Villa College

In this work, the tribological, mechanical, corrosion, and sliding wear behavior of cold metal transfer-wire arc additive manufactured (CMT-WAAM) Al4043 alloy is reported and optimized using the combined use of response surface methodology (RSM) and machine learning (ML). According to microstructural observations, the grain size at the bottom was 67 μm, finer than that at the top (93 μm) due to the higher cooling rate, which led to corresponding tensile strength and elongation. This fine-grained structure in the bottom region also contributed to higher hardness values (70.62 HV) and better wear resistance, as supported by the hardness profile and wear testing. Hardness was lower in the top quarter, which had larger grains. Tensile testing demonstrated that the 0° direction exhibits the highest UTS and elongation due to the fine microstructure developed at an optimal cooling rate. Corrosion test. The corrosion rate of the top part was the lowest (0.098 mm/year) and showed the greatest potential for corrosion protection (Ecorr: − 751.23 mV), indicating a stable passive oxide film. Optimum conditions (33.3 N load, 362 RPM speed, and 50 mm wear track radius) derived by RSM optimization minimized the specific wear rate (SWR) and coefficient of friction (COF), thereby improving wear response. The ML model of XGBoost correctly predicted SWR and COF with an excellent fit in terms of R2 value (0.992 and 0.998, respectively), verifying the validity of the model. This work systematically enlightens the optimization of tribological, mechanical, and corrosion behaviors of WAAM Al4043 alloys, shedding light on their applications in engineering.

This study explores the performance of Roselle fiber-epoxy composites reinforced with biosilica from Paddy straw, emphasizing the effects of silane treatment on the morphology, mechanical properties, dynamic behavior, thermal degradation, wear resistance, and hydrophobicity. Silane treatment enhanced fiber-matrix adhesion, as confirmed by Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy analyses, which improved the interfacial bonding and increased fiber roughness. Biosilica particles (80-90 nm) reinforced the composite, leading to improved mechanical properties. The 3% biosilica composite (ERB3) achieved the highest tensile strength (103.25 MPa), flexural strength (193.3 MPa), and a balanced combination of fiber reinforcement and biosilica content. The 5% biosilica composite (ERB5) showed higher impact strength and ductility but had slightly lower tensile and flexural strength due to particle agglomeration. Dynamic Mechanical Analysis indicated that ERB3 had the best storage modulus (E '), indicating increased stiffness and rigidity, with less energy dissipation and better load-bearing capacity. Hydrophobicity decreased as biosilica content increased, with ERB5 displaying the lowest contact angle (65 degrees), though all composites maintained water resistance suitable for moisture-resistant applications. Thermal degradation tests revealed that ERB5 had the highest thermal stability, with a peak degradation temperature of 480 degrees C and the highest char residue (8.2%). Wear tests demonstrated that biosilica-reinforced composites significantly outperformed pure epoxy (EP) in wear resistance, with ERB1 showing the best results. In summary, ERB3 provides the best overall performance with superior mechanical and tribological properties, while ERB5 excels in impact strength and thermal stability.

This study investigates the combined influence of a yttria-stabilized zirconia (YSZ)-based thermal barrier coating (TBC) and cerium oxide (CeO2) nanoparticles on the performance and emissions of a compression ignition (CI) engine operated using pumpkin seed biodiesel blends. A multidisciplinary approach integrating experimental evaluation, machine learning prediction, and statistical optimization is employed. The coating system comprises a YSZ-Al2O3-CeO2 composite layer applied via plasma spraying, while CeO2 nanoparticles (35-55 ppm) are dispersed in PSB-diesel blends using surfactant-assisted ultrasonication. Engine tests were conducted across biodiesel blends (B10-B30) and four load levels (50-100%). A feed-forward ANN with three input neurons, two hidden layers (optimized through grid search), and one output neuron was trained to predict SFC, BTE, CO, HC, NOx, and smoke with high accuracy (R-2 > 0.99). A user-defined RSM (L27) design was used for multi-response optimization, constrained to maximize BTE while minimizing SFC and emissions. The optimal conditions, B30, 52% load, and 50 ppm CeO2, resulted in reduced SFC (0.316 kg/kWh), enhanced BTE (24.87%), and substantial emission reductions. Novelty arises from the integration of non-edible pumpkin seed biodiesel, CeO2-enhanced nanofuels, a composite TBC, and a hybrid ANN-RSM predictive-optimization framework, which collectively demonstrate a viable pathway toward cleaner and more efficient CI engine operation.

This study evaluated the tensile behavior of Ti 6 A l 7Nb alloy at high temperatures and examined the resulting hardness, corrosion, wear, and microstructural responses of the post-deformed samples after cooling. Tensile tests showed that the yield and ultimate tensile strengths decreased with increasing deformation temperature, from 872.32 MPa to 218.6 MPa, while the strain at failure increased from 12.56% to 63.52%. Microstructural analysis revealed grain coarsening, an increase in beta-phase content, and dynamic recrystallization at higher deformation temperatures; the most severe deformation condition exhibited almost complete retention of the beta phase. The hardness of the post-deformed samples showed a non-linear trend, peaking at 312.35 H V at an intermediate deformation level (T3), then declining to 284.76 H V at the highest deformation level. Corrosion resistance improved after deformation; the intermediate deformation state demonstrated the lowest corrosion rate (12.35 mpy) and the highest charge transfer resistance (1125.3 Omega cm2). Wear resistance decreased with increasing deformation, corresponding to the highest friction coefficient and the greatest material loss. These findings indicate that the Ti-6Al-7Nb alloy, of particular interest for biomedical components such as femoral stems and dental implants, exhibits increased corrosion resistance but diminished wear resistance at elevated deformation temperatures.

Entrepreneurial creativity and innovation are central to the development of novel products, services, and business models, yet the cognitive mechanisms underlying these processes remain complex and multifaceted. This chapter explores how Artificial Intelligence (AI) can be leveraged to map, analyze, and enhance entrepreneurial cognitive processes, including divergent and convergent thinking, pattern recognition, and problem reframing. It examines theoretical frameworks such as associative theory, componential creativity theory, and dual-process models, and highlights AI tools-including machine learning, natural language processing, and generative algorithms-that provide insights into ideation patterns, innovation potential, and cognitive biases. Case studies illustrate practical applications in startups, accelerators, and investment evaluation. The chapter also addresses ethical considerations, limitations, and future research directions, emphasizing the synergy between human cognition and AI as a transformative driver of entrepreneurial creativity and innovation.