International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This paper presents an experimental study related to the optimization of cutting parameters in roughing turning of AISI 1045 steel under flooded conditions. The aim is to find a suitable combination of cutting parameters (cutting speed, depth of cut and feed rate) that minimize specific cutting energy and maximize material removal rate. The machining experiments were performed based on the Taguchi L27 full-factorial orthogonal array and response surface methodology (RSM) has been used to obtain the regression model for the specific cutting energy and material removal rate. Analysis of variance (ANOVA) was used to find out the significance of each cutting parameter. Finally, the developed models were interfaced with an artificial bee colony (ABC) to determine the optimal set of cutting parameters.

Modelling and prediction of cutting forces in metal cutting is very important, due to their significant impacts on quality of machined surface, tool wear, self-excited vibrations, etc. However, accurate modelling of the cutting forces in high pressure jet assisted machining is not a simply task due to complex relations between many highly interlinked variables of cutting process influencing these forces. The objective of this study is to utilize two artificial intelligence methods, namely particle swarm optimization (PSO) and simulated annealing (SA), for prediction of the cutting forces components in high pressure jet assisted turning of carbon steel Ck45E. A study of effect of various process parameters including feed, cutting speed and depth of cut on the cutting forces was carried out. The results obtained from the PSO and SA based models were compared with experimental results for their performance. The analysis reveals that developed models are able to make accurate prediction of cutting forces by utilizing small sized training and testing datasets.

The dynamic characteristics of spindle-holder-tool assembly is one of the most important factors that have considerable influence on cutting process stability, quality of machined surface, tool life, material removal rate, etc. In order to determine the stable cutting conditions it is essential knowledge of the tool point frequency response function (FRF). The objective of this study is development of a two different artificial intelligence methods, namely, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) as a potential modelling techniques for prediction of natural frequencies of tool controlled mode. First of all, the natural frequencies of the tool controlled mode for limited combinations of tool overhang length and tool diameter were identified experimentally. The results were used to train an ANN and ANFIS models and both models were compared for their prediction capability with the experimentally determined data. Regarding the results, ANN and ANFIS models were found to be capable of very accurate predictions of natural frequencies of the tool controlled mode.