Friction drilling is a new trend of hole making process in which the frictional force between the tool and work material is used for hole making, by thrusting of the tool into the work material without chips formation as like conventional drilling. During friction drilling ductility of the material increases with heat, which causes the material to extrude on the front and backsides of the hole, which forms the boss and bush respectively on the work piece, so it provides the required projection for threading. The damage to the surface occurring during the hole making, takes place at high temperature, which could be monitored using thermography. By using this technique damages occur during manufacturing can be monitored and minimized. Due to high temperature during drilling various phenomena like thermal phase transition, plastic flow of material and extrusion may occur, which directly influences mechanical properties and may cause plastic material from the hole surface. Friction drilling is used for hole making process of aerospace, automotive, commercial and industrial products.

This study explores the performance, emission characteristics, and machine learning-based predictive modeling of a compression ignition engine operating on diesel–fusel oil blends. Experiments were carried out at ten engine speeds ranging from 1000 to 3250 rpm under full-load conditions using six blends containing 0%–37% fusel oil by volume. The brake-specific fuel consumption (BSFC) rose from 296 g/kW h for neat diesel to 358 g/kW h for the 37% blend at 2250 rpm, corresponding to a 20.9% increase, while a 22.7% rise was noted at 1000 rpm. Maximum power output declined from 6.0 kW (diesel) to 5.78 kW (37% blend) at 3250 rpm, accompanied by a 12% reduction in engine torque (ET) at higher fusel proportions. Emission measurements indicated a 15%–20% decrease in nitrogen oxides (NOx) with greater fusel content, whereas carbon monoxide (CO) and unburned hydrocarbon emissions increased by 35% and 18%, respectively. Five machine learning models—extreme gradient boosting (XGBoost), random forest (RF), support vector regression, artificial neural network, and multi-layer perceptron, were employed to predict engine parameters using 58 experimental data samples. Hyperparameter optimization through a fivefold cross-validated grid search revealed that XGBoost achieved the highest predictive accuracy, with R2 values exceeding 0.90 for seven of nine target outputs, including 0.94 for BSFC, 0.99 for engine power and NOx, and 0.96 for ET and CO. The RF model exhibited similar reliability, whereas neural networks were less consistent due to data limitations. This hybrid framework effectively minimizes experimental effort by ∼88% while maintaining over 90% prediction accuracy.

EN31 steel is a high-carbon alloy steel used for several industrial applications like drilling & power tools. The novelty of this study lies in the optimization of liquid nitriding duration for the EN31 steel, which enhances the surface properties and a prolonged service life in tribological applications. In order to overcome the limitations, this study has aimed to investigate the impact of liquid nitriding at 570°C for 30, 60, and 90 minutes to study the hardness, corrosion and wear properties of EN31 steel. As a thermochemical surface treatment for EN31 steel, the study explores the application of liquid nitriding. Compared to traditional methods like gas nitriding, carburizing, hard chrome plating and hardening, the liquid nitriding provides uniform compound layer formation, enhanced surface hardness, and faster processing with less distortion. Performance of the modified EN31 steel post nitriding process was evaluated through various tests, like the pin-on-disk method to assess wear resistance and neutral salt spray testing as per ASTM B117 to measure corrosion resistance. Noticeable improvements in the steels tribological and anti-corrosive properties after nitriding were demonstrated through the test results. Upon analysing the microstructure, a uniform, defect-free nitride layer was revealed. The study evaluated that the 90-minute nitriding cycle resulted in the best performance related to corrosion resistance, wear resistance and surface hardness. A maximum hardness of 31.7 HRC would be achieved during the 90 minute cycle. Interestingly, this cycle also produced the deepest effective case depth thereby enhancing durability of steel under intense load bearing conditions. In addition, the outstanding corrosion resistance of steel was confirmed during 24-hour salt spray testing as no red rust was detected on the nitride samples. The test findings are validated the liquid nitriding as an economical surface treatment process for improving performance as well as durability and also reducing maintenance needs of EN31 steel for applications like drilling tools and for enhancing its reliability in abrasive and corrosive environments.