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.

The application of stainless steel 420 is noted in various industrial sectors such as aerospace, automobile etc. However, the wear mechanism of stainless steel 420 is greatly affected by the use of 100Cr6 balls due to the adhesion and abrasion mechanism generated between the contact surfaces. In this work, the ball-on-flat experiments were performed on stainless steel-420 versus 100cr6 friction pairs under dry, minimum quantity lubrication (MQL) and pool conditions. Then, the wear studies in terms of volume loss, profile tracks, micrographs, coefficient of friction, etc. were performed. The results demonstrated that the MQL and pool conditions help to improve the tribological properties of stainless steel 420 alloy against 100Cr6 ball.

In this study, mechanical testing of glass bead (GB), glass fiber (GF), and hybrid (GB/GF) composites was carried out. Following that, drilling tests were undertaken on glass bead/fiber-reinforced hybrid Polyamide 66 (PA66) polymer composites. The purpose of this study is to determine the mechanical properties of the cutting elements and the effect of cutting parameters (spindle speed and feed rate) and reinforcement ratios on thrust force and surface roughness (Ra). The contribution of the cutting parameters to the investigated outcomes was determined using statistical analysis. Optical microscopy and scanning electron microscopy (SEM) was used to inspect the hole quality and damage mechanisms. The results revealed that the feed rate was the most contributing factor to thrust force (96.94%) and surface roughness (63.59%). Furthermore, in comparison to other hybrid composites, the lowest R_a value was obtained as 0.95 µm in samples containing 30% GB, while the R_a value was 1.04 µm in samples containing 10% GF + 20% GB. Polymer PA reinforced with 30% GF had the highest strength, modulus of elasticity, impact strength, and hardness.

Particularly in sectors where mechanisation is increasing, there has been persistent effort to maximise the use of existing assets. Since maintenance management is accountable for the accessibility of assets, it stands to acquire prominence in this setting. One of the most common methods for keeping equipment in good working order is predictive maintenance with machine learning methods. Failures can be spotted before they cause any downtime or extra expenses, and with this aim, the present work deals with the online detection of wear and friction characteristics of stainless steel 316L under lubricating conditions with machine learning models. Wear rate and friction forces were taken into account as reaction parameters, and biomedical-graded stainless steel 316L was chosen as the work material. With more testing, the J48 method’s accuracy improves to 100% in low wear conditions and 99.27% in heavy wear situations. In addition, the graphic showed the accuracy values for several models. The J48 model is the most precise amongst all others, with a value of 100% (minimum wear) and an average of 98.92% (higher wear). Amongst all the models tested under varying machining conditions, the J48’s 98.92% (low wear) and 98.92% (high wear) recall scores stand out as very impressive (higher wear). In terms of F1-score, J48 performs better than any competing model at 99.45% (low wear) and 98.92% (higher wear). As a result, the J48 improves the model’s overall performance.

Damage due to a shortage or excess of or the pollution of lubricating oil is often cited as one of the most significant issues confronted by the rolling mill sectors. Lubrication can be provided by either central lubrication systems or individual lubrication systems. In this study, the wear characteristics of the mono-block rolling plain bearing material that is utilized in wire rod rolling mills were evaluated under conditions where the lubricating oil medium included either 2.5% of scale, 5% of scale, or no scale at all. In this experimental study, a unique ball-on-flat experimental setup similar to the one used in the ASTM G133-05 standards was used. Bronze was used as the bearing material and 100Cr6 roller-bearing steel was used as a steel ball of 6 mm in diameter. The experiments were carried out at room temperature, at three different sliding speeds of 5 mm/s, 10 mm/s, and 15 mm/s, and with three different loads of 10 N, 20 N, and 30 N. The wear mechanisms were analyzed visually and elementally using Scanning Electron Microscope (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) methods. An Analysis of Variance (ANOVA) and the Response Surface Method (RSM) were used to analyze the test results, such as volumetric material loss, the coefficient of friction, and the surface profile. In this study, which was carried out in a lubricant environment containing solid particles, the most effective parameter was the environmental parameter. The increase in the number of solid particles caused an increase in volume loss and friction coefficient.

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.