Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life

Abstract The research objective of this work is to enhance the perception of, sensing in, and control of smart manufacturing systems (SMS) by leveraging active sensor systems within smart products during the manufacturing phase. Smart manufacturing utilizes rich process data, usually collected by the SMS (e.g., machine tools), to enable accurate tracking and monitoring of individual products throughout the process chain. However, until now, the to-be-manufactured product itself has not contributed to the sensing and compilation of product and process data. More specifically, data measured from the product’s structure during its own fabrication. In this paper, we discuss and evaluate the opportunity to actively use the capabilities of smart products within a SMS in terms of technical and economic feasibility. This opportunity emerged only recently with the advancements in smart products engineering. In this research, we developed a smart product prototype and evaluated it on a SMS testbed (CPlab) with eight distinct, fully-connected manufacturing processes. The results of the conducted experiments show the possibility to uniquely identify two distinct ‘fingerprints’ of manufacturing processes solely based on data provided by sensors within the smart product itself. The sensor data was collected directly from the smart product before manufacture was completed, yet after the intended sensor functionality during the product’s use phase was activated. The capability to automatically, accurately, and reliably identify process signatures and even inform the optimization of manufacturing parameters creates new opportunities for improvements in quality, scheduling, and seamless transparency across the whole value chain.

[1]  Bernd Scholz-Reiter,et al.  Autonomous Processes in Assembly Systems , 2007 .

[2]  Jürgen Beyerer,et al.  Smart Information Visualization for First-Time Quality within the Automobile Production Assembly Line , 2018 .

[3]  Dimitris Kiritsis,et al.  System architecture for closed-loop PLM , 2007, Int. J. Comput. Integr. Manuf..

[4]  Ramy Harik,et al.  Geometrical modeling of tow wrinkles in automated fiber placement , 2020 .

[5]  Ryan B. Wicker,et al.  3D Printing multifunctionality: structures with electronics , 2014 .

[6]  Rumi Ghosh,et al.  Manufacturing Analytics and Industrial Internet of Things , 2017, IEEE Intelligent Systems.

[7]  Eric MacDonald,et al.  Corrections to "3D Printed Elastomeric Lattices With Embedded Deformation Sensing" , 2020, IEEE Access.

[8]  Stephen Hailes,et al.  Security of smart manufacturing systems , 2018 .

[9]  Andrew Kusiak,et al.  Data Mining in Manufacturing: A Review , 2006 .

[10]  Sanjay E. Sarma,et al.  Auto ID systems and intelligent manufacturing control , 2003 .

[11]  Zhiwen Liu,et al.  Smart manufacturing systems: state of the art and future trends , 2019, The International Journal of Advanced Manufacturing Technology.

[12]  Tzu-Chang Lee,et al.  Head Motion Recognition Using a Smart Helmet for Motorcycle Riders , 2019, 2019 International Conference on Machine Learning and Cybernetics (ICMLC).

[13]  Andrew Kusiak,et al.  From data to big data in production research: the past and future trends , 2019, Int. J. Prod. Res..

[14]  Barbara Linke,et al.  Data-driven Sustainability in Manufacturing: Selected Examples , 2019, Procedia Manufacturing.

[15]  Francesco Ramella What is smart manufacturing , 2020 .

[16]  Klaus-Dieter Thoben,et al.  Accessing servitisation potential of PLM data by applying the product avatar concept , 2015 .

[17]  J. Wende,et al.  Eine Implementierung von Losgröße 1 nach Industrie-4.0-Prinzipien , 2014, Elektrotech. Informationstechnik.

[18]  Klaus-Dieter Thoben,et al.  Machine learning in manufacturing: advantages, challenges, and applications , 2016 .

[19]  Gerardo Beruvides,et al.  Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process , 2015, Comput. Ind..

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

[21]  Tim Schmidt,et al.  Towards (pro-)active intelligent products , 2018 .

[22]  Andrew Kusiak,et al.  Smart manufacturing must embrace big data , 2017, Nature.

[23]  Marco Macchi,et al.  Building a Total Cost of Ownership model to support manufacturing asset lifecycle management , 2019, Production Planning & Control.

[24]  Anand Asundi,et al.  Structural health monitoring of smart composite materials by using EFPI and FBG sensors , 2003 .

[25]  Tao Yu,et al.  The process quality control of single-piece and small-batch products in advanced manufacturing environment , 2009, 2009 16th International Conference on Industrial Engineering and Engineering Management.

[26]  Stephen C.-Y. Lu,et al.  Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering , 1990 .

[27]  Manoj Kumar Tiwari,et al.  Data mining in manufacturing: a review based on the kind of knowledge , 2009, J. Intell. Manuf..

[28]  Michael C. McAlpine,et al.  3D Printed Bionic Ears , 2013, Nano letters.

[29]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[30]  James Moyne,et al.  Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing , 2017 .

[31]  Luca Fumagalli,et al.  Exploring the role of Digital Twin for Asset Lifecycle Management , 2018 .

[32]  Bernd Scholz-Reiter,et al.  Autonomously controlled production systems—Influence of autonomous control level on logistic performance , 2009 .

[33]  Ronay Ak,et al.  A Survey of the Advancing Use and Development of Machine Learning in Smart Manufacturing. , 2018, Journal of manufacturing systems.

[34]  Karan Menon,et al.  Industrial internet platform provider and end-user perceptions of platform openness impacts , 2020, Industry and Innovation.

[35]  Edward Griffor,et al.  Cyber-Physical Systems and Internet of Things , 2019 .

[36]  Klaus-Dieter Thoben,et al.  A Product Avatar for Leisure Boats Owners: Concept, Development and Findings , 2013, PLM.

[37]  Michael Freitag,et al.  Indoor Positioning in Car Parks by using Wi-Fi Round-Trip-Time to support Finished Vehicle Logistics on Port Terminals , 2019 .

[38]  Nina Korlina Madzhi,et al.  Smart helmet with sensors for accident prevention , 2013, 2013 International Conference on Electrical, Electronics and System Engineering (ICEESE).

[39]  Thorsten Wuest Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning , 2015 .

[40]  Dimitris Kiritsis,et al.  Closed-loop PLM for intelligent products in the era of the Internet of things , 2011, Comput. Aided Des..

[41]  Ryan B. Wicker,et al.  3D Printed Electronics With High Performance, Multi-Layered Electrical Interconnect , 2017, IEEE Access.

[42]  Guofei Jiang,et al.  Modeling and analytics for cyber-physical systems in the age of big data , 2014, PERV.

[43]  Jürgen Beyerer,et al.  A quality information management model for smart rework control within vehicle assembly processes , 2018, 2018 International Conference on Information Management and Processing (ICIMP).

[44]  Luke Renaud,et al.  Aerosol based direct-write micro-additive fabrication method for sub-mm 3D metal-dielectric structures , 2015 .

[45]  Bernd Scholz-Reiter,et al.  Modelling Dynamics of Autonomous Logistic Processes: Discrete-event versus Continuous Approaches , 2005 .

[46]  Jay Lee,et al.  Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .

[47]  Bernd Scholz-Reiter,et al.  Towards a standardised information exchange within finished vehicle logistics based on RFID and EPCIS , 2017, Int. J. Prod. Res..

[48]  Raffaella Di Sante,et al.  Fibre Optic Sensors for Structural Health Monitoring of Aircraft Composite Structures: Recent Advances and Applications , 2015, Sensors.

[49]  Klaus-Dieter Thoben,et al.  An approach to monitoring quality in manufacturing using supervised machine learning on product state data , 2013, Journal of Intelligent Manufacturing.

[50]  Sima Noghanian,et al.  Developing flexible 3D printed antenna using conductive ABS materials , 2015, 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting.

[51]  Duncan C. McFarlane,et al.  Product intelligence in industrial control: Theory and practice , 2013, Annu. Rev. Control..

[52]  Christopher Sacco,et al.  Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection , 2020 .

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

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

[55]  Klaus-Dieter Thoben,et al.  A survey of product lifecycle models: towards complex products and service offers , 2016 .

[56]  Connor Jennings,et al.  A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests , 2017 .

[57]  Christian Prasse,et al.  Towards Decentralized Production: A Novel Method to Identify Flexibility Potentials in Production Sequences Based on Flexibility Graphs , 2015, Int. J. Autom. Technol..

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

[59]  Jan Holmström,et al.  Intelligent Products: A survey , 2009, Comput. Ind..

[60]  Meng Zhang,et al.  Digital twin driven smart product design framework , 2020 .

[61]  Klaus-Dieter Thoben,et al.  Application of the Stage Gate Model in Production Supporting Quality Management , 2014 .

[62]  Naeem Jardine,et al.  Wireless SMART Product Tracking using Radio Frequency Identification , 2019, 2019 IEEE 2nd Wireless Africa Conference (WAC).

[63]  H. Xin,et al.  3D printing multi-functionality: Embedded RF antennas and components , 2015, 2015 9th European Conference on Antennas and Propagation (EuCAP).

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

[65]  Valentina Zaccaria,et al.  Fleet Monitoring and Diagnostics Framework Based on Digital Twin of Aero-Engines , 2018, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy.

[66]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[67]  Klaus-Dieter Thoben,et al.  Digital Representations of Intelligent Products: Product Avatar 2.0 , 2013 .

[68]  G. Seliger,et al.  Opportunities of Sustainable Manufacturing in Industry 4.0 , 2016 .

[69]  Volker Zöllmer,et al.  International Conference on System-integrated Intelligence : New Challenges for Product and Production Engineering , SysInt 2016 Customized Smartness : A Survey on links between Additive Manufacturing and Sensor Integration , 2016 .

[70]  Thorsten Wuest,et al.  Cascade Use and the Management of Product Lifecycles , 2017 .

[71]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[72]  M. Renn,et al.  Printing conformal electronics on 3D structures with Aerosol Jet technology , 2012, 2012 Future of Instrumentation International Workshop (FIIW) Proceedings.

[73]  Octavian Morariu,et al.  Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems , 2020, Comput. Ind..

[74]  Marco Macchi,et al.  A review on the characteristics of cyber-physical systems for the future smart factories , 2020, Journal of Manufacturing Systems.

[75]  Patrice Mégret,et al.  Fiber Bragg Grating Sensors toward Structural Health Monitoring in Composite Materials: Challenges and Solutions , 2014, Sensors.

[76]  Thorsten Wuest,et al.  Holistic approach to machine tool data analytics , 2018, Journal of Manufacturing Systems.

[77]  Przemysław Zawadzki,et al.  Smart product design and production control for effective mass customization in the Industry 4.0 concept , 2016 .

[78]  Anton Umek,et al.  Identification and Selection of Sensors Suitable for Integration into Sport Equipment: Smart Golf Club , 2016, 2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI).

[79]  Anton Umek,et al.  Suitability of Strain Gage Sensors for Integration into Smart Sport Equipment: A Golf Club Example , 2017, Sensors.