Smart and Sustainable Manufacturing Systems
Scope & Guideline
Transforming industry through sustainable technology.
Introduction
Aims and Scopes
- Smart Manufacturing Technologies:
Exploration of advanced manufacturing technologies such as machine learning, digital twins, and automation systems that enhance production efficiency and adaptability. - Sustainability in Manufacturing:
Research on sustainable practices within manufacturing processes, including energy efficiency, waste reduction, and eco-friendly materials, to promote a greener industry. - Integration of Digital Solutions:
Focus on integrating digital solutions like IoT, cloud computing, and AI into manufacturing systems to enable real-time monitoring, predictive maintenance, and improved decision-making. - Data-Driven Approaches:
Utilization of data analytics and machine learning techniques to optimize manufacturing processes, enhance quality control, and improve operational performance. - Collaborative and Adaptive Systems:
Development of collaborative frameworks and adaptive manufacturing systems that respond to dynamic market demands and facilitate human-robot collaboration.
Trending and Emerging
- Machine Learning and AI Applications:
There is a significant increase in research focused on the application of machine learning and AI in manufacturing processes, particularly in predictive maintenance, quality control, and process optimization. - Digital Twins and Virtual Simulation:
The use of digital twins and virtual simulation technologies is rapidly emerging, allowing for real-time monitoring and optimization of manufacturing systems. - Sustainable Manufacturing Practices:
Research on specific sustainable manufacturing practices, such as decarbonization pathways and eco-efficient processes, is on the rise, reflecting a global push towards sustainability. - Cyber-Physical Systems and IoT Integration:
The integration of cyber-physical systems and IoT in manufacturing is trending, facilitating enhanced connectivity and data exchange across manufacturing environments. - Collaborative Robotics and Human-Robot Interaction:
Emerging studies are increasingly focusing on collaborative robotics and the optimization of human-robot interactions, highlighting the importance of workforce adaptation in smart manufacturing.
Declining or Waning
- Traditional Manufacturing Techniques:
There has been a noticeable decrease in research focused solely on traditional manufacturing techniques, as the emphasis shifts towards smart and automated processes that leverage modern technology. - Generalized Sustainability Concepts:
While sustainability remains a core theme, the exploration of generalized sustainability concepts without specific application to manufacturing contexts is becoming less frequent. - Manual Quality Control Methods:
The focus on manual quality control methods is waning as automated and machine learning-based quality assurance systems are increasingly prioritized. - Conventional Supply Chain Management Solutions:
Research on conventional supply chain management practices is declining as the integration of digital and smart technologies transforms traditional approaches. - Static Educational Frameworks:
The previous emphasis on static educational frameworks in manufacturing is diminishing, with a growing interest in adaptive, interdisciplinary curricula that better prepare students for modern manufacturing challenges.
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