Cutting forces are one of the inherent phenomena and a very significant indicator of the metal cutting process. The work presented in this paper is an investigation of the prediction of these parameters in turning using soft computing techniques. During the experimental research focus is placed on the application of various methods of cooling and lubricating of the cutting zone. On this occasion were used the conventional method of cooling and lubricating, high pressure jet assisted machining, and minimal quantity lubrication technique. The data obtained by experiment are used to create two different models, namely, artificial neural network and adaptive networks based fuzzy inference systems for prediction of cutting forces. Furthermore, both models are compared with the experimental data and results are indicated.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

The surface roughness of the machined parts is one of the most important factors that have considerable influence on the quality and functional properties of products. The objective of this study is development of a surface roughness prediction model for machining Inconel 718 in high-pressure jet assisted turning using the fuzzy expert system, where the fuzzy system is optimized using two bioinspired algorithms: genetic algorithm and particle swarm optimization. The effect of various influential machining parameters, such as diameter of the nozzle, pressure of the jet, cutting speed, feed rate, and distance between the impact point of the jet and cutting edge were taken into consideration in this study. The predicted surface roughness values obtained from developed fuzzy expert systems were compared with the experimental data, and the results indicate that proposed systems can be effectively used to estimate the surface roughness in high-pressure jet assisted turning.

The improvement of productivity, efficiency, and product quality requires the use of modern machining equipment, and modern process management. Successful management of the cutting processes requires a lot of knowledge about workpiece materials, cutting tool materials and geometry, tool machine, cooling and lubrication fluids including dosage techniques, and cutting conditions. However, the mentioned requirements are difficult to achieve in hard turning (Fig. 1). Hard turning is the cutting process for workpiece materials which are hardened above 45 HRc. This method has been introduced to replace traditional processes, which included turning, heat treatment and grinding [1]. Hard turning is almost performed using harder cutting tool materials such are the ceramics (Al 2 O 3) and cubic boron nitride tools (CBN), at lower cutting parameter values. The use of these tools and parameters causes expensive production, because of expensive tools and long machining time. The use of brittle tools requires continuous cut due to poor toughness of cutting tool edges. Use of cooling and lubrication fluid supplied under high pressure can bring some improvements in machining. This technique of fluid supplying dates from the fifties of the last century. In modern machining are used the high pressure tool systems that allow the fluid supply under pressures up to 15 MPa. The high pressure jet assisted machining (HPJAM) concept is to inject an extremely high pressure jet of cooling and lubrication fluid in the cutting zone, between chip and tool edge. In this techniques are used pressures from 40 to 200 MPa, so that jet is participating in the chips forming, similar to the non-conventional technologies [2-4]. HPJAM was established as a method that would substantially increase the removal rate and Abstract The machining of hard-to-machine bearing steel AISI 52100 (100Cr6), hardened to 62 HRc, is almost impossible using standard machining conditions and carbide cutting tools. The purpose of this research is machinability analysis and conclusions about the conditions that allow the machining of mentioned steel with carbide tools. In this paper, the turning process is carried out using coated carbide inserts and high pressure jet assisted machining, as a special technique of cooling and lubrication. In this technique, coolant circulation system with filters, environmentally acceptable, is used. A jet of cooling and lubrication fluid under extremely high pressure (50 MPa) is directed into the zone between the cutting tool edge and the workpiece. Experimental measurements were performed for different cutting parameters. Cutting forces, tool wear, surface roughness, chip shapes, and material removal rates were analyzed. The presented results show an increase in productivity, low intensity tool wear, and surface roughness in acceptable limits.

With the development of high-performance CNC machine tools, milling has been established as one of the main means of machining thin-walled parts. Thus, the selection of process parameters for milling operations is an important issue in end milling of thin-walled parts to assure product quality and increase productivity. The current study explores three machining parameters, namely wall thickness, feed, and machining strategies, that influence dimensional and form errors, surface roughness, and machining time milling of 7075-T6 aluminum alloy thin-walled parts. The effects of machining parameters on each of the response variables were analyzed using graphs of the main effects and three-dimensional surface plots. Analysis of the results show that the most influential factor for wall thickness deviation, dimensions deviation, perpendicularity deviation, flatness deviation, surface roughness of inner walls, surface roughness of outer walls, and surface roughness of reference plane was machining strategy, while feed is the most influential parameter affecting the machined time, followed by the machining strategy. The desirability concept has been used for simultaneous optimization in terms of machining parameters of the thin-walled parts machining process. Finally, a confirmation test with the optimal parameter settings was carried out to validate the results.