Journal Information
Journal of Manufacturing Systems
http://www.journals.elsevier.com/journal-of-manufacturing-systems/
Impact Factor:
3.642
Publisher:
ELSEVIER
ISSN:
0278-6125
Viewed:
4597
Tracked:
2

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Call For Papers
The Journal of Manufacturing Systems (JMSY) publishes state-of-the-art fundamental and applied research in manufacturing at the systems level. Manufacturing systems are comprised of products, equipment, people, information, control and support functions for the economical and competitive development, production, delivery and total life cycle of products to satisfy market and societal needs.

Pertinent to the journal is work studying emerging manufacturing systems from the equipment level to distributed enterprises, and production challenges within and across various scales, including nano, micro and macro-scale manufacturing.
Papers relevant to the scope of JMSY include, but are not limited to, the following areas:

• Manufacturing Strategy and Paradigms: flexible, reconfigurable and changeable manufacturing systems; rapid manufacturing, lean manufacturing, virtual enterprises.
• Manufacturing Systems Design and Operations: process planning, production planning and controls, modeling, simulation, virtual manufacturing.
• Sustainable Manufacturing: life cycle of products and systems, sustainable manufacturing, design for environments and sustainability.
• Quality Management: product and process quality, quality function deployment, quality by design, six sigma.
• Automation, Control Systems, Human-Machine Interaction: agent-based systems, distributed and integrated control systems, intelligent systems, emergent systems, reconfigurable control, robotics, collaborative robots and human-machine interactions.
• Product Development: product families, reverse engineering concept development, product design and integration with manufacturing systems, product life cycle.
• Supply Chain Management and Logistics: global supply chains, dynamic supply chains, modeling and optimization.
• Manufacturing Information Systems: Internet, Web-based systems, ERP (enterprise resource planning), automatic data capture, enterprise modeling.
• Micro and Nano Manufacturing Systems: systems issues related to microfluidics, nanoelectronics, nano systems, microelectromechanical systems (MEMS), nanomaterials, interconnects (nano to meso to macro); energy, chemical and biological devices.

Papers focused on novel manufacturing systems design, planning, modeling and control from the factory level to the extended enterprise and addressing the new global challenges are invited for the Journal of Manufacturing Systems. Papers addressing new and emerging related topics are also encouraged.
Last updated by Dou Sun in 2019-12-04
Special Issues
Special Issue on Prognostic and Health Management through Collaborative Maintenance
Submission Date: 2020-09-30

Maintenance approach has significantly changed over the last century, with a shift in emphasis from technological to techno-economic considerations. Today, maintenance strategies are applied in an integrated way depending on the criticality of assets and cost-effectiveness. These strategies include but are not limited to the following: corrective maintenance, preventive maintenance, predictive maintenance, proactive and passive maintenance, self-maintenance, smart maintenance, e-Maintenance and collaborative maintenance. The development of these strategies is ongoing, and new requirements are continually created with the increasing complexity of assets and operating conditions. In Industry 4.0, improved automation technology, including self-awareness and self-learning, is a must. Assets can already “talk” to each other or “learn” by themselves using the Internet of Things (IoT) frameworks, cyber-physical systems (CPSs), machine to machine communications, and industrial Internet, that is, the integration of complex physical machinery with networked sensors and software in a collaborative community. However, the knowledge of intelligent data analysis and the ability to create asset self-awareness and self-learning remain limited and many data are underutilized. These obstacles to the collaborative efficiency of assets limit moving closer to the ultimate goal of intelligent maintenance. Prognostic and Health Management (PHM) thorough the collaborative community will be needed for more effective and efficient maintenance, lower energy use and increase capacity, and more reliable and robust. Recently, there are several new trends in successfully development and implementation of such PHM programs: First, there is a spoken need of convergence of the Operational Technology (OT), Information Technology (IT) together with Engineering Technologies (ET); Second, purely data driven or physical model-driven approaches may not satisfy the situations of assets in reality; instead, the context-driven approaches are more and more acceptable, which will effectively use all relevant information (including on-board data, test data, equipment mechanisms, context data, etc.) for diagnosis, prognostics, and decision-making; Third, PHM programs are developed and implemented from component level to system level, and to System of system (SoS) level by using Product Lifetime Management (PLM) perspective. This special session solicits papers that present various PHM program through Collaborative Maintenance, with special focuses on: 1) technology that enables convergence of OT, IT and ET; 2) data-driven, model-based, hybrid-driven, as well as context-driven approaches; 3) studies cover from component to SoS level using PLM. Among them, techniques and approaches used, results obtained, and lessons learned can be included to share experience with this session. These include but not limited to: Sensor and data collection, data quality management, data processing, data fusion technologies for PHM; Taxonomy and FMECA analytics for PHM; Feature extraction methods for PHM; Diagnosis, incl., context definition and awareness, condition monitoring, anomaly detection; Prognostic, incl., severity analysis, prognostics accuracy analysis, prognostics uncertainty / prognostics horizon analysis; Advanced RAM4S approaches for PHM: Reliability, availability, maintainability, safety, security, supportability, sustainability;Nowcasting, forecasting and Remaining Useful life (RUL) prediction; Maintenance analytics and maintenance decision optimization; Maintenance digitalization, visualization, and servitization solutions for PHM applications.
Last updated by Dou Sun in 2019-12-04
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