Journal Article
15 October 2025
Current Issue
Volume 1, Issue 1 (2025)Read More
Featured Content
Journal Article
25 September 2025
Improving Formability of Low-Carbon Steel Shells through Deep Drawing: Experimental Analysis using SPSS
Deep drawing, a prevalent sheet metal forming technique, is beset by fracture, wrinkling, and earing. This research investigates punch velocity, die curvature, and lubrication to enhance the formability of low-carbon steel cups. These parameters were selected due to their direct influence on forming force, metal flow, and thickness distribution, which dictate product quality. This study examines the impact of speed, radius, and lubrication on formability. The pressure of the blank holder and the height of the die can also influence formability. Experiments designed by DOE approach were assessed using SPSS and validated by Bootstrapping. Speed was the primary element influencing forming force and thickness; however, die radius and lubrication had a greater impact on force. Utilizing lubrication (oil or grease) at a rate of 200 mm/min with a die radius of 6–8 mm diminished thinning and friction relative to dry conditions, producing optimal outcomes. The regression models demonstrated R² values of 64.3% for force and 78.7% for thickness, so confirming their validity. A thorough experimental validation demonstrates that in typical deep drawing of low-carbon steel, speed is the predominant factor, while lubrication enhances surface quality. This research enhances traditional deep drawing through statistically validated models, offering novel guidelines to augment manufacturing efficiency and product reliability. Prior research concentrated on blank-holder pressure or sophisticated shaping methodologies.Current Articles
Journal Article1 January 2025
TOWARDS A COMPANION ROBOT: A COGNITIVE AGENT MODEL OF THE DYNAMICS OF LONELINESS
Loneliness is a prevalent and global problem for all people, and it can adversely affect their quality of life. Many research investigations have confirmed the negative psychological impacts of loneliness on people’s the unwanted impact of loneliness. Yet, these interventions are missing the power of reasoning to predict the onset of loneliness. Consequently, this paper presented the work of developing a computational cognitive agent model of loneliness using a causal networking modeling approach by relying on discrepancy model as a benchmark to serve as analytics engine for a companion robot design. Loneliness determinants and their causal relationships were identified from the literature and formalized to construct the intended cognitive agent model. Furthermore, simulation analyses under various parameter settings were implemented to explore the causal relationships among the identified loneliness determinants and those simulations revealed similar behaviors or patterns to existing literature. The designed cognitive agent model was evaluated using both of mathematical analysis and automated logical analysis. These two evaluation approaches have proved the correctness of the designed model. The developed computational loneliness agent model with little tuning can serve as a core analytical engine for intelligent technologies such as robots to control and monitor the adverse effects of lonelinessJournal Article25 September 2025
Improving Formability of Low-Carbon Steel Shells through Deep Drawing: Experimental Analysis using SPSS
Deep drawing, a prevalent sheet metal forming technique, is beset by fracture, wrinkling, and earing. This research investigates punch velocity, die curvature, and lubrication to enhance the formability of low-carbon steel cups. These parameters were selected due to their direct influence on forming force, metal flow, and thickness distribution, which dictate product quality. This study examines the impact of speed, radius, and lubrication on formability. The pressure of the blank holder and the height of the die can also influence formability. Experiments designed by DOE approach were assessed using SPSS and validated by Bootstrapping. Speed was the primary element influencing forming force and thickness; however, die radius and lubrication had a greater impact on force. Utilizing lubrication (oil or grease) at a rate of 200 mm/min with a die radius of 6–8 mm diminished thinning and friction relative to dry conditions, producing optimal outcomes. The regression models demonstrated R² values of 64.3% for force and 78.7% for thickness, so confirming their validity. A thorough experimental validation demonstrates that in typical deep drawing of low-carbon steel, speed is the predominant factor, while lubrication enhances surface quality. This research enhances traditional deep drawing through statistically validated models, offering novel guidelines to augment manufacturing efficiency and product reliability. Prior research concentrated on blank-holder pressure or sophisticated shaping methodologies.Journal Article15 October 2025
Federated Learning for 6G Networks: A Comprehensive Review of Challenges, Techniques, and Future Directions
The convergence of Federated Learning (FL) and sixth-generation (6G) communication systems promise to revolutionize distributed intelligence by addressing emerging demands for data privacy, real-time processing, and massive connectivity. However, integrating FL within 6G introduces complex challenges ranging from heterogeneous data and devices to communication bottlenecks, energy constraints, and stringent security demands. This review provides a comprehensive examination of FL techniques and their applicability in terms of 6G communication models. The review also emphasizes how these technologies are used in real-world fields like healthcare, autonomous systems, and digital twins—areas where privacy, reliability, and latency are mission-critical. Unlike earlier surveys that treat FL and 6G as separate research tracks, this paper critically reviews their convergence, identifying how FL techniques must evolve to meet the architectural, functional, and regulatory demands of 6G systems. It discusses ongoing challenges and emerging directions such as quantum-safe protocols, interpretable federated learning, and energy-aware orchestration. By synthesizing cross disciplinary insights and mapping current gaps, this review aims to guide future research in developing robust, adaptive, and secure FL frameworks.Most Popular Articles
Journal Article
15 October 2025
Federated Learning for 6G Networks: A Comprehensive Review of Challenges, Techniques, and Future Directions
The convergence of Federated Learning (FL) and sixth-generation (6G) communication systems promise to revolutionize distributed intelligence by addressing emerging demands for data privacy, real-time processing, and massive connectivity. However, integrating FL within 6G introduces complex challenges ranging from heterogeneous data and devices to communication bottlenecks, energy constraints, and stringent security demands. This review provides a comprehensive examination of FL techniques and their applicability in terms of 6G communication models. The review also emphasizes how these technologies are used in real-world fields like healthcare, autonomous systems, and digital twins—areas where privacy, reliability, and latency are mission-critical. Unlike earlier surveys that treat FL and 6G as separate research tracks, this paper critically reviews their convergence, identifying how FL techniques must evolve to meet the architectural, functional, and regulatory demands of 6G systems. It discusses ongoing challenges and emerging directions such as quantum-safe protocols, interpretable federated learning, and energy-aware orchestration. By synthesizing cross disciplinary insights and mapping current gaps, this review aims to guide future research in developing robust, adaptive, and secure FL frameworks.Journal Article
25 September 2025
Improving Formability of Low-Carbon Steel Shells through Deep Drawing: Experimental Analysis using SPSS
Deep drawing, a prevalent sheet metal forming technique, is beset by fracture, wrinkling, and earing. This research investigates punch velocity, die curvature, and lubrication to enhance the formability of low-carbon steel cups. These parameters were selected due to their direct influence on forming force, metal flow, and thickness distribution, which dictate product quality. This study examines the impact of speed, radius, and lubrication on formability. The pressure of the blank holder and the height of the die can also influence formability. Experiments designed by DOE approach were assessed using SPSS and validated by Bootstrapping. Speed was the primary element influencing forming force and thickness; however, die radius and lubrication had a greater impact on force. Utilizing lubrication (oil or grease) at a rate of 200 mm/min with a die radius of 6–8 mm diminished thinning and friction relative to dry conditions, producing optimal outcomes. The regression models demonstrated R² values of 64.3% for force and 78.7% for thickness, so confirming their validity. A thorough experimental validation demonstrates that in typical deep drawing of low-carbon steel, speed is the predominant factor, while lubrication enhances surface quality. This research enhances traditional deep drawing through statistically validated models, offering novel guidelines to augment manufacturing efficiency and product reliability. Prior research concentrated on blank-holder pressure or sophisticated shaping methodologies.Journal Article
1 January 2025