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.

The surface preparation of shiny stainless steels is a must for applying esthetic paints, effective functional plasma spray coating, laser cladding, welding, etc., applications. The current work aims for effective surface roughening and erosion MRR of SS 304 work surface using SiC abrasive jet erosion and optimization of the process parameters. The response surface approach is used to design and conduct the studies using the Box–Behnken design method. The surface topography of the eroded surfaces is examined by a 2D profilometer, 3D profilometer, and scanning electron microscope (SEM). The abrasive grit size and working gas pressure greatly affect the surface roughness of SS 304 samples. The influence of the process parameters on the variation of these topographical features is analyzed and confirmed. The working jet pressure is seen to significantly impact erosion MRR. The lower working gas pressure shows a typical influence on R_a (surface preparation) and as pressure increases, erosion MRR rises, and the surface preparation mode shifts to the erosion metal removal/cutting zone. The quality of SS 304 surface prepared from SiC abrasive jet impact is characterized by 3D profilometry.

The main objective of this experimental investigation is to examine favourable machining conditions by utilising fewer resources of machining industries for the techno-economical and ecological benefits. The machining operations are performed in turning SS 304 using coated carbide tool inserts under dry, water-soluble cutting fluid solution in the form of flood cooling and small-quantity lubrication (SQL) conditions by employing a newly formed micro-jet for a comparative classical chips study and analysis. The machining experiments are conducted in turning by a 25 kW precision CNC lathe with a special arrangement of micro-jets into the machining zone. Machining speeds and feed rates are varied under dry, micro-jet, and flood cooling conditions and their effects are studied on the type of chips and their morphology, chip reduction coefficient (ξ), and chip shear plane distance (d). The effect of machining environments on tool health conditions (such as BUEs, tool-edge chipping, and edge breaking) is examined for the inferences. In the range of low-speed machining (less than 600 m/min), metal cutting seems easier in flood cooling conditions, but it imposes more unfavourable effects (such as edge chipping and edge breaking) on the ceramic cutting tool’s health. On the other hand, the dry machining condition shows a favourable performance for a ceramic cutting tool. The optimum machining condition is found in the micro-jet SQL by the analysis of experimental data and observation results for the tool and work combination. The analysis of the results is carried out by the response surface methodology (RSM) and artificial neural network (ANN). The ANN model is found to be more accurate than RSM. The aspects of effective green machining are emphasised.

This investigation explores the application of supervised machine learning regression approaches to predict various responses, including penetration, bead width, bead height, hardness, ultimate tensile strength, and percentage elongation in autogenous TIG-, A-TIG-, and TIG-welded joints of SS304H, which is considered as an advanced high-temperature resistant material. The machine learning (ML) models were constructed based on the data gathered from 50 experimental runs, considering eight key input variables: gas flow rate, torch angle, filler material, welding pass, flux application, root gap, arc gap, and heat input. A total of 80% of the collected dataset was used for training the models, while the remaining 20% was reserved for testing their performance. Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. Among these, the XGBoost model has demonstrated the highest predictive capability, achieving R² scores of 0.886 for penetration, 0.926 for width, 0.915 for weld bead height, 0.868 for hardness, 0.906 for ultimate tensile strength, and 0.926 for percentage elongation, along with the lowest values of RMSE, MAE, and MSE across all responses. The outcomes establish that machine learning models, particularly XGBoost, can accurately predict welding characteristics, marking a significant advancement in the optimization of TIG welding parameters. Consequently, integrating such predictive models can substantially enhance the precision, reliability, and overall efficiency of welding processes.

Aluminum spot welding is extensively applied in automotive, aerospace, and rail sectors due to its favorable strength-to-weight ratio. While resistance spot welding (RSW) has been the traditional method, its high residual stresses, electrode wear, and limited performance with high-strength aluminum alloys have driven interest toward alternative techniques. Friction stir spot welding (FSSW) offers significant advantages over RSW, linear friction welding (LFW), and hybrid processes, including solid-state joining that minimizes porosity, lower energy consumption, and the elimination of consumable electrodes. Compared to LFW, FSSW requires simpler fixturing and is more adaptable for localized repairs, while offering superior joint surface quality over hybrid laser-assisted methods. Despite these advantages, gaps remain in understanding the influence of process parameters on heat generation, microstructural evolution, and mechanical performance. This review consolidates advancements in tool design, thermal characterization, and weld property for aluminum alloys. It presents comparative insights into temperature distribution, weld strength, hardness variation, and metallurgical transformations reported across studies. By critically synthesizing the earlier works, this work identifies knowledge gaps and potential design improvements, aiming to support the development of more efficient and robust FSSW processes for industrial application.