Recent Advances in Artificial Intelligence and Automation for Remanufacturing
摘要截稿:
全文截稿: 2018-07-01
影响因子: 5.057
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 1区
• 小类 : 计算机:跨学科应用 - 1区
• 小类 : 工程:制造 - 1区
• 小类 : 机器人学 - 1区
Overview
To cope with the growing concerns of limited natural resources, sustainability, and the increasingly stringent legislation on industrial pollution and other environmental issues, remanufacturing has recently emerged as a viable approach of international importance in the US, China and Europe to drive sustainable manufacturing, promote conservation and more comprehensive utilization of energy and resources, and drive the business and society to embrace circular economy. Due to the arrival of Industry 4.0, Internet of Things, cyber-physical systems, cloud manufacturing, and so on, remanufacturing is in the process of undergoing a significant transformation to become more intelligent and automated. More strikingly, various artificial intelligence techniques, machine learning algorithms, and big data analytics are being researched and deployed into remanufacturing context, e.g., design for remanufacturing, advanced remanufacturing process, robotics in remanufacturing, critical failure prediction, inventory forecasting, resilient remanufacturing networks, closed-loop supply chain management, etc.
This special issue will focus publishing original research works that advance artificial intelligence and automation in the remanufacturing field, including service and maintenance, from various aspects that tackle product, process and system issues in remanufacturing. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges and opportunities facing. It also welcomes studies that stimulate the research discussion of moving towards intelligent remanufacturing in a particular industrial sector.
Potential topics include, but are not limited to:
• Data-driven approaches to design for remanufacturing
• Big data analytics for in-use and service data exploitation
• Machine learning in critical failure (remaining life) prediction
• Simulation-based process design for remanufacturing
• Robotics and FMS for remanufacturing
• Scale-up methodologies for novel remanufacturing process
• Dynamic modeling and control for remanufacturing machinery under uncertainties
• Remanufacturing with precision, efficiency, and reliability
• Data-driven human factor study in remanufacturing
• Data mining for closed-loop supply chain performance improvement
• AI-based intelligent diagnosis and conditional monitoring in remanufacturing