Smart manufacturing

Smart manufacturing[1] is a broad category of manufacturing that employs computer-integrated manufacturing, high levels of adaptability and rapid design changes, digital information technology, and more flexible technical workforce training.[2] Other goals sometimes include fast changes in production levels based on demand,[3][1] optimization of the supply chain,[3] efficient production and recyclability.[4] In this concept, as smart factory has interoperable systems, multi-scale dynamic modelling and simulation, intelligent automation, strong cyber security, and networked sensors.

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The broad definition of smart manufacturing covers many different technologies. Some of the key technologies in the smart manufacturing movement include big data processing capabilities, industrial connectivity devices and services, and advanced robotics.[5]

[6] Graphic of a sample manufacturing control system showing the interconnectivity of data analysis, computing and automation.
Advanced robotics used in automotive production

Big data processing

Smart manufacturing utilizes big data analytics, to refine complicated processes and manage supply chains.[7] Big data analytics refers to a method for gathering and understanding large data sets in terms of what are known as the three V's, velocity, variety and volume. Velocity informs the frequency of data acquisition, which can be concurrent with the application of previous data. Variety describes the different types of data that may be handled. Volume represents the amount of data.[8] Big data analytics allows an enterprise to use smart manufacturing to predict demand and the need for design changes rather than reacting to orders placed.[2]

Some products have embedded sensors, which produce large amounts of data that can be used to understand consumer behavior and improve future versions of the product.[9][10][11]

Advanced robotics

Advanced industrial robots, also known as smart machines, operate autonomously and can communicate directly with manufacturing systems. In some advanced manufacturing contexts, they can work with humans for co-assembly tasks.[12] By evaluating sensory input and distinguishing between different product configurations, these machines are able to solve problems and make decisions independent of people. These robots are able to complete work beyond what they were initially programmed to do and have artificial intelligence that allows them to learn from experience.[5] These machines have the flexibility to be reconfigured and re-purposed. This gives them the ability to respond rapidly to design changes and innovation, which is a competitive advantage over more traditional manufacturing processes.[13] An area of concern surrounding advanced robotics is the safety and well-being of the human workers who interact with robotic systems. Traditionally, measures have been taken to segregate robots from the human workforce, but advances in robotic cognitive ability have opened up opportunities, such as cobots, for robots to work collaboratively with people.[14]

Cloud computing allows large amounts of data storage or computational power to be rapidly applied to manufacturing, and allow a large amount of data on machine performance and output quality to be collected. This can improve machine configuration, predictive maintenance, and fault analysis. Better predictions can facilitate better strategies for ordering raw materials or scheduling production runs.

Further information: Industrial Internet of Things and Cyber-physical systems

3D printing

As of 2019, 3D printing is mainly used in rapid prototyping, design iteration, and small-scale production. Improvements in speed, quality, and materials could make it useful in mass production[15][16] and mass customization.[16]

However, 3D printing developed so much in recent years that it is no longer used just as technology for prototyping. 3D printing sector is moving beyond prototyping especially it is becoming increasingly widespread in supply chains. The industries where digital manufacturing with 3D printing is the most seen are automotive, industrial and medical. In the auto industry, 3D printing is used not only for prototyping but also for the full production of final parts and products. 3D printing has also been used by suppliers and digital manufacturers coming together to help fight COVID-19.[17]

3D printing allows to prototype more successfully, thus companies are saving time and money as significant volumes of parts can be produced in a short period. There is great potential for 3D printing to revolutionise supply chains, hence more companies are using it. The main challenge that 3D printing faces is the change of people's mindset. Moreover, some workers will need to re-learn a set of new skills to manage 3D printing technology.[17]

Eliminating workplace inefficiencies and hazards

Smart manufacturing can also be attributed to surveying workplace inefficiencies and assisting in worker safety. Efficiency optimization is a huge focus for adopters of "smart" systems, which is done through data research and intelligent learning automation. For instance operators can be given personal access cards with inbuilt Wi-Fi and Bluetooth, which can connect to the machines and a Cloud platform to determine which operator is working on which machine in real time.[18] An intelligent, interconnected 'smart' system can be established to set a performance target, determine if the target is obtainable, and identify inefficiencies through failed or delayed performance targets.[19] In general, automation may alleviate inefficiencies due to human error. And in general, evolving AI eliminates the inefficiencies of its predecessors.

As robots take on more of the physical tasks of manufacturing, workers no longer need to be present and are exposed to fewer hazards.[20]

Impact of Industry 4.0

Industry 4.0 is a project in the high-tech strategy of the German government that promotes the computerization of traditional industries such as manufacturing. The goal is the intelligent factory (Smart Factory) that is characterized by adaptability, resource efficiency, and ergonomics, as well as the integration of customers and business partners in business and value processes. Its technological foundation consists of cyber-physical systems and the Internet of Things.[21]

This kind of "intelligent manufacturing" makes a great use of:

  • Wireless connections, both during product assembly and long-distance interactions with them;
  • Last generation sensors, distributed along the supply chain and the same products (Internet of things)
  • Elaboration of a great amount of data to control all phases of construction, distribution and usage of a good.

European Roadmap "Factories of the Future" and German one "Industrie 4.0″ illustrate several of the action lines to undertake and the related benefits. Some examples are:

  • Advanced manufacturing processes and rapid prototyping will make possible for each customer to order one-of-a-kind product without significant cost increase.
  • Collaborative Virtual Factory (VF) platforms will drastically reduce cost and time associated to new product design and engineering of the production process, by exploiting complete simulation and virtual testing throughout the Product Lifecycle.
  • Advanced Human-Machine interaction (HMI) and augmented reality (AR) devices will help increasing safety in production plants and reducing physical demand to workers (whose age has an increasing trend).
  • Machine learning will be fundamental to optimize the production processes, both for reducing lead times and reducing the energy consumption.
  • Cyber-physical systems and machine-to-machine (M2M) communication will allow to gather and share real-time data from the shop floor in order to reduce downtime and idle time by conducting extremely effective predictive maintenance.

Statistics

The Ministry of Economy, Trade and Industry in South Korea announced on 10 March 2016 that it had aided the construction of smart factories in 1,240 small and medium enterprises, which it said resulted in an average 27.6% decrease in defective products, 7.1% faster production of prototypes, and 29.2% lower cost.[22]

See also

References
  1. Lu, Yuqian; Xu, Xun; Wang, Lihui (July 2020). "Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios". Journal of Manufacturing Systems. 56: 312–325. doi:10.1016/j.jmsy.2020.06.010. S2CID 225557967.
  2. Davis, Jim; Edgar, Thomas; Porter, James; Bernaden, John; Sarli, Michael (2012-12-20). "Smart manufacturing, manufacturing intelligence and demand-dynamic performance". Computers & Chemical Engineering. FOCAPO 2012. 47: 145–156. doi:10.1016/j.compchemeng.2012.06.037.
  3. SMLC 2011
  4. Shipp, Stephanie S. (March 2012). "Emerging Global Trends in Advanced Manufacturing" (PDF). Emerging Global Trends in Advanced Manufacturing. Institute for Defense Analysis. Archived from the original (PDF) on 2012-06-06. Retrieved 2020-04-12.
  5. "On the Journey to a Smart Manufacturing Revolution". www.industryweek.com. 2015-12-30. Retrieved 2016-02-17.
  6. Albus, James S. (1995-01-01), English: Architecture for the NBS Automated Manufacturing Research Facility (AMRF)., retrieved 2016-03-04
  7. Rachuri, Sudarsan (February 4, 2014). "Smart Manufacturing Systems Design and Analysis" (PDF). National Institute of Standards and Technology. Retrieved February 16, 2016.
  8. Leveling, J.; Edelbrock, M.; Otto, B. (2014-12-01). "Big data analytics for supply chain management". 2014 IEEE International Conference on Industrial Engineering and Engineering Management. pp. 918–922. doi:10.1109/IEEM.2014.7058772. ISBN 978-1-4799-6410-9. S2CID 31872187.
  9. Yang, Chen; Shen, Weiming; Wang, Xianbin (January 2018). "The Internet of Things in Manufacturing: Key Issues and Potential Applications". IEEE Systems, Man, and Cybernetics Magazine. 4 (1): 6–15. doi:10.1109/MSMC.2017.2702391. S2CID 42651835.
  10. Porter, Michael E. (November 2014). "How Smart, Connected Products Are Transforming Competition". Harvard Business Review. April 2016.
  11. "Building Smarter Manufacturing With The Internet of Things (IoT)". IT World Canada. Lopez Research. 2014. Retrieved 2020-04-12.
  12. Wang, W.; Li, R.; Chen, Y.; Diekel, Z.; Jia, Y. (2019). "Facilitating Human-Robot Collaborative Tasks by Teaching-Learning-Collaboration From Human Demonstrations". IEEE Transactions on Automation Science and Engineering. 16 (2): 640–653. doi:10.1109/tase.2018.2840345. ISSN 1545-5955.
  13. "Robotic Systems for Smart Manufacturing". www.nist.gov. US Department of Commerce. October 2013. Retrieved 2016-03-04.
  14. Bicchi, Antonio; Peshkin, Michael A.; Colgate, J. Edward (2008-01-01). Siciliano, Bruno; Khatib, Oussama Khatib (eds.). Safety for Physical Human–Robot Interaction. Springer Berlin Heidelberg. pp. 1335–1348. doi:10.1007/978-3-540-30301-5_58. ISBN 9783540239574.
  15. Zimmermann, Stefan (March 26, 2018). "Industry 4.0 – 3D Printing in Manufacturing Industries". Atos Blog. Atos SE. Retrieved 2019-06-09.
  16. Hughes, Andrew (Mar 23, 2017). "Industry 4.0 is About More Than Data: 3D Printing in Manufacturing". Digital Transformation and Operational Excellence Blog. LNS Research. Retrieved 2019-06-09.
  17. Wilson, Georgia (May 16, 2020). "The evolution of 3D printing in manufacturing". Manufacturing Global. BizClick Medial Limited. Retrieved 2021-06-04.
  18. "ThingTrax". ThingTrax Connected Manufacturing. London. Archived from the original on 2017-04-12. Retrieved 2020-04-12.
  19. Jung, Kiwook (2015-03-16). "Mapping Strategic Goals and Operational Performance Metrics for Smart Manufacturing Systems". Procedia Computer Science. 44 (44 p.184–193): 184–193. doi:10.1016/j.procs.2015.03.051.
  20. Louchez, Alain (January 6, 2014). "From Smart Manufacturing to Manufacturing Smart". www.automationworld.com. Automation World. Retrieved 2016-03-04.
  21. Jacinto, Joan (July 31, 2014). "Smart Manufacturing? Industry 4.0? What's It All About?". The Vault - Siemens Totally Integrated Automation.
  22. Jung Min-hee (March 11, 2016). "Smart Factories Improving Productivity of SMEs".
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