Current Issue
Volume 1, Issue 1 - 2025 (October 2025 )
Issue Details:
Volume 1 Issue 1 (October 2025)Issue Description:
Welcome to the 2025 issue of Future Edge: Journal of Progressive Research. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.
We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.
As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.
Dr. Yugesh A Kharche
Editor-in-Chief
Future Edge: Journal of Progressive Research
Articles in This Issue
Advancements in Non-Conventional Manufacturing: Process Optimization and Applications
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.
Contributors:
Sustainable Laser Machining of AL6061 Alloy: A Multi-Objective Optimization Study
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.
Contributors:
Modeling and Simulation of Electric Vehicle Drive System using MATLAB/Simulink
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.
Contributors:
Revolutionizing Telehealth with Nanosensors: Transforming Diagnosis, Monitoring, and Remote Care
Nanosensors are redefining telehealth by combining advanced nanotechnology with computational intelligence to enable real-time monitoring, precise diagnosis and automated treatment support. Their integration with Internet of Medical Things (IoMT) architecture allow seamless collection, transmission and analysis of physiological data through cloud platform and edge computing system. These networks helps continuous remote patient monitoring, early anomaly detection using AI-driven analysis and reduced dependence on hospital-centric care model. The implementation of nanosensors in teleICU and hospital- at-home frameworks also addresses scalability and resource optimization in healthcare systems. However, challenges such as secure data transmission, interoperability between heterogeneous devices, fault tolerance and privacy protection must be resolved to ensure reliable deployment. This paper presents a comprehensive review of nanosensor technologies from computational perspective, detailing their communication protocols, data processing pipelines and cybersecurity requirement. Future research direction emphasize energy-efficient sensor design, federated learning for distributed analytics and blockchain-enabled security model for trustworthy remote healthcare ecosystems.
Contributors:
Review and Perspectives on Nature-Inspired Computational Algorithms
The integration of artificial intelligence into daily life is increasingly pervasive and unavoidable. In this expansive domain, nature-inspired algorithms play a crucial role in multiparameter optimization, with applications spreading across numerous fields. Optimization is essential in various sectors, including engineering, business operations, and industrial design. Bio-inspired computing serves as a comprehensive approach that intersects computer science, mathematics, and biology. In recent years, computer simulations have emerged as vital instruments for resolving optimization challenges using diverse and efficient search algorithms. Bioinspired computing optimization algorithms represent a growing field that draws upon the principles and inspirations derived from biological evolution to formulate novel and robust competitive methodologies. In recent years, these bio-inspired algorithms have gained recognition in the realm of machine learning for their capability to identify optimal solutions to complex scientific and engineering challenges. To overcome the limitations associated with traditional optimization methods, current trends favor the application of bio-inspired algorithms as a promising approach for addressing intricate optimization issues. This paper offers an examination of select nature-inspired algorithms alongside their potential applications.
Contributors:
Detrimental Characteristics of Wastewater
Identifying many unfavourable properties of wastewater and its effects on the environment and public health is crucial. Sewage is a complex mixture of effluents from industrial processes, domestic usage, and agriculture, and it poses a terrible risk of holding infectious agents, heavy metals, plastics microbeads, and toxic chemicals. Such pollutants cause water pollution and promote rapid growth of algae and aquatic plant life at the same time slowing water circulation, affecting aquatic life and causing eutrophication suffering from waterborne diseases, and other chronic sicknesses brought about by chemicals. New risks such as AMR genes and endocrine disruptors add to the picture making it even more complex. In this work, the chemical, biological, and physical properties of the wastewater are discussed with much emphasis on their implications for the environment and health. Besides, it analyzes generic risk management measures, such as the technological developments of treatment processes, efficient legislative mechanisms, and public involvement, and provides recommendations. The outcomes highlighted by the findings suggest that significant enhancements of both, effective interventions and guidance research, are needed for the sustainable improvement of the management of wastewaters as well as the protection of ecosystems and human beings.