction method of the copper based powder metallurgy materials on friction interface

“…Tsuha et al proposed an approach for boundary layer representation of elastohydrodynamic (EHD) lubricated contact studies as a function of EHD stiffness parameters, the emphasis has been focused on the prediction of EHD pressure and film thickness [23]. Kumar et al developed statistical modeling to predict the tribological behavior of ZA/ZrB2 in situ composites [24]. Elaiyarasan et al used an artificial neural network (ANN) model to predict the material deposition rate (MDR) and surface roughness (SR) [25].…”

Correlating topographical characteristics of relaxed layer to tribology in Cu-Gr-TiC composite system

“…The possibility of choosing a suitable metal matrix, reinforcements, and manufacturing processes and parameters have a significant influence over the physical and mechanical characteristics of metal matrix composites. The matrix phase of the composite assists in fabrication and the transfer of loads from the matrix to the reinforcement and vice versa [1][2][3]. As a matrix, copper and its alloys are widely employed for variety of industrial and engineering applications owing to their good thermal conductivity, electrical conductivity and ease of manufacturing [4][5][6][7].…”

Prediction of tribology in (Mg2Si+TiB2)/A356 composites based on RSM method and correlative with topographical characteristics

“…Various researchers explored the tribology of Al alloy hybrid composite by applying different statistical methods such as the Taguchi design approach, Factorial design, Regression analysis, Response surface methodology (RSM), and Artificial Neural Network (ANN) [15][16][17][18][19]. The RSM technique is widely used for a system containing multiple variables to optimise wear variables for optimal system performance [20].…”