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 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.

This study presents intensive numerical analyses of the ring elongation testing technique (RETT) used to characterize the 7175-T7351 aluminum alloy and St 37 carbon steel rings materials. Theoretical solutions were developed to predict the elastic properties of the materials of the ring using both curved and straight beam formulas. Besides, to predict the full deformation behaviors of the ring's materials, elastic, and elastic-plastic finite element analyses (FEA) were performed on the 2D and 3D ring models. Moreover, FEA were conducted to study the effects of ring dimensions, pin diameter, and ring shape on the RETT results. Modeling the ring specimens with plane stress elements and 3D-stress elements was done with the FEA commercial package (ABAQUS). Theoretical and the FEA results were then compared to the experimental results. The theoretically obtained elastic results accurately predicted the elastic properties of the ring materials. Furthermore, the FEA results successfully predicted the full deformation behaviors of the ring materials.