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Recently, cylinder liners of automotive and aircraft engines are encountered severe wear due to piston slap occurred by the repeated sliding stroke of piston. This research focuses on providing an alternative material for cylinder liners, which can be obtained through friction stir processing on AA7075 with different weight ratios of reinforcements like Al2O3 and SiC. Friction stir processing is executed with a tool rotational speed of 1000 r/min and traveling speed of 56 mm/min at 2° tool tilt angle. The images of optical microscopy reveal the fine-grains with a homogeneous distribution of secondary phase particles and also offer decrement in particle clustering except at some localized regions. It is identified that addition of 3.7 wt.% Al2O3 + 3.0 wt.% SiC in AA7075 enhanced the microhardness about 33.96% higher than base matrix. Hence, wear resistance of the specimen is improved. Dry sliding wear behavior of specimens is analyzed using pin on disc at 20, 40, and 60 N load conditions to create the actual sliding behavior of piston on cylinder wall. Increasing the wt.% of Al2O3 particles enhanced the wear resistance of AA7075 surface hybrid composites at 20 N load due to the formation of protective tribofilm. Specimen with 3.7% Al2O3 + 3.0% SiC has better lubrication and load-bearing capacity that exhibits superior tribological behavior at 40 and 60 N. Higher Fe content of 5.44 wt.% has been obtained through spectroscopy analysis in S5, which is 8% and 6% higher than S3 (7.5% of Al2O3) and S7 (6% of SiC), respectively. The scratching action of this hard specimen on the counterpart brings more Fe content. Increase of Al2O3 particles paved the way for tribofilm formation and identified through scanning electron microscopy micrographs. The observation from worn specimens shows that finer wear debris had increased by adding more quantity of SiC particles due to abrasive wear mechanism.

This investigation has designed a tool condition monitoring system (TCM) while milling of Inconel 625 based on sound and vibration signatures. The experiments were carried out based on response surface methodology (RSM) central composite design, design of experiments. The process parameters such as speed, feed, depth of cut and vegetable-based cutting fluids were optimized based on surface roughness, flank wear. It was found that the sound pressure and vibration signatures have the direct relation with flank wear. The statistical features like root mean square, skewness, kurtosis and mean values were extracted from the experimental data. From the designed NN estimator, the cutting tool flank wear was predicted with the mean square error (MSE) of 0.084212.

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Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.