manufacturing

Robots struggle to autonomously perform complex maintenance tasks—such as disassembly and assembly—in unstructured environments due to environmental uncertainties, particularly discrepancies between CAD models and real-world scenes. Method: This paper proposes a closed-loop autonomous execution framework integrating symbolic task planning with multimodal perception. It introduces the first approach that jointly leverages CAD prior models and real-time RGB-D sensory data to dynamically refine the symbolic planner. The framework unifies task parsing, executable instruction generation, and adaptive closed-loop control to enable end-to-end mapping from high-level intent to low-level robot actions. Results: Experimental validation in realistic maintenance scenarios demonstrates robust performance under ±5 mm pose deviations between model and reality. The system successfully executes fully autonomous disassembly and assembly operations, significantly improving reliability and generalization capability for maintenance tasks in non-structured environments.

为应对多样化生产带来的挑战，本文提出一种基于CAD模型的编程和执行系统，通过动态参数化的行为树控制结构实现人-机器人-起重机协同任务。

This study addresses the quality control challenges in additive manufacturing arising from its layer-by-layer fabrication process and high degree of customization. It presents the first systematic adaptation of the Six Sigma DMAIC methodology tailored to this context. By integrating multi-source sensing and measurement data across the entire manufacturing chain—including materials, design, process parameters, and post-processing—the work employs advanced techniques such as deep learning, machine learning, design of experiments, simulation, ontological analysis, and network science to model the complex relationships among design inputs, process variations, and final part quality. The proposed optimization framework enables real-time anomaly detection and simultaneously optimizes lead time and energy consumption, thereby significantly enhancing the quality stability and process controllability of additive manufacturing systems.

Manufacturing SMEs face critical bottlenecks in visual assembly quality control—including scarce real-image acquisition, high annotation costs, and insufficient training data. To address these challenges, this paper proposes a CAD model–driven fully synthetic data framework. It establishes an end-to-end virtual generation pipeline integrating parametric CAD modeling, physics-based rendering, and YOLO-family object detection, enabling efficient simulation-to-reality transfer learning. This work represents the first systematic deployment of a purely synthetic data approach to industrial inspection of planetary gear assemblies. Experiments demonstrate a 99.5% mAP@0.5:0.95 on synthetic data; after domain adaptation, detection accuracy remains at 93% on real-world images. The framework significantly reduces dependence on manual annotation and physical image collection, validating its feasibility for lightweight, reusable, and low-cost industrial deployment.

In micro-manufacturing, robotic positioning accuracy is severely compromised by multiple uncertainty sources—including measurement noise, model mismatch, and joint compliance—leading to degraded task reliability. Method: This paper proposes an uncertainty-aware multi-robot cooperative control paradigm that incorporates human sensory compensation principles into control design. By integrating robust control theory, multi-agent coordination algorithms, and probabilistic uncertainty modeling, the approach enables real-time, sensor-driven dynamic error suppression without requiring costly hardware upgrades. Contribution/Results: Experimental evaluation demonstrates substantial reductions in positioning deviation and task failure rate during micrometer-scale operations. The proposed method achieves uncertainty suppression performance comparable to high-end precision sensor-based solutions, thereby establishing a novel pathway toward low-cost, high-robustness automation for micro-manufacturing.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

This study addresses the challenges of control design in complex industrial processes characterized by multivariable coupled dynamics by proposing an automated control strategy generation framework that integrates large language models (LLMs) with Bayesian optimization. The approach decomposes control design into structured code generation steps, ensuring physical consistency through execution-based validation and feedback-driven repair. It pioneers the automatic synthesis of decentralized PI controller architectures and their tuning environments directly from dynamic process models. Evaluated on a nonlinear gas preheater benchmark, the generated control schemes—subsequently refined via Bayesian optimization—achieve a 26.5% improvement in closed-loop performance and significantly enhance the transient response of pressure loops, thereby demonstrating the method’s effectiveness and novelty.

This study addresses the challenge of accurately assessing univariate process capability indices (PCIs) in manufacturing under atypical conditions where standard assumptions—particularly normality—are often violated. To overcome this limitation, the authors propose a systematic and unified PCI analysis workflow that integrates outlier detection, normality assessment, optimal distribution fitting, and corresponding PCI computation tailored to diverse distributional assumptions and data characteristics. By offering a structured and actionable framework, the approach enables practitioners to select the most appropriate PCI based on actual process behavior, thereby significantly enhancing both the accuracy and applicability of capability evaluations. This methodology provides a practical and streamlined guide for quality control and process improvement in real-world industrial settings where data frequently deviate from idealized statistical conditions.

This study addresses the limitations of manual visual inspection in traditional carpet manufacturing—namely low efficiency, high subjectivity, and poor consistency—which are incompatible with the quality control demands of high-speed, wide-width looms. To overcome these challenges, the authors propose an online machine vision system integrating synchronized line-scan cameras with combined bright-field and grazing illumination. A domain-specific defect taxonomy is established, and a phased modeling strategy, inspired by the MVTec AD paradigm, is developed: beginning with unsupervised anomaly detection and progressively evolving toward supervised detection and segmentation through a human-in-the-loop annotation flywheel. The system achieves high-resolution detection of subtle structural defects across multi-meter widths, significantly reducing miss rates, improving process sigma levels, and enabling continuous AI model iteration and end-to-end deployment.

Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.