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Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Author

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  • Hietam Elhoone
  • Tianyang Zhang
  • Mohd Anwar
  • Salil Desai

Abstract

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.

Suggested Citation

  • Hietam Elhoone & Tianyang Zhang & Mohd Anwar & Salil Desai, 2020. "Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2841-2861, May.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2841-2861
    DOI: 10.1080/00207543.2019.1671627
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    Cited by:

    1. Nick J. Fox, 2024. "Artificial Intelligence and the Black Hole of Capitalism: A More-than-Human Political Ethology," Social Sciences, MDPI, vol. 13(10), pages 1-16, September.
    2. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    3. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    4. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.
    5. Bordoloi, Tausif & Shapira, Philip & Mativenga, Paul, 2022. "Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. J. Apolinar Muñoz Rodríguez, 2022. "Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling," Energies, MDPI, vol. 15(18), pages 1-23, September.

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