Future Edge Journal of Progressive Research

Non-conventional manufacturing has emerged as a transformative approach in modern industrial production, enabling the processing of advanced materials and complex geometries beyond the capabilities of traditional machining methods. This study presents a comprehensive overview of key non-conventional manufacturing techniques, including Wire Arc Additive Manufacturing (WAAM), Wire Electrical Discharge Machining (WEDM), and Directed Energy Deposition (DED), with a focus on their process principles, optimization strategies, and industrial applications. The research highlights the importance of parameter optimization using statistical and computational techniques such as Taguchi methods, Grey Relational Analysis, and heuristic algorithms to enhance performance metrics like material removal rate, surface quality, and mechanical properties. Additionally, advancements such as nano-material mixed dielectric fluids in WEDM and real-time monitoring systems in DED are discussed for improving process efficiency and quality control. The study also examines material characterization, sustainability aspects, and the integration of digital technologies like Digital Twins and artificial intelligence in manufacturing systems. Despite challenges related to defect formation, material compatibility, and scalability, non-conventional manufacturing continues to drive innovation across aerospace, automotive, biomedical, and energy sectors. The findings emphasize the critical role of intelligent optimization, hybrid manufacturing, and sustainable practices in shaping the future of advanced manufacturing.

Laser machining has become a critical process in contemporary manufacturing, with higher precision, less tool wear, and the ability to machine complex geometries. AL6061 alloy, with its strength-to-weight ratio, corrosion resistance, and machinability, is extensively used in aerospace, automotive, and structural applications. Nevertheless, conventional machining of the alloy tends to be energy-hungry and causes extensive tool wear. While laser machining offers a non-contact option, it too can be energy-intensive and thermally damaging if not optimized correctly. The present study examines the sustainability of AL6061 alloy laser machining through optimization of some of the most critical process parameters, namely laser power, scanning speed, and pulse frequency, using a multiobjective strategy. Experimental investigation used a Taguchi L9 orthogonal array and recorded responses like surface roughness, material removal rate (MRR), and energy utilization. Data analysis involved the use of Grey Relational Analysis (GRA), allowing for the consideration of several conflicting objectives at one time. The experiments determined the best parameter setting of 18W laser power, 150 mm/s scan speed, and 60 kHz pulse rate. At these parameters, surface roughness decreased by 50% (from 6.2 µm to 3.1 µm), MRR rose by 27%, and energy efficiency rose by 19%. Statistical validation using ANOVA established the significance of the chosen parameters for sustainable machining results. This research bridges an important research gap by combining performance measures with environmental factors, providing a blueprint for industries looking to implement sustainable laser machining techniques. The results not only advance energy-efficient manufacturing but also demonstrate the potential of intelligent optimization methods in green manufacturing systems. Future research can investigate AI-based adaptive control, real-time feedback, and full lifecycle analysis to further enhance the sustainability of laser machining processes.

This paper presents the modeling and simulation of an Electric Vehicle (EV) drive system using MATLAB/Simulink. The proposed EV model includes a lithium-ion battery, an inverter, a Brushless DC (BLDC) motor, and a vehicle dynamics block. The simulation analyzes motor performance under various conditions such as acceleration, regenerative braking, and variable load. The results demonstrate effective torque-speed response and power efficiency of the system. This model can be a foundation for future development of intelligent control algorithms for electric mobility.