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Improvement of cutting performance of aluminium alloy 6061

Al Ani Habeeb

Journal of Achievements in Materials and Manufacturing Engineering

2023

Vol. 121, nr 2

258--266

Surface irregularities can result in the formation of nucleation sites for wear and cracks. Roughness is considered one of the important predictors when it comes to the performance of mechanical instruments or components. The study aimed to establish prediction models using response surface methodology (RSM) to optimise surface roughness (SR) when turning aluminium alloy 6061 with carbide insert TiCN/TiN using RSM. Design/methodology/approach RSM is a well-established method utilised by many studies in the literature to predict the machining outcomes and to choose the ideal machining parameters of specific machining processes and materials. It is an economical, practical, and relatively easy method. Moreover, it is a common method utilised in machining process modelling. Therefore, the study used RSM to develop prediction models and optimise the machining parameters to achieve the optimal surface roughness when turning aluminium alloy 6061 with carbide insert TiCN/TiN. Findings Both first and second-order models were developed and were found to be adequate according to the analysis of variance. The most contributing factor to the surface roughness was cutting speed. The contour plots have been generated and show different cutting parameter plots and how they influence the surface roughness (SR) values. Surface roughness reached its highest value when the feed rate increased, cutting depth increased, and cutting speed decreased. High cutting speed, low feed rate, and low cutting depth should be used to obtain the lowest surface roughness. Research limitations/implications Further development of contours generated by the RSM models will facilitate the selection of the ideal combination of cutting speed, feed rate, and depth to achieve optimal surface roughness. RSM is considered an efficient and convenient method, requiring little experimentation and giving highly crucial inputs and information. Practical implications Surface roughness equations clearly explain that the cutting speed and cutting feed rate are major contributors to surface roughness. Low cutting speed, high cutting depth, and feed rate correspond to a higher surface roughness. Originality/value In conclusion, reliable models for the prediction of surface roughness were developed and used to optimise the machining efficiency of aluminium alloy 6061. RSM is considered an efficient and convenient method, requiring little experimentation and giving highly crucial inputs and information.