Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies

The machining processes on the advanced machining workshop floor are becoming more sophisticated with the interdependent intrinsic processes, generation of ever-increasing in-process data and machining domain knowledge. To manage and utilize those above effectively, an industrial dataspace for machining workshop (IDMW) is presented with a three-layer framework. The IDMW architecture is Schema Centralized–Data Distributed, which relies on Process-Workpiece-Centric knowledge schema description and data storage in decentralized data silos. Subsequently, the pre-processing method for the data silos driven by RFID event graphical deduction model is elaborated to associate decentralized data with knowledge schema. Furthermore, through two industrial case studies, it is found that IDMW is effective in managing heterogeneous data, interconnecting the resource entities, handling domain knowledge, and thereby enabling machining operations control on the machining workshop floor particularly.

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

[2]  Qiang Liu,et al.  Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop , 2018, Journal of Ambient Intelligence and Humanized Computing.

[3]  F BabiceanuRadu,et al.  Big Data and virtualization for manufacturing cyber-physical systems , 2016 .

[4]  Paul Valckenaers,et al.  An Approach for the Integration of a Scheduling System and a Multi-Agent Manufacturing Execution System. towards a Collaborative Framework , 2012 .

[5]  Sami Kara,et al.  Manufacturing big data ecosystem: A systematic literature review , 2020, Robotics Comput. Integr. Manuf..

[6]  Pingyu Jiang,et al.  Manufacturing Knowledge Graph: A Connectivism to Answer Production Problems Query With Knowledge Reuse , 2019, IEEE Access.

[7]  Lida Xu,et al.  Big data for cyber physical systems in industry 4.0: a survey , 2019, Enterp. Inf. Syst..

[8]  K. Parthiban,et al.  An Efficient Architecture to Ensure Data Integrity in ERP Systems , 2019, 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS).

[9]  Peigen Li,et al.  Toward New-Generation Intelligent Manufacturing , 2018 .

[10]  WanJiafu,et al.  Towards smart factory for industry 4.0 , 2016 .

[11]  Kanet Katchasuwanmanee,et al.  Development of the Energy-smart Production Management system (e-ProMan): A Big Data driven approach, analysis and optimisation , 2016 .

[12]  Pingyu Jiang,et al.  Production events graphical deduction model enabled real-time production control system for smart job shop , 2018 .

[13]  Chao Liu,et al.  Web-based digital twin modeling and remote control of cyber-physical production systems , 2020, Robotics Comput. Integr. Manuf..

[14]  Remzi Seker,et al.  Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook , 2016, Comput. Ind..

[15]  Zhiqiang Wang,et al.  Effects of information technology alignment and information sharing on supply chain operational performance , 2013, Comput. Ind. Eng..

[16]  Xun Xu,et al.  Resource virtualization: A core technology for developing cyber-physical production systems , 2018 .

[17]  Norman W. Paton,et al.  Incrementally improving dataspaces based on user feedback , 2013, Inf. Syst..

[18]  Xun Xu,et al.  ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment , 2019, J. Intell. Manuf..

[19]  David Maier,et al.  From databases to dataspaces: a new abstraction for information management , 2005, SGMD.

[20]  Ling Chen,et al.  Practicability of Dataspace Systems , 2010, J. Digit. Content Technol. its Appl..

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

[22]  Steffen Lohmann,et al.  Ontology-based information modelling in the industrial data space , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[23]  Ray Y. Zhong,et al.  Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .

[24]  Xun Xu,et al.  A Knowledge Management System to Support Design for Additive Manufacturing Using Bayesian Networks , 2018 .

[25]  Evgeniy Gabrilovich,et al.  A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.

[26]  Ratna Babu Chinnam,et al.  Product design and manufacturing process based ontology for manufacturing knowledge reuse , 2019, J. Intell. Manuf..

[27]  Lihui Wang,et al.  A big data analytics based machining optimisation approach , 2018, J. Intell. Manuf..

[28]  Dawn M. Tilbury,et al.  The model-based product agent: A control oriented architecture for intelligent products in multi-agent manufacturing systems , 2019, Control Engineering Practice.

[29]  Pingyu Jiang,et al.  Sensitivity analysis-based process stability evaluation for one-of-a-kind production , 2019 .

[30]  Kevin I-Kai Wang,et al.  Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues , 2020, Robotics Comput. Integr. Manuf..

[31]  Paul G. Maropoulos,et al.  A knowledge capturing and sharing framework for improving the testing processes in global product development using storytelling and video sharing , 2018 .

[32]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[33]  Christian Köhler,et al.  Using Semantic Programming for Developing a Web Content Management System for Semantic Phenotype Data , 2018, DILS.

[34]  Yuan-Shin Lee,et al.  A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM) , 2017 .

[35]  Richard David Evans,et al.  A new paradigm for virtual knowledge sharing in product development based on emergent social software platforms , 2018 .

[36]  Chao Liu,et al.  Industrial Dataspace: A Broker to Run Cyber-Physical-Social Production System in Level of Machining Workshops , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).

[37]  Pulin Li,et al.  Mini-MES: A Microservices-Based Apps System for Data Interconnecting and Production Controlling in Decentralized Manufacturing , 2019, Applied Sciences.

[38]  Varish Mulwad,et al.  Integrated access to big data polystores through a knowledge-driven framework , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[39]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[40]  Pingyu Jiang,et al.  Knowledge-based innovative methods for collaborative quality control in equipment outsourcing chain , 2017, 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[41]  James Gao,et al.  An overview of manufacturing knowledge sharing in the product development process , 2018 .

[42]  Yu Peng,et al.  Review on cyber-physical systems , 2017, IEEE/CAA Journal of Automatica Sinica.

[43]  Chaoyang Zhang,et al.  Configuration Design of the Add-on Cyber-physical System with CNC Machine Tools and its Application Perspectives☆ , 2016 .

[44]  Ray Y. Zhong,et al.  Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors , 2017, Int. J. Prod. Res..

[45]  Jan Jürjens,et al.  Extending model-based privacy analysis for the industrial data space by exploiting privacy level agreements , 2018, SAC.

[46]  Diego Calvanese,et al.  Ontology-Based Data Access: A Survey , 2018, IJCAI.

[47]  Ronald J. Deibert,et al.  The Governance of Digital Technology, Big Data, and the Internet: New Roles and Responsibilities for Business , 2019 .

[48]  Edward Curry,et al.  Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems , 2019, Real-time Linked Dataspaces.

[49]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[50]  MengChu Zhou,et al.  Mighty MESs; state-of-the-art and future manufacturing execution systems , 2004, IEEE Robotics & Automation Magazine.

[51]  Peter Thanisch,et al.  Dataspace Management for Large Data Sets , 2019, Innovative Computing Trends and Applications.

[52]  Tsegay Tesfay Mezgebe,et al.  CoMM: a consensus algorithm for multi-agent-based manufacturing system to deal with perturbation , 2019, The International Journal of Advanced Manufacturing Technology.