Aug 2026· Journal of Computing and Information Science in Engineering· 0 citations
TL;DR
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.
Abstract
Manufacturing systems are undergoing a fundamental transformation driven by the convergence of cyber-physical infrastructure, ubiquitous sensing, and artificial intelligence. At the heart of this transformation lies the challenge of embedding physical knowledge into data-driven models to achieve both predictive accuracy and engineering interpretability. Physics-informed machine learning (PIML) has emerged as a paradigm for integrating conservation laws, constitutive equations, and domain constraints into neural architectures.
Despite significant advances in smart design and manufacturing, three persistent gaps remain. First, multimodal sensor fusion in manufacturing environments continues to struggle with heterogeneous data types and varying sampling rates, where data-driven approaches often fail to generalize across operating conditions. Second, intelligent fault diagnosis under data scarcity, label noise, and cross-domain shifts demands methods that can encode physical priors while maintaining sample efficiency. Third, dynamic production planning under disturbances requires optimization frameworks that respect physical constraints while adapting in real time.
This special issue brings together 10 contributions that address these challenges through novel PIML models. These works demonstrate how the systematic integration of physical knowledge can enhance model robustness, reduce data requirements, and improve generalization across manufacturing applications.
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
Machine learning, digital twins, cyber-physical systems, and smart infrastructure are changing the way additive and hybrid manufacturing goes from static, process-defined to adaptive, data-driven manufacturing. The reviewed integrated architectural approach is able to link manufacturing at the physical level with the sensing, data infrastructure, physics-based modelling, surrogate modelling, artificial intelligence, and closed-loop control levels. Digital threads enable ongoing data connectivity and traceability from design to production, inspection, and maintenance phases, and digital twins maintain a dynamic representation of changing conditions in the processes. In the manufacturing sector, Edge and cloud infrastructure make it possible to capture and process data in real time and manage and analyze it at scale in a variety of factory conditions. Surrogate and physics-informed models complement high-fidelity physics-based simulations for reducing computational demands and enabling rapid prediction and optimization. Layer-to-layer and within-layer control strategies further allow the adjustment of manufacturing parameters in an adaptive way using real-time process information. The framework also introduces the possibility of hybrid manufacturing processes: Additive deposition and subtractive machining, finishing, and inspection processes are linked through continuous digital data exchange. Key needs for safe industrial deployment are identified to include safety, cyber security, regulatory compliance, data governance, and model traceability. Overall, the integrated approach offers a way to more autonomous, responsive, traceable, and efficient manufacturing systems, and puts the emphasis on the remaining need for physics-based knowledge, transparent decision making, human supervision, and validated control architectures.
M. Uddin, Sumi Ghosh· Journal of Computer Science...· 0 citations
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.
Vaishali A. Kherdekar, Shravani Kherdekar· Asian Journal of Research in...· 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
To address the critical scarcity of full life-cycle failure data for ball screws in industrial applications, this paper proposes the Hybrid Physics-Informed Data Augmentation (HPIDA) framework. This framework integrates an autoregressive (AR) system identification model with Archard’s wear law and Hertzian contact mechanics, constructing a comprehensive physical mapping chain from microscopic wear to macroscopic vibration signals. Furthermore, a domain randomization strategy is employed to synthesize high-fidelity, virtual full-life trajectories that encompass diverse degradation rates. Validation on the PredMAIN dataset demonstrates that the Wasserstein-1 distance for all generated signals strictly remains below 0.08, while both the power spectrum cosine similarity and the autocorrelation Pearson coefficient consistently exceed 0.9. The synthesized data exhibit a high degree of concordance with authentic signals across three dimensions: statistical distribution, frequency-domain structure, and temporal characteristics. Consequently, this approach provides robust, physically-grounded data to effectively support the training of deep learning-based Remaining Useful Life (RUL) prediction models under few-shot constraints.
Temperature-control materials, notably composite phase-change materials (CPCMs), show great potential for the thermal management of next-generation power electronics. However, the synergistic optimization of multiple performance metrics still relies heavily on Edisonian trial-and-error experimentation. Herein, we present an artificial intelligence framework that incorporates a physics-constrained inverse-design system (PHICS) built upon a directed acyclic graph (DAG) architecture for CPCMs, integrating interface, phase, and carrier engineering. By encoding structural hierarchies and physical causality through DAG, PHICS framework couples forward predictive modeling with a diversity-enhanced NSGA-II optimizer to efficiently map Pareto-optimal design boundaries. Guided by these predictions, we successfully fabricate a high-performance CPCM composed of an oriented graphite fiber skeleton and an n-octacosane matrix with amorphous alumina (am-Al2O3) interfacial transition layers. Benefiting from the bifunctional role of the am-Al2O3 interlayer as both an interfacial phonon bridge and electron barrier, the resulting CPCMs achieve a superior balance of thermal conduction, thermal storage, and electrical insulation. These results demonstrate the accuracy of PHICS-guided multifunctional composite design, establishing a closed-loop platform that combines physics-constrained machine learning with experimental validation to solve key thermal–electrical trade-offs.
Dongliang Ding, Min-hao Zou, Ruoyu Huang et al.· National Science Review· 0 citations
