A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance

Abstract Manufacturing, through the Industry 4.0 concept, is moving to the next phase; that of digitalization. Industry 4.0 enables the transition of traditional manufacturing systems to modern digitalized ones, generating significant economic opportunities by reshaping of industry. This procedure requires high-performance processes and flexible production systems. The adoption of the Internet of Things (IoT) in manufacturing will enable effective and adaptive planning and control of production systems. Towards that end, the proposed work presents a cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance. The proposed system demonstrated that it is possible to deploy a cost-effective and reliable real-time data collection, processing, and analysis from the shop floor. It also demonstrates that such collected data can be used in an adaptive decision making system, which includes a multi-criteria decision-making algorithm and a condition-based maintenance strategy aiming to improve factory performances when compared to traditional approaches. The proposed system consists of different modules (monitoring, adaptive scheduling, condition-based maintenance) interconnected through the cloud-based platform, enabled by communication protocols under the Industry 4.0 and IoT paradigms. The proposed system is applied and validated in a real-case study from a high-precision mold-making industry.

[1]  Nebil Buyurgan,et al.  Application of the analytical hierarchy process for real-time scheduling and part routing in advanced manufacturing systems , 2008 .

[2]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[3]  Nikolaos Tapoglou,et al.  Cloud based platform for optimal machining parameter selection based on function blocks and real time monitoring , 2015 .

[4]  Dimitris Mourtzis,et al.  A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring☆ , 2016 .

[5]  J. C. Kappatou,et al.  Taking advantage of the induction motor inherent eccentricity aiming to discriminate the broken bar fault from load oscillations , 2014, 2014 International Conference on Electrical Machines (ICEM).

[6]  Michael J. Pont,et al.  Application of Dempster-Shafer theory in condition monitoring applications: a case study , 2001, Pattern Recognit. Lett..

[7]  Botond Kádár,et al.  Solution Approaches to Real-time Control of Customized Mass Production , 2007 .

[8]  Weili Wu,et al.  Wireless Sensor Networks and Applications , 2008 .

[9]  Stefano Nativi,et al.  Big Data challenges in building the Global Earth Observation System of Systems , 2015, Environ. Model. Softw..

[10]  George Chryssolouris,et al.  Dynamic scheduling of manufacturing job shops using extreme value theory , 2000 .

[11]  Lihui Wang,et al.  Cloud-enhanced predictive maintenance , 2018 .

[12]  Jin Wang,et al.  Game Theory Based Real‐Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing , 2017, Int. J. Intell. Syst..

[13]  Jeff Morgan,et al.  The Cyber Physical Implementation of Cloud Manufactuirng Monitoring Systems , 2015 .

[14]  Masahiko Mori,et al.  Development of remote monitoring and maintenance system for machine tools , 2008 .

[15]  Yu Zheng,et al.  Methodologies for Cross-Domain Data Fusion: An Overview , 2015, IEEE Transactions on Big Data.

[16]  David Taniar,et al.  Sensor data management in the cloud: Data storage, data ingestion, and data retrieval , 2018, Concurr. Comput. Pract. Exp..

[17]  Lihui Wang,et al.  Cloud-based adaptive process planning considering availability and capabilities of machine tools , 2016 .

[18]  Timo Oksanen,et al.  Aggregating OPC UA servers for monitoring manufacturing systems and mobile work machines , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[19]  Lihui Wang,et al.  Current status and advancement of cyber-physical systems in manufacturing , 2015 .

[20]  Federico Castanedo,et al.  A Review of Data Fusion Techniques , 2013, TheScientificWorldJournal.

[21]  Subramaniam Ganesan,et al.  Condition based maintenance: a survey , 2012 .

[22]  Dimitris Mourtzis,et al.  Development of methods and tools for the design and operation of manufacturing networks for mass customisation , 2016 .

[23]  Athanasios V. Vasilakos,et al.  The role of big data analytics in Internet of Things , 2017, Comput. Networks.

[24]  Willy Herroelen,et al.  Project scheduling under uncertainty: Survey and research potentials , 2005, Eur. J. Oper. Res..

[25]  Jay Lee,et al.  Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4.0 Environment , 2015 .

[26]  M A Sinclair,et al.  Global drivers, sustainable manufacturing and systems ergonomics. , 2015, Applied ergonomics.

[27]  Robert X. Gao,et al.  Cloud-enabled prognosis for manufacturing , 2015 .

[28]  Maria Manuela Cruz-Cunha,et al.  E-Business Issues, Challenges and Opportunities for SMEs: Driving Competitiveness , 2010 .

[29]  George Q. Huang,et al.  Wireless manufacturing: a literature review, recent developments, and case studies , 2009 .

[30]  Anjali Awasthi,et al.  Using AHP and Dempster-Shafer theory for evaluating sustainable transport solutions , 2011, Environ. Model. Softw..

[31]  Dimitris Mourtzis,et al.  A knowledge-based social networking app for collaborative problem-solving in manufacturing , 2016 .

[32]  Dimitris Mourtzis,et al.  Cloud-Based Adaptive Shop-Floor Scheduling Considering Machine Tool Availability , 2015 .

[33]  Xun Xu,et al.  From cloud computing to cloud manufacturing , 2012 .

[34]  Xi Wang,et al.  Dynamic Multiple-Period Reconfiguration of Real-Time Scheduling Based on Timed DES Supervisory Control , 2016, IEEE Transactions on Industrial Informatics.

[35]  Sotiris Makris,et al.  A web based tool for dynamic job rotation scheduling using multiple criteria , 2011 .

[36]  Dazhong Wu,et al.  Cloud manufacturing: Strategic vision and state-of-the-art☆ , 2013 .

[37]  In Lee,et al.  The Internet of Things (IoT): Applications, investments, and challenges for enterprises , 2015 .

[38]  George Chryssolouris,et al.  Manufacturing Systems: Theory and Practice , 1992 .

[39]  Dimitris Mourtzis,et al.  Cloud-based cyber-physical systems and quality of services , 2016 .

[40]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[41]  George Chryssolouris,et al.  An approach for allocating manufacturing resources to production tasks , 1991 .

[42]  Soundar R. T. Kumara,et al.  Cyber-physical systems in manufacturing , 2016 .

[43]  Fei Tao,et al.  Real-Time Information-Driven Production Scheduling System , 2017 .

[44]  Pravin Varaiya,et al.  Real-Time Scheduling of Distributed Resources , 2013, IEEE Transactions on Smart Grid.

[45]  Ying Yan,et al.  A two-layer dynamic scheduling method for minimising the earliness and tardiness of a re-entrant production line , 2012 .

[46]  Paul K. Wright,et al.  Cyber-physical product manufacturing , 2014 .

[47]  George Chryssolouris,et al.  On a Predictive Maintenance Platform for Production Systems , 2012 .

[48]  A. Valente,et al.  Development of multi-level adaptive control and scheduling solutions for shop-floor automation in reconfigurable manufacturing systems , 2011 .