In the rail industry, there are four types of steel grades used for monoblock wheels, namely ER6, ER7, ER8 and ER9. ER7 steel is manufactured in accordance with the EN13262 standard and is utilized in European railway lines. These train wheels are formed by pressing and rolling after which they are machined using turning process to achieve their final dimensions. However, machining ER7 steels can be challenging due to their high mechanical properties, which can facilitate rapid tool wear and thermal cracking. Therefore, while the use of coolants is critical to improving their machinability, using conventional flood coolants adds extra operational costs, energy and waste. An alternative is to use minimum quantity lubrication (MQL) cooling technology, which applies small amounts of coolant mixed with air to the cutting zone, leaving a near-dry machined surface. In the current study, preliminary tests were undertaken under dry conditions and using coated carbide inserts to determine the optimal cutting parameters for machining ER7 steel. The impact of the cutting speed and feed rate on surface roughness (R_a), energy consumption and cutting temperature were investigated and used as a benchmark to determine the optimal cutting parameters. Next, additional machining tests were conducted using MQL and nano-MQL cooling technologies to determine their impact on the aforementioned machining outputs. According to preliminary tests, and within the tested range of the cutting parameters, using a cutting speed of 300 m/min and a feed rate of 0.15 mm/rev resulted in minimal surface roughness. As a result, using these optimal cutting parameters with MQL and Nano-MQL (NMQL) cooling technologies, the surface roughness was further reduced by 24% and 34%, respectively, in comparison to dry conditions. Additionally, tool wear was reduced by 34.1% and 37.6%, respectively. The overall results from this study demonstrated the feasibility of using MQL coolants as a sustainable machining alternative for steel parts for rail wheel applications. In addition, the current study highlight the enhanced performance of MQL cooling technology with the addition of nano additives.

In recent years, turning of hardened steels has replaced grinding for finishing operations. This process is compared to grinding operations; hard turning has higher material removal rates, the possibility of greater process flexibility, lower equipment costs, and shorter setup time. CBN or ceramic cutting tools are widely used hard part machining. For successful application of hard turning, selection of suitable cutting parameters for a given cutting tool is an important step. For this purpose, an experimental investigation was conducted to determine the effects of cutting tool edge geometry, feed rate and cutting speed on surface roughness and resultant cutting force in hard turning of AISI H13 steel with ceramic cutting tools. Machining experiments were conducted in a CNC lathe based on Taguchi experimental design (L₁₆) in different levels of cutting parameters. In the experiments, a Kistler 9257 B, three cutting force components (Fc, Ff and Fr) piezoelectric dynamometer was used to measure cutting forces. Surface roughness measurements were performed by using a Mahrsurf PS1 device. For statistical analysis, analysis of variance has been performed and mathematical model have been developed for surface roughness and resultant cutting forces. The analysis of variance results showed that the cutting edge geometry, cutting speed and feed rate were the most significant factors on resultant cutting force while the cutting edge geometry and feed rate were the most significant factor for the surface roughness. The regression analysis was applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Afterwards a confirmation tests were performed to make a comparison between the predicted results and the measured results. According to the confirmation test results, measured values are within the 95% confidence interval.

Information technology applications are crucial to the proper utilization of manufacturing equipment in the new industrial age, i.e., Industry 4.0. There are certain fundamental conditions that users must meet to adapt the manufacturing processes to Industry 4.0. For this, as in the past, there is a major need for modeling and simulation tools in this industrial age. In the creation of industry-driven predictive models for machining processes, substantial progress has recently been made. This paper includes a comprehensive review of predictive performance models for machining (particularly analytical models), as well as a list of existing models' strengths and drawbacks. It contains a review of available modeling tools, as well as their usability and/or limits in the monitoring of industrial machining operations. The goal of process models is to forecast principal variables such as stress, strain, force, and temperature. These factors, however, should be connected to performance outcomes, i.e., product quality and manufacturing efficiency, to be valuable to the industry (dimensional accuracy, surface quality, surface integrity, tool life, energy consumption, etc.). Industry adoption of cutting models depends on a model's ability to make this connection and predict the performance of process outputs. Therefore, this review article organizes and summarizes a variety of critical research themes connected to well-established analytical models for machining processes.

ER7 quality steels produced according to EN13262 standard used in European railway lines can be produced at Kardemir A.Ş Railway Wheel Production Facilities. These wheels, which are produced by pressing and rolling, are included in the machining process to be brought to their final dimensions after the production process. In this experimental study, the effects of cutting speed (CS) and feed rate (FR) on surface roughness (SR), power consumption (PC) and cutting temperature (CT) were investigated during turning of ER7 wheel material with 1mm depth of cut value under dry test conditions using MT-TiCN+Al2O3 CVD coated-RCMX 1606M0 K15 quality tool. Analysis of Variance (ANOVA) method was used to determine the effect rates of the input parameters on the results. In addition, estimation equations were obtained using the Response Surface Method. As a result of this study, the effective parameter for SR is FR with 57.158%, while the effective parameter for PC and CT is found to be CS, and it is found to be 58.071% and 64.977%, respectively.