Integrating Machine Learning Model and Digital Twin System for Additive Manufacturing

Jyeniskhan N. Keutayeva A. Kazbek G. Ali M.H. Shehab E.
2023 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2023 #11 71113 - 71126 pp.

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products are key challenges. The digital twin has emerged as a technology solution to address these challenge, allowing real-time monitoring and control of the manufacturing process. This paper proposes a digital twin system framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring. Particularly, the system utilizes machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects, demonstrating high efficiency. The Unity client user interface is also developed for control and visualization, facilitating easy additive manufacturing process monitoring. This research article presents a detailed description of the proposed digital twin framework and its workflow for implementation, the machine learning models, and the Unity client user interface. It also demonstrates the effectiveness of the integrated system through case studies and experimental results. The main findings show that the proposed digital twin system met its functional requirements and effectively detects defects and provides real-time control and monitoring of the additive manufacturing process. This paper contributes to the growing field of digital twin technology and additive manufacturing, providing a promising solution for enhancing the quality and reliability in the field of additive manufacturing.

Additive manufacturing , defect detection , digital twin , machine learning , real-time control , smart manufacturing , unity

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Nazarbayev University, SEDS, Mechanical and Aerospace Engineering Department, Astana, 010000, Kazakhstan
Nazarbayev University, SEDS, Robotics Engineering Department, Astana, 010000, Kazakhstan
Nazarbayev University, SEDS, Civil and Environmental Engineering Department, Astana, 010000, Kazakhstan
Nazarbayev University, School of Engineering and Digital Sciences, Astana, 010000, Kazakhstan

Nazarbayev University
Nazarbayev University
Nazarbayev University
Nazarbayev University

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