Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

Abstract Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.

[1]  Kazuhiro Ohkura,et al.  Modelling of Biological Manufacturing Systems for Dynamic Reconfiguration , 1997 .

[2]  Irlán Grangel-González,et al.  Towards a Semantic Administrative Shell for Industry 4.0 Components , 2016, 2016 IEEE Tenth International Conference on Semantic Computing (ICSC).

[3]  Yoram Koren,et al.  Manufacturing system architecture for cost-effective mass-individualization , 2018 .

[4]  L. Li China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0” , 2017, Technological Forecasting and Social Change.

[5]  Katherine C. Morris,et al.  Current Standards Landscape for Smart Manufacturing Systems , 2016 .

[6]  Suk-Hwan Suh,et al.  Architecture and implementation of a shop-floor programming system for STEP-compliant CNC , 2003, Comput. Aided Des..

[7]  Irlán Grangel-González,et al.  An RDF-based approach for implementing industry 4.0 components with Administration Shells , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

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

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

[10]  Xun Xu,et al.  A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect , 2019, Journal of Manufacturing Systems.

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

[12]  Lihui Wang,et al.  Combined strength of holons, agents and function blocks in cyber-physical systems , 2016 .

[13]  Xun Xu,et al.  Machine Tool 4.0 for the new era of manufacturing , 2017 .

[14]  Gabor Sziebig,et al.  Step-NC based industrial robot CAM system , 2009 .

[15]  Peter B. Luh,et al.  Holonic manufacturing scheduling: architecture, cooperation mechanism and implementation , 1998 .

[16]  Thomas Hedberg,et al.  A standards-based approach for linking as-planned to as-fabricated product data. , 2018, CIRP annals ... manufacturing technology.

[17]  Sasa Zivanovic,et al.  An approach for applying STEP-NC in robot machining , 2018 .

[18]  Duck Bong Kim,et al.  Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC , 2016 .

[19]  Joseph K. Davidson,et al.  Semantic Interoperability of GD&T Data Through ISO 10303 Step AP242 , 2016, DAC 2016.

[20]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..

[21]  Matthieu Rauch,et al.  STEP-NC compliant process planning of additive manufacturing: remanufacturing , 2017 .

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

[23]  Suk-Hwan Suh,et al.  STEP-compliant CNC system for turning: Data model, architecture, and implementation , 2006, Comput. Aided Des..

[24]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[25]  Nadra Guizani,et al.  Improving Cognitive Ability of Edge Intelligent IIoT through Machine Learning , 2019, IEEE Network.

[26]  Bijan Sarkar,et al.  Dynamic schedule execution in an agent based holonic manufacturing system , 2013 .

[27]  Xun Xu,et al.  Realization of STEP-NC enabled machining , 2006 .

[28]  P. Trnka,et al.  OPC-UA information model for large-scale process control applications , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[29]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[31]  Lihui Wang,et al.  Function block design for adaptive execution control of job shop machining operations , 2009 .

[32]  Martin Hardwick,et al.  Lessons learned implementing STEP-NC AP-238 , 2006, Int. J. Comput. Integr. Manuf..

[33]  Birte Caesar,et al.  (Self-)adaptiveness for manufacturing systems: challenges and approaches , 2019, SICS Software-Intensive Cyber-Physical Systems.

[34]  Yoram Koren,et al.  The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems , 2010 .

[35]  Thomas Hedberg,et al.  Interoperability: linking design and tolerancing with metrology , 2016, Procedia CIRP.

[36]  William G. Rippey,et al.  Web-enabled, Real-time, Quality Assurance for Machining Production Systems , 2013 .

[37]  László Monostori,et al.  Agent-based systems for manufacturing , 2006 .

[38]  Rivai Wardhani,et al.  Model-based manufacturing based on STEP AP242 , 2016, 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[39]  Allison Barnard Feeney,et al.  Testing the Digital Thread in Support of Model-Based Manufacturing and Inspection , 2016, J. Comput. Inf. Sci. Eng..

[40]  Lihui Wang,et al.  Challenges in smart manufacturing , 2016 .

[41]  Jennifer E. Rowley,et al.  The wisdom hierarchy: representations of the DIKW hierarchy , 2007, J. Inf. Sci..

[42]  Yi Wu,et al.  Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.

[43]  Paulo Leitão,et al.  ADACOR: A holonic architecture for agile and adaptive manufacturing control , 2006, Comput. Ind..

[44]  Abdulrahman Al-Ahmari,et al.  Requirements of the Smart Factory System: A Survey and Perspective , 2018, Machines.

[45]  Lihui Wang,et al.  A review of function blocks for process planning and control of manufacturing equipment , 2012 .

[46]  Suk-Hwan Suh,et al.  Reincarnation of G-code based part programs into STEP-NC for turning applications , 2007, Comput. Aided Des..

[47]  Iko Miyazawa,et al.  OPC UA information model, data exchange, safety and security for IEC 61131–3 , 2011, SICE Annual Conference 2011.

[48]  Robert W. Brennan,et al.  Evaluating the performance of reactive control architectures for manufacturing production control , 2001, Comput. Ind..

[49]  Xun Xu,et al.  Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services , 2019, Robotics and Computer-Integrated Manufacturing.

[50]  Yu-Cheng Chou,et al.  A bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling , 2013 .

[51]  S. Jack Hu,et al.  Evolving paradigms of manufacturing: From mass production to mass customization and personalization , 2013 .

[52]  W. L. Yeung,et al.  Behavioral modeling and verification of multi-agent systems for manufacturing control , 2011, Expert Syst. Appl..

[53]  Sebti Foufou,et al.  A proposed mapping method for aligning machine execution data to numerical control code , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).

[54]  Thomas F. Edgar,et al.  Smart manufacturing, manufacturing intelligence and demand-dynamic performance , 2012, Comput. Chem. Eng..

[55]  Masahiko Mori,et al.  Machine monitoring system based on MTConnect technology , 2014 .

[56]  Ying Liu,et al.  Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor , 2017, IEEE Transactions on Industrial Informatics.

[57]  Renan Bonnard,et al.  A STEP-NC compliant robotic machining platform for advanced manufacturing , 2017, The International Journal of Advanced Manufacturing Technology.

[58]  Xiaojun Chen,et al.  A review: Knowledge reasoning over knowledge graph , 2020, Expert Syst. Appl..

[59]  Jean-Yves Hascoët,et al.  A new digital chain for additive manufacturing processes , 2010 .

[60]  Lihui Wang,et al.  From Intelligence Science to Intelligent Manufacturing , 2019, Engineering.

[61]  Ji Ma,et al.  ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments , 2010, IEEE Transactions on Automation Science and Engineering.

[62]  Jakub Rosner,et al.  OPC UA Object Oriented Model for Public Transportation System , 2011, 2011 UKSim 5th European Symposium on Computer Modeling and Simulation.

[63]  A. Tharumarajah,et al.  Comparison of the bionic, fractal and holonic manufacturing system concepts , 1996 .

[64]  Abdelghani Bekrar,et al.  The control of myopic behavior in semi-heterarchical production systems: A holonic framework , 2013, Eng. Appl. Artif. Intell..

[65]  Lihui Wang,et al.  Design of adaptive function blocks for dynamic assembly planning and control , 2008 .

[66]  Allison Barnard Feeney,et al.  Contextualising manufacturing data for lifecycle decision-making. , 2018, International journal of product lifecycle management.

[67]  A. Nassehi,et al.  The application of multi-agent systems for STEP-NC computer aided process planning of prismatic components , 2006 .

[68]  Juergen Jasperneite,et al.  The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0 , 2017, IEEE Industrial Electronics Magazine.