The present research work amis to develop a controlled vibratory system for improving the performance of slotting process based on the vibration-assisted milling technique. In addition, the establishment of a statistical model for predicting the process efficiency was a key aim target.In the current investigation, an ultrasonic vibratory device was designed to provide vibration oscillations in the cutting feed direction to the 7075 aluminum alloy workpiece side at 34.7 kHz frequency and 10 μm amplitude. A LabView code has been developed to control the input vibration parameters. A full factorial design of experiments with 4 factors and 2 levels was conducted. An in-depth statistical analysis is then implemented to study the effect of machining and vibration parameters on the process performance. Then, a statistical model for predicting the process response in terms of cutting force, surface roughness, and machining accuracy has been established.Some experiments showed a significant reduction in cutting force up to 50% in the feed direction. Additionally, an improvement in workpiece surface roughness was recorded. Regarding the machined surface accuracy, introducing ultrasonic vibrations significantly reduces the produced slot width error compared to Conventional Milling (CM).The developed statistical model shows a very well agreement with the experimental results. Therefore, the developed VAM technique is suitable for manufacturing parts that require high geometrical and dimensional accuracy.

The current study aims to evaluate the performance of the ultrasonic vibration-assisted milling (USVAM) process when machining two different materials with high deviations in mechanical properties, specifically 7075 aluminium alloy and Ti-6Al-4V titanium alloy. Additionally, this study seeks to develop an AI-based model to predict the process performance based on experimental data for the different workpiece characteristics. In this regard, an ultrasonic vibratory setup was designed to provide vibration oscillations at 28 kHz frequency and 8 µm amplitude in the cutting feed direction for the two characterised materials of 7075 aluminium alloy (150 BHN) and Ti-6Al-4V titanium alloy (350 BHN) workpieces. A series of slotting experiments were conducted using both conventional milling (CM) and USVAM techniques. The axial cutting force and machined slot surface roughness were evaluated for each method. Subsequently, Support Vector Regression (SVR) and artificial neural network (ANN) models were built, tested and compared. AI-based models were developed to analyse the experimental results and predict the process performance for both workpieces. The experiments demonstrated a significant reduction in cutting force by up to 30% and an improvement in surface roughness by approximately four times when using USVAM compared to CM for both materials. Validated by the experimental findings, the ANN model accurately and better predicted the performance metrics with RMSE = 0.11 µm and 0.12 N for Al surface roughness and cutting force. Regarding Ti, surface roughness and cutting force were predicted with RMSE of 0.12 µm and 0.14 N, respectively. The results indicate that USVAM significantly enhances milling performance in terms of a reduced cutting force and improved surface roughness for both 7075 aluminium alloy and Ti-6Al-4V titanium alloy. The ANN model proved to be an effective tool for predicting the outcomes of the USVAM process, offering valuable insights for optimising milling operations across different materials.

This study aims to illustrate the design, fabrication, and optimisation of an ultrasonic welding (UW) machine to join copper wires with non-woven PVC textiles as smart textiles. The study explicitly evaluates UW parameters’ impact on heat generation, joint strength, and electrical properties, with a comprehensive understanding of the process dynamics and developing a predictive model applicable to smart textiles. The methodological approach involved designing and manufacturing an ultrasonic piezoelectric transducer using ABAQUS finite element analyses (FEA) software and constructing a UW machine for the current purpose. The full factorial design (FFD) approach was employed in experiments to systematically assess the influence of welding time, welding pressure, and copper wire diameter on the produced joints. Experimental data were meticulously collected, and a backpropagation neural network (BPNN) model was constructed based on the analysis of these results. The results of the experimental investigation provided valuable insights into the UW process, elucidating the intricate relationship between welding parameters and heat generation, joint strength, and post-welding electrical properties of the copper wires. This dataset served as the basis for developing a neural network model, showcasing a high level of accuracy in predicting welding outcomes compared to the FFD model. The neural network model provides a valuable tool for controlling and optimising the UW process in the realm of smart textile production.

This study aims to evaluate the crashworthiness performance of hybrid energy-absorbing structures composed of thin-walled square tubes filled with various lattice configurations. The research seeks to determine how combining different lattice types (single, dual-hybrid, and tri-ple-hybrid) affects energy absorption under axial impact loading, contributing to the develop-ment of lightweight and high-performance crashworthy systems for transportation safety. Finite Element Analysis (FEA) was performed using Abaqus/Explicit to simulate axial impact loading on hybrid models. Each model consisted of a thin-walled square aluminum tube (Al6063-T5) filled with an internal lattice structure (AlSi10Mg). Lattice configurations included single (BCC, FCC, Cubic), dual-hybrid (Cubic+BCC, Cubic+FCC, BCC+FCC), and triple-hybrid (Cu-bic+BCC+FCC) combinations. To ensure a fair comparison, the total mass of all models was fixed at 27.0 g (19.78 g tube + 7.22 g lattice) by adjusting lattice strut diameters accordingly. Crashworthiness metrics (Total Energy Absorption (EA), Specific Energy Absorption (ES), Peak Crushing Force (FP), Mean Crushing Force (FM), and Crushing Force Efficiency (ηF)) were computed. Validation against published experimental data confirmed the accuracy of the FEA methodology, with a deviation percentage below 3.5%. Generally, the dual-hybrid and triple-hybrid models showed enhanced crashworthiness performance compared to single-hybrid models. The triple-hybrid model demonstrated the best crashworthiness performance with an EA of 1024.009 J and an ES of 37.926 J/g. This represents a 13.5% improvement over the top dual-hybrid model and a 17.1% improvement over the best single-hybrid model. The combi-nation of multiple lattice structures within hybrid energy absorbers significantly improves crashworthiness performance. The findings support the integration of complex lattice architectures in lightweight energy-absorbing components for enhanced safety in transportation applications.

The study aims to investigate the likelihood of Zernike polynomial being used for reconstructing rabbit corneal surfaces as scanned by the Pentacam segment tomographer, and hence evaluate the accuracy of corneal power maps calculated from such Zernike fitted surfaces. The study utilised a data set of both eyes of 21 rabbits using a reverse engineering approach for deductive reasoning. Pentacam raw elevation data were fitted to Zernike polynomials of orders 2 to 20. The surface fitting process to Zernike polynomials was carried out using randomly selected 80% of the corneal surface data points, and the root means squared fitting error (RMS) was determined for the other 20% of the surface data following the Pareto principle. The process was carried out for both the anterior and posterior surfaces of the corneal surfaces that were measured via Pentacam scans. Raw elevation data and the fitted corneal surfaces were then used to determine corneal axial and tangential curvature maps. For reconstructed surfaces calculated using the Zernike fitted surfaces, the mean and standard deviation of the error incurred by the fitting were calculated. For power maps computed using the raw elevation data, different levels of discrete cosine transform (DCT) smoothing were employed to infer the smoothing level utilised by the Pentacam device. The RMS error was not significantly improved for Zernike polynomial orders above 12 and 10 when fitting the anterior and posterior surfaces of the cornea, respectively. This was noted by the statistically non-significant increase in accuracy when the order was increased beyond these values. The corneal curvature calculations suggest that a smoothing process is employed in the corneal curvature maps outputted by the Pentacam device; however, the exact smoothing method is unknown. Additionally, the results suggest that fitting corneal surfaces to high-order Zernike polynomials will incur a clinical error in the calculation of axial and tangential corneal curvature of at least 0.16 ± 01 D and 0.36 ± 0.02 D, respectively. Rabbit corneal anterior and posterior surfaces scanned via the Pentacam were optimally fitted to orders 12 and 10 Zernike polynomials. This is essential to get stable values of high-order aberrations that are not affected by Zernike polynomial fittings, such as comas for Intracorneal Ring Segments (ICRS) adjustments or spherical aberration for pre-cataract operations. Smoothing was necessary to replicate the corneal curvature maps outputted by the Pentacam tomographer, and fitting corneal surfaces to Zernike polynomials introduces errors in the calculation of both the axial and tangential corneal curvatures.

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal and harmonic analyses, confirming a strong longitudinal resonance near 27.9 kHz with a tip amplitude of about 46 µm. A 27-run factorial experiment varied tool rotation (600–900 rpm), welding speed (45–55 mm/min), and plunge depth (0.10–0.25 mm). Welded joints were assessed using tensile strength and Vickers hardness. Four predictive models, support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANNs), and multiple linear regression (MLR) were trained and compared under five-fold cross-validation. The best joint quality was obtained at 900 rpm, 55 mm/min, and a 0.25 mm plunge depth, yielding a tensile strength of 188.7 MPa and a hardness of 102 HV. Overall, MLR provided the strongest predictive performance while remaining interpretable (UTS R² = 0.81, RMSE = 11.84 MPa; hardness R² = 0.67, RMSE = 2.36 HV), matching the ANN for UTS prediction and outperforming the ANN, GPR, and SVR for hardness. A coupling physics-based ultrasonic design with an interpretable predictive model offers a practical route to reduce trial and error, improve parameter selection, and accelerate the process development for ultrasonic vibration-assisted FSW of aluminium alloys; however, modest models can outperform complex ones when the dataset is limited.