Digital Twin-Based Audit Engine for Explainable Machine Learning in Automated Manufacturing Compliance
Abstract
Automated manufacturing is increasingly reliant on machine-learning models to support operations and decision-making, at the same becoming highly regulated and monitored by assurance frameworks that seek to enhance customer trust. Responding to safety, cyber-security, and sustainability requirements creates a burden represented by manual audits consuming part of the operational budget. Furthermore, machine-learning models are black-boxes, even for the processes governed by them. Knowledge gaps prevent the construction of risk-averse systems capable proactively adapt to noncompliance thresholds. Indeed, a technology is needed to match the demand of industry and comply with regulations in a trustworthy manner. Digital twins can ingest data from a physical manufacturing operation and, with sufficient fidelity, can be used for onside auditing of a dedicated or industry-wide audit engine hosted on the cloud
Artificial Intelligence and in particular Machine Learning technologies have a growing role in society and industry. In particular, Machine Learning is used in automated systems that can be examined both for the decision support they provide and for their own decision-making processes. Explainability is seen as a necessity to guarantee the correct operation of a system, especially when humans are responsible for operating the machine or the decisions taken therein. A growing number of frameworks, regulations, and directives support the implementation of explaining techniques for Artificial Intelligence used in product safety, data protection, and sustainability contexts. Nevertheless, these efforts are often disconnected from the rapidly evolving technologies and their requirements of respect for human factors in auditing, acting, and interpreting.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
21570-21588 |
Published |
July 14, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Ghatoth Mishra, Dr. Daniel Moreau (%2026). Digital Twin-Based Audit Engine for Explainable Machine Learning in Automated Manufacturing Compliance. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21570-21588. https://doi.org/10.15662/ijetemr.2026.1104003 |
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17. Sanku, R., Recharla, M., BG, M. B., & Chikop, S. A. (2026, February). Privacy-Preserving Federated Learning Framework with Adaptive Aggregated Gradient Perturbation for IoT Healthcare Systems. In 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-6). IEEE.
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21. Nagabhyru, K. C., Gadi, A. L., Seenu, A., Davuluri, P. N., Segireddy, A. R., & Pamisetty, V. (2026, March). Towards Automated Financial Risk Scoring in Automotive Financing with Explainable Machine Learning. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1-6). IEEE.
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25. Onaji, I., Tiwari, D., Soulatiantork, P., Song, B., & Tiwari, A. (2022). Digital twin in manufacturing: Conceptual framework and case studies. International Journal of Computer Integrated Manufacturing, 35(8), 831–858.
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