Further expansion from Smart Manufacturing System (SMS) to Smart Manufacturing Implementation System (SMIS): industrial application scenarios and evaluation

Various countries in the world have issued policies on smart manufacturing and put forward relevant system architecture, including its hierarchy and component elements. However, from the perspective of implementation, it is necessary to further expand and study the relationship between the elements of the framework and the specific application scenarios. The focus of this study is to plan the specific implementation schemes from top-level planning for industrial practice in smart manufacturing on the basis of existing resources, elements, foundations, environment, standards, and norms. In this paper, industrial application scenarios for Smart Manufacturing Implementation System (SMIS) are deduced through a framework of SMIS. Then, an evaluation method for SMIS using Fuzzy DEMATEL is proposed. This study can provide guidance for enterprises to implement the scheme and steps of smart manufacturing. In addition, the industrial scenarios of SMIS proposed in this paper can provide reference for enterprises’ top-level planning.

[1]  C. B. Besant.  Rolls-Royce-Imperial College Teaching Company programme , 1988 .

[2]  Ruey-Shun Chen,et al.  Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology , 2009, Expert Syst. Appl..

[3]  Yannis Charalabidis,et al.  Transforming traditional production system transactions to interoperable eBusiness-aware systems with the use of generic process models , 2010 .

[4]  M. I. Lomakin,et al.  Multimodel Processing of Measurement Data in Intelligent Systems for Predicting the Reliability of Spaceborne Equipment , 2014 .

[5]  Mike P. Papazoglou,et al.  A Reference Architecture and Knowledge-Based Structures for Smart Manufacturing Networks , 2015, IEEE Software.

[6]  Michael Baldea,et al.  Smart Manufacturing Approach for Efficient Operation of Industrial Steam-Methane Reformers , 2015 .

[7]  Mike P. Papazoglou,et al.  A Reference Architecture and Knowledge-Based Structures for Smart Manufacturing Networks , 2015 .

[8]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[9]  George Q. Huang,et al.  IoT-based real-time production logistics synchronization system under smart cloud manufacturing , 2016 .

[10]  Ashutosh Nayak,et al.  Resource sharing in cyber-physical systems: modelling framework and case studies , 2016 .

[11]  Lusheng Wang,et al.  An Analytical Framework for Multilayer Partial Frequency Reuse Scheme Design in Mobile Communication Systems , 2016, IEEE Transactions on Vehicular Technology.

[12]  Jegannathan Kenthorai Raman,et al.  Life cycle assessment of algae biodiesel and its co-products , 2016 .

[13]  Mehdi Savaghebi,et al.  Flexible System Integration and Advanced Hierarchical Control Architectures in the Microgrid Research Laboratory of Aalborg University , 2016, IEEE Transactions on Industry Applications.

[14]  F. Richard Yu,et al.  Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges , 2017, IEEE Communications Surveys & Tutorials.

[15]  Ray Y. Zhong,et al.  Integrating RFID and BIM technologies for mitigating risks and improving schedule performance of prefabricated house construction , 2017 .

[16]  Richardson Ribeiro,et al.  Modeling and control of flexible context-dependent manufacturing systems , 2017, Inf. Sci..

[17]  Toly Chen,et al.  Feasibility Evaluation and Optimization of a Smart Manufacturing System Based on 3D Printing: A Review , 2017, Int. J. Intell. Syst..

[18]  Charles E. Dickerson,et al.  Dynamic Production System Identification for Smart Manufacturing Systems. , 2018, Journal of manufacturing systems.

[19]  Hideki Kobayashi,et al.  A simulation methodology for a system of product life cycle systems , 2018, Adv. Eng. Informatics.

[20]  Yuan Chang,et al.  A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios , 2018, The International Journal of Advanced Manufacturing Technology.

[21]  Marko Mladineo,et al.  Selecting manufacturing partners in push and pull-type smart collaborative networks , 2018, Adv. Eng. Informatics.

[22]  Yingfeng Zhang,et al.  Coloured Petri net-based active sensing system of real-time and multi-source manufacturing information for smart factory , 2018 .

[23]  Mauro Onori,et al.  BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors , 2018, J. Intell. Manuf..

[24]  Albert T. Jones,et al.  A hybrid simulation-based assessment framework of smart manufacturing systems , 2018, Int. J. Comput. Integr. Manuf..

[25]  Biswajit Sarkar,et al.  Effect of inspection performance in smart manufacturing system based on human quality control system , 2018 .

[26]  G. L. Cascella,et al.  Evaluating the effectiveness of spatial augmented reality in smart manufacturing: a solution for manual working stations , 2017, The International Journal of Advanced Manufacturing Technology.

[27]  Manchun Li,et al.  Using big data to track marine oil transportation along the 21st-century Maritime Silk Road , 2018, Science China Technological Sciences.

[28]  C. Robert Kenley,et al.  Reference architectures for smart manufacturing: A critical review , 2018, Journal of Manufacturing Systems.

[29]  Runliang Dou,et al.  Foreword: Smart manufacturing, innovative product and service design to empower Industry 4.0 , 2018, Comput. Ind. Eng..

[30]  A. Kusiak.  Smart manufacturing , 2018, Int. J. Prod. Res..

[31]  Ray Y. Zhong,et al.  Extending augmented Lagrangian coordination for the optimal configuration of cloud-based smart manufacturing services with production capacity constraint , 2019, Robotics Comput. Integr. Manuf..

[32]  Peihuang Lou,et al.  A general architecture of a 3D visualization system for shop floor management , 2017, Journal of Intelligent Manufacturing.

[33]  Wernher Behrendt,et al.  An open source approach to the design and implementation of Digital Twins for Smart Manufacturing , 2019, Int. J. Comput. Integr. Manuf..

[34]  Andrew Kusiak.  Editorial: Intelligent manufacturing: bridging two centuries , 2019, J. Intell. Manuf..

[35]  Dao Yin,et al.  An overall framework and subsystems for smart manufacturing integrated system (SMIS) from multi-layers based on multi-perspectives , 2019, The International Journal of Advanced Manufacturing Technology.

[36]  Duck Bong Kim,et al.  An approach for composing predictive models from disparate knowledge sources in smart manufacturing environments , 2017, Journal of Intelligent Manufacturing.

[37]  Xin Lu,et al.  Fog Computing and Convolutional Neural Network Enabled Prognosis for Machining Process Optimization , 2019, Springer Series in Advanced Manufacturing.

[38]  Seung Ki Moon,et al.  Eco-modular product architecture identification and assessment for product recovery , 2019, J. Intell. Manuf..

[39]  Bongju Jeong,et al.  Tactical supply planning in smart manufacturing supply chain , 2019, Robotics and Computer-Integrated Manufacturing.

[40]  Huixia Liu,et al.  Two-stage design method of robust deadlock control for automated manufacturing systems with a type of unreliable resources , 2019, Inf. Sci..

[41]  Milton Borsato,et al.  Method for digital evaluation of existing production systems adequacy to changes in product engineering in the context of the automotive industry , 2019, Adv. Eng. Informatics.

[42]  Libin Zhang,et al.  An embedded self-adapting network service framework for networked manufacturing system , 2016, Journal of Intelligent Manufacturing.

[43]  Xianyu Zhang,et al.  Reference subsystems for Smart Manufacturing Collaborative System (SMCS) from multi-processes, multi-intersections and multi-operators , 2019, Enterp. Inf. Syst..

[44]  Abdulrahman Al-Ahmari,et al.  Robust deadlock control for automated manufacturing systems based on elementary siphon theory , 2020, Inf. Sci..

[45]  Muhammad Rizwan Asghar,et al.  Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing , 2020 .

[46]  Sinan Q. Salih,et al.  Internet of things assisted condition‐based support for smart manufacturing industry using learning technique , 2020, Comput. Intell..

[47]  George Q. Huang,et al.  IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park , 2020, Adv. Eng. Informatics.

[48]  Angappa Gunasekaran,et al.  A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation , 2020, International Journal of Production Economics.

[49]  Dao Yin,et al.  Application of industrial big data for smart manufacturing in product service system based on system engineering using fuzzy DEMATEL , 2020 .

[50]  Nei Kato,et al.  Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective , 2020, IEEE Communications Surveys & Tutorials.

[51]  Zhenyu Liu,et al.  Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network , 2020 .

[52]  Kai Chen,et al.  Formal modelling of a sheet metal smart manufacturing system by using Petri nets and first-order predicate logic , 2020, J. Intell. Manuf..

[53]  Xinguo Ming,et al.  An implementation for Smart Manufacturing Information System (SMIS) from an industrial practice survey , 2020, Comput. Ind. Eng..