In this work, the dry turning of Inconel 601 alloy in a dry environment with PVD-coated cutting inserts was studied. Turning was performed at various cutting speeds, feeds, insert shapes, corner radii, rake angles, and approach angles. After machining, arithmetic mean surface roughness (Ra) and flank wear (VB) were measured, and the material removal rate was also calculated (MRR). An analysis of variance (ANOVA) was performed to determine the effects of the turning input parameters. For the measured values, the turning process was modeled using an artificial neural network (ANN). Based on the obtained model, the process parameters were optimized using a genetic algorithm (GA). The objective function was to simultaneously minimize Ra and VB and maximize MRR. The accuracy of the model and the optimal values were further validated by confirmation experiments. The maximum percentage errors, which are less than 2%, indicate the possibility of practical implementation of the hybrid approach for modeling and optimization of dry turning of Inconel 601 alloy.

The study investigated the turning of C45 steel in a dry environment. The input parameters that were varied were cutting speed, feed, depth of cut, corner radius and insert type. The experimental investigations were carried out according to a custom experimental design using the D-optimality criterion. The measured output parameters were dimensional deviation, flank wear and surface roughness, while the material removal rate was calculated. A detailed analysis and evaluation of the effects of the input parameters on the output parameters was carried out. The model was diagnosed and appropriate regression equations was established. Based on the obtained regression equations, multi-objective optimisation was performed using particle swarm optimisation. The objective function was to simultaneously minimise dimensional deviation, flank wear and surface roughness and maximise material removal rate. The optimisation was carried out for different weighting coefficients of each output function for different production requirements. The obtained models and optimal values were verified by additional confirmation experiments.

This paper presents a new innovative approach for designing and manufacturing a fixture for locating and clamping porous blocks of biocompatible material, which is required for their machining on CNC machines. Manufacturing porous blocks for their application in medical and/or dental fields is gaining traction. However, limited solutions are available today. In order to address this issue, a new design has been proposed for locating and clamping porous blocks. Finite element analysis was used as a verification tool for the designed fixture with the workpiece, which showed a low concentration of stresses. After the manufacturing, dimensional verification in the form of CAD analysis showed small deviations on the manufactured object with deviations peaking around +0.015 mm, thus validating the adequate locating and clamping of the workpiece.