Keywords
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines lifecycle reliability and translation in real-time generative models for process support, with links to intelligent manufacturing and evidence governance. The organizing question is how lifecycle evidence should connect design, manufacturing, operation, degradation, and end-of-life choices. Ten or more scholarly and user-supplied records are synthesized through a mechanism-to-decision framework spanning system boundaries, measurement, representation, evaluation, translation, and governance. No experiment, participant dataset, effect estimate, or production result is invented. Particular attention is given to short-term efficiency shifting environmental or reliability costs downstream. The review argues that credible translation requires source-level traceability, explicit validity domains, failure-aware evaluation, and revalidation triggers. Google Scholar-supplied records are retained in structured APA form, and no missing DOI or pagination field is completed by conjecture.
References
Anderson, R. (2020). Security engineering: A guide to building dependable distributed systems (3rd ed.). Wiley.
Guan, G. F., Liu, X., & Wang, L. (2026, May). A data-driven framework for contamination risk assessment in high-volume lithium-ion battery manufacturing. In 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 492-496). IEEE.
Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: Concepts and precepts. Ashgate.
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
Linkov, I., & Trump, B. D. (2019). The science and practice of resilience. Springer. https://doi.org/10.1007/978-3-030-04565-4
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (pp. 8748-8763). PMLR.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Zhang, Z., Liu, W., Tao, J., Zhu, H., Li, S., & Xiao, Y. (2025, December). Unsupervised anomaly detection in cloud-native microservices via cross-service temporal contrastive learning. In 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF) (pp. 221-226). IEEE.