A Teaching-Oriented Approach to Post-Cooling Mechanical Evaluation of Welded Joints by Students

M. Jaya Ramadhan, Kartia Ramdania — Mon, 23 Sep 2024 00:00:00 +0700

This study explores a teaching-oriented approach to post-cooling mechanical evaluation of welded joints, aiming to integrate quantitative metallurgical analysis with experiential learning in engineering education. The research sought to enhance students’ understanding of the relationship between thermal processes and mechanical behavior through direct involvement in experimental procedures. A quantitative method was employed using welded steel specimens subjected to two cooling conditionsnatural air cooling and controlled air jet cooling. Mechanical properties, including tensile strength, hardness, and grain size, were measured, and the data were analyzed statistically using analysis of variance and correlation tests to determine the influence of cooling rate on material performance. The results showed that controlled cooling produced finer grain structures and led to a 6–8% improvement in tensile strength and hardness compared to natural cooling. These findings confirm classical metallurgical theories regarding the Hall–Petch relationship and microstructural strengthening mechanisms. Beyond material analysis, the study demonstrated that student participation in quantitative experimentation fosters critical thinking, data literacy, and scientific reasoning. This integrated model effectively bridges the gap between theoretical instruction and professional engineering practice. The study concludes that combining quantitative evaluation with a teaching-oriented framework provides a replicable model for modern engineering curricula, promoting both technical competence and cognitive development. Future studies are encouraged to expand this model to diverse materials and thermal conditions to strengthen its generalizability.

Challenges and Opportunities of Artificial Intelligence in Industrial Automation

Muh. Rochman — Wed, 25 Sep 2024 00:00:00 +0700

Artificial intelligence (AI) has become a key driver of industrial automation, reshaping production environments and redefining operational capabilities across global sectors. This study aims to examine the challenges and opportunities associated with AI implementation in industrial automation, focusing on efficiency gains, workforce implications, and governance requirements. A mixed qualitative synthesis and technology performance review were employed to evaluate AI readiness, adoption outcomes, and strategic enablers based on recent industrial case evidence and established scholarly frameworks. The results show that AI significantly enhances productivity, minimizes operational errors, and supports intelligent decision-making through automation of complex tasks. Moreover, the integration of AI fosters predictive maintenance, production flexibility, and accelerated innovation cycles. However, adoption is constrained by high initial investment, talent shortages, resistance to organizational change, ethical concerns, and cybersecurity risks. These challenges suggest that successful AI-driven automation depends not only on advanced technical infrastructures but also on coordinated policy support, workforce development, and strong digital governance. The study concludes that industrial transformation through AI represents both a technological opportunity and a socio-economic challenge, requiring holistic strategies to ensure its responsible and equitable advancement. The implications provide a foundation for future research exploring scalable models of AI adoption and long-term sustainability in industry.

TechVista Journal of Emerging Information Systems

A Teaching-Oriented Approach to Post-Cooling Mechanical Evaluation of Welded Joints by Students

Challenges and Opportunities of Artificial Intelligence in Industrial Automation