Jul 2026· Asian Journal of Research in Computer Science· 0 citations
TL;DR
This review synthesises the state of the art across five interconnected domains: structural simulation and finite element analysis, computational fluid dynamics, design automation and topology optimisation, manufacturing process simulation with particular attention to additive manufacturing, and prognostics together with robotic control.
Abstract
The integration of artificial intelligence and machine learning into mechanical engineering has moved from a peripheral research interest to a central force reshaping design, simulation, manufacturing, and maintenance practice. This review synthesises the state of the art across five interconnected domains: structural simulation and finite element analysis, computational fluid dynamics, design automation and topology optimisation, manufacturing process simulation with particular attention to additive manufacturing, and prognostics together with robotic control. Surrogate and physics-informed learning architectures reduce the computational burden of high-fidelity simulation by orders of magnitude, but they also introduce new questions around generalisability, interpretability, and validation against first-principles physics. Deep generative models are reframing topology optimisation and conceptual design as a learned mapping rather than an iterative search, and hybrid digital twins increasingly couple physics-based solvers with data-driven correction terms to support real-time decision making on the shop floor. Prognostics and health management has benefited substantially from deep sequence models for remaining useful life estimation, while reinforcement learning is maturing as a control paradigm for robotic manipulation and mechatronic systems. Persistent challenges include data scarcity in high-value manufacturing contexts, the limited physical interpretability of black-box predictors, inconsistent verification and validation protocols, and the computational and environmental cost of training large models. The review closes by identifying future research directions centred on physics-constrained architectures, federated and transfer learning across heterogeneous industrial datasets, and standardised benchmarking, before outlining the principal limitations of the present narrative synthesis.
Physics-informed machine learning, digital twins, and additive manufacturing are a new direction for the creation of intelligent, adaptive, and high-performance engineering systems that are being integrated into a smart industrial framework. The strategy combines multimodal sensing, data fusion, physics-based modeling, machine learning, process optimization, and closed-loop control, and addresses the challenges of enhancing manufacturing performance across the product life cycle. In physics-informed machine learning, physics principles are incorporated into the data-driven models, which enhances prediction accuracy, decreases the need for large data sets, and facilitates generalization from model to model for different process conditions. Digital twins are virtual models of AM systems that support real-time monitoring and anomaly detection, predictive analysis, virtual experiments, and adaptive process control. The combination of edge computing and intelligent controllers enhances quick decision-making processes during the fabrication process. Aerospace, defense, biomedical engineering, and advanced composite manufacturing are just a few of the applications that show promise for achieving better dimensional accuracy, defect reduction, lightweight design, energy efficiency, material utilization, and process traceability. Industrial deployment is, however, hindered by the lack of high-quality datasets, class imbalance, limited model transferability, interoperability, high computational requirements, cybersecurity, certification, and lifecycle governance. For scalable implementation, standardized data formats, open architectures, benchmark datasets, federated learning, hybrid modeling, and rigorous validation procedures are all important. In general, physics-based informed intelligence and adaptive AM create a promising basis for reliable, efficient, traceable, and environmentally friendly high-performance engineering.
Fahmina Afrin· Journal of Artificial Intell...· 0 citations
Ten contributions are brought together to demonstrate how the systematic integration of physical knowledge can enhance model robustness, reduce data requirements, and improve generalization across manufacturing applications.
Jiewu Leng, Hui Yang, Min Xia et al.· Journal of Computing and Inf...· 0 citations
The evidence indicates that LLMs are becoming useful semantic and coordination layers in engineering workflows, but not dependable engineering substitutes in human-in-the-loop, evidence-grounded systems where retrieval, validation, tool use, and structured knowledge help keep outputs useful and bounded in safety-relevant tasks.
Artificial intelligence (AI) is transforming smart manufacturing by enabling intelligent automation, data-driven decisions, and stronger collaboration between humans and manufacturing systems. The widespread adoption of collaborative robots, the industrial internet of things, and cyber-physical systems is driving demand for manufacturing environments that are safer, more flexible, and more efficient. Despite AI’s broad application in manufacturing, few studies have combined adaptive safety and intelligent task allocation within a single human-centered framework. This review offers a comprehensive look at AI applications that support these two complementary functions. Literature from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library was systematically reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and analysed thematically. The findings show that machine learning, deep learning, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making. That said, challenges remain - such as interoperability, explainability, limited access to high-quality manufacturing data, industrial validation, and integrating multiple AI technologies. This review gives researchers and practitioners a holistic perspective and highlights integrated, human-centered AI frameworks as key enablers of resilient, efficient, and sustainable Industry 5.0 manufacturing systems.
Zaliha Baso, N. Yadav, P. Faujdar· Cureus Journal of Computer S...· 0 citations
This article delves deep into the confluence of simulation, ML, and statistics, showcasing how they synergize to improve engineering workflows and emphasizes that DCE is not just a technological advancement but a foundational strategy for next-generation engineering solutions.
Benjamin Scott· International Journal of Dat...· 0 citations
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, integrating singular value decomposition (SVD)-based reduced-order models with a Variational Autoencoder with Arbitrary Conditioning (AC-VAE) and hybrid simulation frameworks to support real-time process prediction and optimization. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative Horizontal Direct Chill (HDC) continuous casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications.
Sofija Milicic, A. Horr, S. Elgeti et al.· Processes· 0 citations