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Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on surface roughness, tool wear and cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define a model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have a relative prediction error below 10%. The research has shown that when the training dataset is small neural network modeling methodologies are comparable with regression analysis methodology and can even offer better results, in which case an average relative error of 3.35%. Advantages of off-line process control which utilizes process models by using these two modeling methodologies are explained in theory.

This paper presents a new approach improving the reliability of flank wear prediction during the end milling process. In the present work, prediction of flank wear has been achieved by using cutting parameters and force signals as the sensitive carriers of information about the machining process. A series of experiments were conducted to establish the relationship between flank wear and cutting force components as well as the cutting parameters such as cutting speed, feed per tooth, and radial depth of cut. In order to be able to predict flank wear a new linear regression mathematical model has been developed by utilizing functional data analysis methodology. Regression coefficients of the model are in the form of time dependent functions that have been determined through the use of functional data analysis methodology. The mathematical model has been developed by means of applied cutting parameters and measured cutting forces components during the end milling of workpiece made of 42CrMo4 steel. The efficiency and flexibility of the developed model have been verified by comparing it with the separate experimental data set.

The latest trends in machining research show that great efforts are being made to understand the impact of different cooling and lubrication techniques as well as cutting parameters on machining performances. This paper presents the investigation results of different cutting parameters and different cutting environments such as dry machining, minimum quantity lubrication (MQL) and minimum quantity lubrication with compressed cold air (MQL+CCA) on average surface roughness, cutting force and material removal rate. The experiments were designed based on three input parameters and three different cutting environments when turning of EN AW-2011 alloy. Taguchi-based grey relational analysis was used to identify the optimal process parameters by which minimum values of surface roughness, minimum value of cutting force and maximum value of material removal rate will be achieved. The results showed that minimum quantity lubrication in the stream of compressed cold air, in comparison to dry and minimum quantity lubrication machining, gives the best machining performances. Therefore, the use of MQL + CCA method, which reduces the amount of lubricant may represent in the described extent of turning operations an alternative to turning processes most often carried out by wet method that causes considerable costs for purchasing, maintaining and using cutting fluids.