Inconel 718’s exceptional strength and corrosion resistance make it a versatile superalloy widely adopted in diverse industries, attesting to its reliability. Electrochemical machining (ECM) further enhances its suitability for intricate part fabrication, ensuring complex shapes, dimensional accuracy, stress-free results, and minimal thermal damage. Thus, this research endeavors to conduct a novel investigation into the electrochemical machining (ECM) of the superalloy Inconel 718. The study focuses on unraveling the intricate influence of key input process parameters—namely, electrolytic concentration, tool feed rate, and voltage—on critical response variables such as surface roughness (SR), material removal rate (MRR), and radial overcut (RO) in the machining process. The powerful tool, response surface methodology (RSM), is used for understanding and optimizing complex systems by developing mathematical models that describe the relationships between input and response variables. Under a 95% confidence level, analysis of variance (ANOVA) suggests that electrolyte concentration, voltage, and tool feed rate are the most important factors influencing the response characteristics. Moreover, the incorporation of ANN modeling and the MOGA-ANN optimization algorithm introduces a novel and comprehensive approach to determining the optimal machining parameters. It considers multiple objectives simultaneously, considering the trade-offs between them, and provides a set of solutions that achieve the desired balance between MRR, SR, and RO. Confirmation experiments are carried out, and the absolute percentage errors between experimental and optimized values are assessed. The detailed surface topography and elemental mapping were performed using a scanning electron microscope (SEM). The nano/micro particles of Inconel 718 metal powder, obtained from ECM sludge/cakes, along with the released hydrogen byproducts, offer promising opportunities for recycling and various applications. These materials can be effectively utilized in powder metallurgy products, leading to enhanced cost efficiency.

High specific strength and corrosion resistance are the important characteristics of Ti–6Al–4V. Relatively soft and inferior wear resistive properties of Ti–6Al–4V alloy restrict further scope of exotic applications. In the present study, attempts were carried out to improve tribological properties of Ti–6Al–4V surface by preplacing B₄C, hBN and Ti powders over the substrate and irradiating it with a fibre laser in an argon shroud. Clad layers, consisting of in situ multicomponents such as TiB₂, TiN, TiO₂, VN, V₂O₃, C, etc., were developed in the coating matrix. The effects of change in laser scanning speed on coating properties were studied extensively. Microhardness of the tailored surface was improved up to 2200 HV_0·05 as compared to a microhardness value of 350 HV_0·05 of the untreated Ti–6Al–4V substrate. The wear performance of the hard coating was tested with universal wear testing machine and analysed.

The wear loss and frictional characteristics of magnesium-based hybrid composites reinforced with boron carbide (B₄C) particles and graphite filler were the main subjects of the investigation. Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. Additionally, feature importance analysis indicated that the graphite weight percentage was the most significant influence in predicting the coefficient of friction and wear loss characteristics.