http://yetl.yabesh.ir/yetl1/handle/yetl/19045 2026-08-26T12:31:17Z 2026-08-26T12:31:17Z Park, Jung Kyu Jung, Woo-Kyun Suh, Eun Suk http://yetl.yabesh.ir/yetl1/handle/yetl/4315834 2026-08-23T07:56:30Z 2026-01-01T00:00:00Z

Human–Artificial Intelligence Dynamic Resource Reallocation for Disruption-Prone Engineering Workflows Park, Jung Kyu; Jung, Woo-Kyun; Suh, Eun Suk Abstract. Managing modern engineering processes is increasingly challenging as they evolve into socio-technical systems where human experts and artificial intelligence (AI) agents coexist. The efficiency of such environments is highly vulnerable to unanticipated stochastic interruptions—such as urgent design changes, field issues, and equipment malfunctions—which share a common operational signature: they are unplanned, time-critical, and induce cascading bottlenecks that increase project makespan, exposing the limitations of conventional static scheduling. This study proposes a generalized human–AI collaborative dynamic reallocation framework to minimize the makespan and enhance the operational flexibility of disruption-prone engineering workflows. The proposed framework addresses dynamic workflow characteristics through two complementary policies: (i) task offloading from overloaded human experts to AI agents, and (ii) resource sharing to lend computational capacity to bottlenecked agents. These two policies are operationalized through an integrated dynamic allocation process that derives resource distribution via a long short-term memory-based workload predictor refined by a reinforcement learning calibration layer. The framework is evaluated via simulation using industrial issue-log data from large-scale software development, where urgent design changes serve as the representative interruption class. The proposed method reduced the makespan by approximately 12% compared to a first-in-first-out, first-available baseline heuristic under uncertain interruptions, and its robustness was verified across 27 diverse workload scenarios. This study presents a new dynamic resource allocation model that strengthens operational resilience by mitigating bottlenecks and proactively utilizing AI resources in engineering collaboration systems.

2026-01-01T00:00:00Z Wang, Hao Yang, Yujing Kan, Chen http://yetl.yabesh.ir/yetl1/handle/yetl/4315833 2026-08-23T07:56:27Z 2026-01-01T00:00:00Z

Self-Supervised Point Cloud Mining for Surface Anomaly Detection in Additive Manufacturing Wang, Hao; Yang, Yujing; Kan, Chen Abstract. With rapid advances in three-dimensional (3D) metrology, point cloud data are increasingly available for surface quality inspection in additive manufacturing (AM). Compared to images, point clouds capture richer geometric information for characterizing surface anomalies, enabling more comprehensive defect diagnosis and mitigation. However, it remains challenging to extract anomaly-pertinent information from scanned point clouds, due to (1) the scarcity of annotated point cloud data for training robust anomaly detection models and (2) the inherent complexity of point cloud processing, stemming from their high dimensionality, high volume, and unstructured nature. To address the challenges, this study develops a new framework for self-supervised representation learning of point clouds to glean anomaly-pertinent features. Specifically, a graph contrastive learning scheme is constructed by integrating ℓ-hop subgraphs, hard-negative sampling, and graph neural networks (GNNs) to explore the self-similarity of AM-fabricated surface patterns and highlight anomaly-induced variations. Unlike most existing approaches, it requires no external training samples or manual annotations. The framework has been evaluated using simulations and real-world data collected from wire arc additive manufacturing (WAAM). Results demonstrate that it outperforms the state-of-the-art benchmarks in accurately locating and characterizing surface defects, including subtle ones. The developed framework has strong potential for broader applications in differentiating surface textures and geometric patterns across diverse AM processes.

2026-01-01T00:00:00Z Zhang, Haihui Zhao, Yuhang Tong, Yifei Gao, Shujian Du, Xiaodong http://yetl.yabesh.ir/yetl1/handle/yetl/4315832 2026-08-23T07:56:25Z 2026-01-01T00:00:00Z

Physics-Informed Dynamic Cell Layout Planning for Matrix-Structured Manufacturing Workshops Under Volatile Environment Zhang, Haihui; Zhao, Yuhang; Tong, Yifei; Gao, Shujian; Du, Xiaodong Abstract. Modern manufacturing enterprises face growing challenges due to frequent production disturbances and intensified demand fluctuations. As a resilient manufacturing paradigm in the era of Industry 5.0, the matrix manufacturing system (MMS) can effectively accommodate multivariety and multibatch production requirements. However, traditional layout algorithms lack effective dynamic response mechanisms and have limited capability for real-time optimization of cell configurations under a volatile environment. To address this issue, we propose a dynamic cell layout planning method based on a consensus-enhanced fruit fly optimization algorithm (CE-FOA), in which physical constraints are embedded into the optimization process through feasible layout representation, fitness evaluation, and constraint-guided search. A consensus-driven evolutionary mechanism is incorporated into the conventional FOA to enhance global search efficiency and convergence stability, while a logistics relationship dimensionality reduction strategy is devised to lower computational complexity during optimization. Case study results from an MMS-based optoelectronic-pod (OP) workshop show that CE-FOA outperforms traditional FOA and simulated annealing (SA) in solution quality and convergence rate. These results validate the effectiveness and superior performance of the proposed approach, demonstrate the practical value of embedding manufacturing physical knowledge into dynamic layout optimization, and provide a new solution for dynamic workshop layout planning under the MMS paradigm.

2026-01-01T00:00:00Z Gao, Yiping Gao, Liang Li, Xinyu Yang, Demin http://yetl.yabesh.ir/yetl1/handle/yetl/4315830 2026-08-23T07:56:22Z 2026-01-01T00:00:00Z

An Evolutionary One-Shot Neural Architecture Search Method Based on Single-Path Cells Toward Physics-Informed Fault Diagnosis Gao, Yiping; Gao, Liang; Li, Xinyu; Yang, Demin Abstract. Fault diagnosis is important for the complex equipment, and physics-informed fault diagnosis has become an emerging trend. While physics-informed fault diagnosis is hard to realize, unless several problems are addressed, one of the limitations is searching for the best architecture, which influences the performance greatly. Neural architecture search (NAS) has been a research hotspot. However, limited by computing resources and the deviation of supernet prediction, NAS might miss the best architecture, and impedes the application of NAS in physics-informed fault diagnosis greatly. Thus, this article proposes an evolutionary one-shot NAS method based on single-path cells (SPC-NAS) for physics-informed fault diagnosis. The proposed method develops a new supernet based on single-path cells, to reduce the computing resources and improve the reusability. An improved supernet training method is introduced to reduce the deviation between the one-shot model prediction and the stand-alone model accuracy. Finally, an evolutionary search strategy with constraint is developed to find the best architecture. The experimental results show that the proposed method can automatically find the best architecture for different tasks, which achieved an accuracy of 100% in Case Western Reserve University (CWRU) dataset with only 0.292 M parameters. All the results indicate that the proposed method can address the limitation to search the best architecture and provides a foundation for future integration with physics-informed methods.

2026-01-01T00:00:00Z