Journal of Manufacturing Systems最新文献_第5页

Cluster system maintenance scheduling multi-objective optimization method integrating time uncertainty GlueVaR risk 集成时间不确定性GlueVaR风险的集群系统维护调度多目标优化方法

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-11-08 DOI: 10.1016/j.jmsy.2025.11.004

Zhongji Su , Zexi Hua , Yongchuan Tang , Qingyuan Zhu , Zhipeng Qi , Lei Wang

Uncertainty in maintenance timing affects planning for systems with time window constraints, creating risks of overflow and operational disruptions. This paper proposes a multi-objective robust optimization method for Cluster system maintenance planning, integrating Glue Value at Risk (GlueVaR) to capture timing uncertainty. The method restores system reliability through maintenance while using GlueVaR to quantify timing uncertainty. Using GlueVaR's multi-parameter features to capture decision-makers' risk preferences, the method embeds maintenance strategies and decision tendencies into system metrics. The approach constructs a multi-objective optimization model with nested maintenance-level decisions and task scheduling. An improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) solves the model, screens optimal solutions, and analyzes time window overflow risk. Simulations on equipment clusters from outdoor signaling systems at railway stations show that maintenance risks decrease by 31.13 %, 45.54 %, and 61.09 % under generally optimistic, relatively conservative, and conservative decision-making tendencies, respectively. These results confirm the correctness and effectiveness of the proposed methodology.

维护时间的不确定性影响了有时间窗口限制的系统规划，造成了溢出和操作中断的风险。本文提出了一种多目标鲁棒优化方法，利用Glue Value at Risk （GlueVaR）来捕获时间不确定性。该方法通过维护恢复系统可靠性，同时使用GlueVaR量化时序不确定性。该方法利用GlueVaR的多参数特征捕捉决策者的风险偏好，将维护策略和决策倾向嵌入到系统度量中。该方法构建了一个具有嵌套维护级决策和任务调度的多目标优化模型。采用改进的基于分解的多目标进化算法（MOEA/D）对模型进行求解，筛选最优解，分析时间窗溢出风险。对火车站室外信号系统设备群的仿真结果表明，在一般乐观、相对保守和保守决策倾向下，维修风险分别降低了31.13 %、45.54 %和61.09 %。这些结果证实了所提出方法的正确性和有效性。

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引用次数: 0

Uncertainty-aware power consumption prediction in customized stainless-steel manufacturing: A comparative study of hierarchical Bayesian and deep neural models 定制不锈钢制造中不确定性感知功耗预测：层次贝叶斯模型与深度神经模型的比较研究

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-11-07 DOI: 10.1016/j.jmsy.2025.10.010

Akarawint Chawalitanont, Atit Bashyal, Hendro Wicaksono

Energy-efficient and data-driven decision-making has become a critical priority in modern manufacturing, particularly in customized or make-to-order (MTO) production where product variability causes large fluctuations in power consumption. Existing prediction models in this domain are often deterministic, lacking the ability to quantify uncertainty and capture hierarchical data dependencies, which limits their reliability for operational use. This study addresses this gap by developing a hierarchical Bayesian learning framework for power consumption prediction in customized stainless-steel manufacturing. The objective is to design models that not only achieve high predictive accuracy but also provide calibrated uncertainty estimates to support risk-aware production decisions. Four models, i.e., Hierarchical Bayesian Linear Regression (HBLR), Hierarchical Bayesian Neural Network (HBNN), Fully Connected Neural Network (FCN), and One-Dimensional Convolutional Neural Network (1D-CNN), were implemented and benchmarked using three inference algorithms: No-U-Turn Sampler (NUTS), Automatic Differentiation Variational Inference (ADVI), and Stein Variational Gradient Descent (SVGD). The innovation lies in systematically quantifying uncertainty using coverage probability, sharpness, and calibration error, and in establishing a unified comparison between probabilistic and deterministic models. Results show that the HBLR–NUTS model achieves the best trade-off between accuracy (RMSE = 11.85) and calibration quality (coverage

\approx

0.98), while ADVI offers near-equivalent performance with significantly lower computation time. These uncertainty-aware predictions can be directly integrated into Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) environments for energy-optimized scheduling and cost-aware planning. The proposed framework provides a scalable, interpretable, and statistically reliable foundation for advancing sustainable, data-driven manufacturing analytics.

节能和数据驱动的决策已成为现代制造业的关键优先事项，特别是在定制或按订单生产（MTO）生产中，产品的可变性会导致功耗的大幅波动。该领域中现有的预测模型通常是确定性的，缺乏量化不确定性和捕获分层数据依赖关系的能力，这限制了它们在操作使用中的可靠性。本研究通过开发用于定制不锈钢制造中功耗预测的分层贝叶斯学习框架来解决这一差距。目标是设计模型，不仅可以实现高预测精度，还可以提供校准的不确定性估计，以支持风险意识生产决策。采用No-U-Turn Sampler （NUTS）、自动微分变分推理（ADVI）和Stein变分梯度下降（SVGD）三种推理算法，实现了层次贝叶斯线性回归（HBLR）、层次贝叶斯神经网络（HBNN）、全连接神经网络（FCN）和一维卷积神经网络（1D-CNN）四个模型，并对其进行了基准测试。创新之处在于利用覆盖概率、清晰度和校准误差系统地量化不确定性，并在概率模型和确定性模型之间建立统一的比较。结果表明，HBLR-NUTS模型在精度（RMSE = 11.85）和校准质量（覆盖率≈0.98）之间达到了最佳平衡，而ADVI模型在计算时间显著缩短的情况下提供了接近等效的性能。这些不确定性预测可以直接集成到制造执行系统（MES）和企业资源规划（ERP）环境中，以实现能源优化调度和成本意识规划。提出的框架为推进可持续、数据驱动的制造分析提供了可扩展、可解释和统计可靠的基础。

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引用次数: 0

Modeling and optimization of positioning setpoints in a roll-to-roll system for optical fiber manufacturing 光纤卷对卷系统定位设定值的建模与优化

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-11-07 DOI: 10.1016/j.jmsy.2025.11.002

Fuxuan Chi , Han Lin , Jinchuan Zheng , Baohua Jia

Roll-to-Roll (R2R) system is widely employed in continuous manufacturing processes requiring high throughput and precise control. Conventional tension-based control mechanism works well for most materials, but it is insufficient for high precision fabrication of optical fibers, which exhibit viscoelastic properties due to their polymer protective layer. Optical fibers can elongate without noticeable tension variation, significantly compromising the position accuracy, a serious issue in applications such as distributed optical sensing. To date, no existing control strategies in R2R systems have adequately addressed this limitation, nor have system models been developed that capture both fiber tension and elongation simultaneously. Here, we propose, to the best of our knowledge, a tension control scheme and a model for R2R systems to simultaneously account for fiber tension and elongation. The model incorporates the analysis of fiber viscoelastic deformation during winding, tension variation induced by winding, utilizing parameter identification and simplification approach developed through combined simulation and experiments. It is verified by experiments with two commonly used polymer-coated fibers, namely acrylic and polyimide, under diverse conditions, including different winding speeds and tension references. The experimental results confirm the model’s accuracy in predicting the elongation and tension variation of the fiber during the R2R winding process. By enabling system analysis and accurate prediction of material elongation, this model facilitates position-aimed pre-compensation in R2R systems, significantly enhancing position accuracy. It is applicable to a wide range of R2R processes for optical fibers (with or without viscoelasticity), such as in the fabrication of Fiber Bragg Grating (FBG) arrays.

卷对卷（R2R）系统广泛应用于要求高吞吐量和精确控制的连续制造过程中。传统的基于张力的控制机制对大多数材料都能很好地工作，但对于光纤的高精度制造来说是不够的，光纤由于具有聚合物保护层而表现出粘弹性。光纤可以在没有明显张力变化的情况下拉长，这极大地影响了位置精度，这是分布式光学传感等应用中的一个严重问题。到目前为止，R2R系统中没有现有的控制策略能够充分解决这一限制，也没有开发出能够同时捕获纤维张力和伸长的系统模型。在这里，我们提出，据我们所知，张力控制方案和R2R系统的模型，同时考虑纤维张力和伸长率。该模型采用仿真与实验相结合的参数辨识和简化方法，分析了缠绕过程中纤维的粘弹性变形和缠绕引起的张力变化。用常用的两种聚合物包覆纤维（丙烯酸纤维和聚酰亚胺纤维）在不同的条件下，包括不同的缠绕速度和张力参考，对其进行了实验验证。实验结果证实了该模型在预测R2R缠绕过程中纤维伸长率和张力变化方面的准确性。通过系统分析和准确预测材料伸长率，该模型有助于在R2R系统中进行定位预补偿，显著提高位置精度。它适用于光纤（具有或不具有粘弹性）的广泛R2R工艺，例如光纤布拉格光栅（FBG）阵列的制造。

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引用次数: 0

A novel typical networked process route discovery approach based on networked sequence similarity and intelligent clustering 一种基于网络序列相似度和智能聚类的新型典型网络化工艺路线发现方法

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-11-05 DOI: 10.1016/j.jmsy.2025.10.015

Xinyu Cao , Binzi Xu , Dengchao Huang , Wei Li , Chun Wang , Maoshan Liu , Yan Wang

In current computer-aided process planning (CAPP) systems, the quality of the typical process routes employed directly influences the overall quality of subsequent process planning. With the advent of the big data era, automated analysis and discovery of typical process routes using advanced artificial intelligence (AI) techniques have become a critical issue to address. Current research primarily focuses on linear/simple process routes, with relatively limited exploration of networked process routes. Therefore, considering the characteristics of networked process routes, this paper proposes a novel approach for discovering typical networked process routes based on networked sequence similarity and intelligent clustering. Specifically, by thoroughly analyzing the information requirements of networked process routes and integrating five embedded process information types, a multi-dimensional process information fusion-based comprehensive similarity measure is constructed using the Kuhn–Munkres (KM) algorithm and principal component analysis (PCA). Furthermore, to ensure the clustering effectiveness of the discovered typical networked process routes, quantity and radius soft constraints are introduced into the traditional typical process route discovery problem. Two nutcracker optimization algorithm (NOA)-optimized affinity propagation (AP) algorithms (i.e., NOA-OAP and NOA-IAP) are proposed to address this problem, aiming to enhance clustering performance and identify more suitable and practical typical networked process routes for CAPP. Finally, numerical illustrations validate that the proposed similarity measure can effectively distinguish subtle differences among various networked process routes, and the two proposed clustering algorithms can discover more representative and effective typical process routes.

在当前的计算机辅助工艺规划（CAPP）系统中，典型工艺路线的质量直接影响后续工艺规划的整体质量。随着大数据时代的到来，使用先进的人工智能（AI）技术自动分析和发现典型的工艺路线已成为一个关键问题。目前的研究主要集中在线性/简单工艺路线上，对网络化工艺路线的探索相对有限。因此，考虑到网络化工艺路线的特点，本文提出了一种基于网络化序列相似性和智能聚类的典型网络化工艺路线发现方法。具体而言，通过深入分析网络化工艺路线的信息需求，整合5种嵌入式工艺信息类型，利用Kuhn-Munkres （KM）算法和主成分分析（PCA），构建了基于多维工艺信息融合的综合相似度测度。此外，为了保证发现的典型网络化工艺路线的聚类有效性，在传统的典型工艺路线发现问题中引入了数量和半径软约束。针对这一问题，提出了两种胡桃夹子优化算法（nutcracker optimization algorithm， NOA）优化的亲和传播（affinity propagation， AP）算法（即NOA- oap和NOA- iap），旨在提高聚类性能，为CAPP找到更适合和实用的典型网络化工艺路线。最后，通过数值算例验证了所提出的相似度度量方法能够有效区分各种网络化工艺路线之间的细微差异，所提出的两种聚类算法能够发现更具代表性和有效性的典型工艺路线。

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引用次数: 0

Towards a next-generation LLM empowered low-code programming industrial robotic system for human-centric smart manufacturing 面向以人为中心的智能制造的下一代LLM授权低代码编程工业机器人系统

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.jmsy.2025.10.012

Wenhang Dong , Dongpeng Li , Yuchen Ji , Hongpeng Chen , Shimin Liu , Zheng Ma , Fang Hao , Yuqi Ji , Hongwen Xing , Pai Zheng

Industrial robotic systems have been widely adopted in modern industries due to their advantages in high flexibility and strong adaptability. However, these systems are often limited by fragmented workflows, high cognitive demands on operators, and complex interaction programming. To address these issues, this study proposes a next-generation low-code programming framework empowered by large language models (LLMs), aiming to advance human-centric smart manufacturing (HCSM). By integrating the reasoning capabilities of LLMs into industrial robotic systems, the framework prioritizes intuitive, efficient, and operator-friendly interaction, establishing a novel paradigm for industrial applications. Additionally, the system incorporates a cognitive assistance module to reduce the cognitive burden on unskilled operators. Moreover, an LLM-based low-code programming module was designed, employing a multi-agent mechanism for intent recognition, parameter extraction, and human verification, thereby significantly enhancing the system’s ability to robustly handle unstructured natural language instructions in industrial environments. Finally, the system was validated through a case study on aircraft panel drilling, demonstrating its practicality and reliability while supporting unskilled operators in performing complex tasks. This validation indicates that the proposed method has broad potential for industrial applications.

工业机器人系统以其灵活性高、适应性强等优点在现代工业中得到广泛应用。然而，这些系统通常受到分散的工作流程、对操作员的高认知要求和复杂的交互编程的限制。为了解决这些问题，本研究提出了由大型语言模型（llm）支持的下一代低代码编程框架，旨在推进以人为中心的智能制造（HCSM）。通过将llm的推理能力集成到工业机器人系统中，该框架优先考虑直观、高效和操作友好的交互，为工业应用建立了一种新的范例。此外，该系统还集成了一个认知辅助模块，以减轻非熟练操作员的认知负担。此外，设计了基于llm的低码编程模块，采用多智能体机制进行意图识别、参数提取和人工验证，显著提高了系统在工业环境中鲁棒处理非结构化自然语言指令的能力。最后，通过飞机面板钻井的案例研究验证了该系统的实用性和可靠性，同时支持非熟练操作员执行复杂任务。这表明该方法具有广泛的工业应用潜力。

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引用次数: 0

Using a digital twin and smart services to enable automatic generation of context-sensitive instructions 使用数字孪生和智能服务来自动生成上下文敏感指令

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.jmsy.2025.10.007

Karl Lossie , Jan Hendrik Hellmich , Junjie Liang , Jonas Baum , Amon Göppert , Dennis Grunert , Robert H. Schmitt

The increasing diversity and shorter life cycles of technical products pose significant challenges for manufacturing companies, particularly in the context of providing specific and context-sensitive instructions to employees, especially in domains including maintenance, assembly and disassembly. This challenge holds significant importance in the context of the current skilled worker shortage. This paper proposes a solution by leveraging digital twin technology and smart services to automate the generation of context-sensitive instructions. The research outlines the development of a smart service system that uses real-time data from digital twins to create and deliver adaptive and user-specific instructions via smart devices. A conceptual design of the smart service system, a prototypical implementation using a rolling mill maintenance task, and the verification and validation of the developed system were carried out. The results indicate that the proposed system effectively addresses the challenges of traditional manual instructions, enhancing efficiency, accuracy, and user satisfaction.

技术产品的日益多样化和更短的生命周期给制造公司带来了巨大的挑战，特别是在向员工提供具体和上下文敏感的指令的背景下，特别是在维护、组装和拆卸等领域。在当前技术工人短缺的背景下，这一挑战具有重要意义。本文提出了一种利用数字孪生技术和智能服务来自动生成上下文敏感指令的解决方案。该研究概述了智能服务系统的发展，该系统使用来自数字孪生的实时数据，通过智能设备创建并提供自适应和用户特定的指令。对智能服务系统进行了概念设计，利用轧机维护任务进行了原型实现，并对所开发的系统进行了验证和验证。结果表明，该系统有效地解决了传统手工指令的挑战，提高了效率、准确性和用户满意度。

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引用次数: 0

Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins 使用几何和过程数字双胞胎的增材制造部件对隐形网络物理攻击的操作弹性

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-10-31 DOI: 10.1016/j.jmsy.2025.10.009

Jeremy Cleeman , Adrian Jackson , Anandkumar Patel , Zihan Wang , Thomas Feldhausen , Chenhui Shao , Hongyi Xu , Rajiv Malhotra

Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.

对增材制造（AM）数字骨干的网络物理攻击可能会损害打印部件的功能。他们可以改变数字几何中的特征以引入几何缺陷（例如，缺失的圆角）或改变工艺参数以创建局部缺陷（例如，空洞）。解决与现有解决方案相关的停机时间、浪费和质量恶化需要操作弹性，即，在没有生产停止或部件处置（保持产量）的情况下，快速消除或中断缺陷形成（以保持部件功能）。由于攻击引起的改变具有固有的不可预测性，缺乏对原始几何模型的访问以识别改变的几何特征，以及通过攻击驱动的实时不受控制（或外生）参数的改变在过程中强加未知过程动力学，因此无法满足这一需求。这项工作通过创建两个数字双胞胎（DT）首次建立了上述操作弹性。Geo-DT基于一种独特的物理场驱动软传感器和拓扑优化方法。过程数字孪生（Pro-DT）将局部缺陷量化与一种新的强化学习公式和训练方法相结合。这些方法进步的重要性和我们方法的可扩展性在一个真实的AM测试平台上进行了检验。研究表明，Geo-DT可以在不需要原始数字几何或明确的攻击改变几何特征知识的情况下纠正几何缺陷。此外，Pro-DT可以加速局部缺陷的实时破坏，尽管攻击驱动了未知过程动力学的强加。我们讨论了我们的框架如何超越当前对攻击前安全性和攻击中检测的关注，以实现AM及其他领域的弹性。

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引用次数: 0

A novel approach to digital twin-based energy efficiency monitoring and failure analysis in industrial applications 工业应用中基于数字孪生的能效监测和故障分析的新方法

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-10-31 DOI: 10.1016/j.jmsy.2025.10.011

Mohsen Zeynivand, Parisa Esmaili, Loredana Cristaldi, Giambattista Gruosso

Machine tools are critical to modern manufacturing, yet their high energy consumption and vulnerability to faults present significant operational challenges. While predictive models can enhance energy optimization and fault diagnosis, their performance is often constrained by the scarcity of high-quality training data. To address this gap, this study presents a real-time digital twin (DT) framework that integrates OPAL-RT HIL simulation with OPC-UA-based cloud communication. The system enables both energy monitoring and synthetic fault data generation under diverse machining conditions. The DT operates in a bidirectional loop with a cloud-based data acquisition layer, allowing real-time parameter input and retrieval of simulated outputs. Model fidelity is verified by aligning simulation results with real-world CNC machine measurements and further confirmed through pattern-based external validation. The framework is applied to analyze energy consumption across varying machining parameters — such as electrospindle speed, feed rate, tool length, and depth of cut — and to simulate bearing fault scenarios for evaluating their impact on power consumption. These simulations produce labeled datasets suitable for future diagnostic and predictive maintenance applications. This work delivers a validated, closed-loop DT framework that unites high-fidelity OPAL-RT simulation, real-time OPC-UA data exchange, and synthetic data generation, extending predictive maintenance capabilities beyond those of prior modeling or diagnostic approaches. The proposed methodology offers a scalable foundation for energy-aware machining and real-time fault detection, contributing to sustainable manufacturing practices and operational resilience in smart industrial systems.

机床对现代制造业至关重要，但它们的高能耗和易故障性给操作带来了重大挑战。虽然预测模型可以增强能量优化和故障诊断，但其性能往往受到高质量训练数据的缺乏的限制。为了解决这一差距，本研究提出了一个实时数字孪生（DT）框架，该框架将OPAL-RT HIL仿真与基于opc - ua的云通信集成在一起。该系统能够在各种加工条件下进行能量监测和综合故障数据生成。DT在一个基于云的数据采集层的双向循环中工作，允许实时参数输入和模拟输出的检索。通过将仿真结果与实际数控机床测量结果比对来验证模型的保真度，并通过基于模式的外部验证进一步确认。该框架用于分析不同加工参数（如电主轴速度、进给速度、刀具长度和切削深度）的能耗，并模拟轴承故障场景，以评估其对功耗的影响。这些模拟产生适合未来诊断和预测性维护应用的标记数据集。这项工作提供了一个经过验证的闭环DT框架，该框架将高保真OPAL-RT仿真、实时OPC-UA数据交换和合成数据生成结合在一起，扩展了预测维护能力，超越了先前的建模或诊断方法。所提出的方法为能源感知加工和实时故障检测提供了可扩展的基础，有助于智能工业系统的可持续制造实践和操作弹性。

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引用次数: 0

Cognitive Digital Twin frameworks in manufacturing—A critical survey, evaluation criteria, and future directions 制造业中的认知数字孪生框架——关键调查、评估标准和未来方向

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-10-31 DOI: 10.1016/j.jmsy.2025.10.004

Yangyang Liu , Tang Ji , Xiangyu Guo , Xun Xu , Jan Polzer

Cognitive Digital Twin (CDT) represents an advanced evolution of traditional Digital Twin (DT) technology, overcoming constraints in perception, reasoning, learning, and self-evolution to meet the growing demands of complex and dynamic industrial systems. This study first analyses the conceptual evolution of CDT and categorises it into three categories based on differing research trends. Through a comparative analysis of the definitions across these categories, we summarise the core features of CDT. Based on these characteristics, this study proposes a novel evaluation criteria for CDT, which systematically assesses its performance in cognitive functions such as perception, reasoning, and memory. Finally, building upon the preceding analysis, we identify the key challenges currently facing the field and envision potential future research directions to provide theoretical insights and practical guidance for developing next-generation DT technology.

认知数字孪生（CDT）是传统数字孪生（DT）技术的高级进化，克服了感知、推理、学习和自我进化方面的限制，以满足复杂和动态工业系统日益增长的需求。本研究首先分析了CDT的概念演变，并根据不同的研究趋势将其分为三类。通过对这些分类定义的比较分析，我们总结了CDT的核心特征。基于这些特点，本研究提出了一种新的CDT评价标准，该标准系统地评价了CDT在感知、推理和记忆等认知功能方面的表现。最后，在上述分析的基础上，我们确定了该领域目前面临的主要挑战，并展望了潜在的未来研究方向，为开发下一代DT技术提供理论见解和实践指导。

引用次数: 0

A dual-arm robotic system for automated multi-branch wire harness assembly in automotive industry 汽车工业多支路线束自动装配的双臂机器人系统

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-10-30 DOI: 10.1016/j.jmsy.2025.10.008

Pablo Malvido Fresnillo , Saigopal Vasudevan , Wael M. Mohammed , Jose A. Perez Garcia , Jose L. Martinez Lastra

Wire harnesses are critical components in modern vehicles, responsible for transmitting electrical signals and power to sensors and actuators. Despite the high level of automation in the automotive industry, wire harness manufacturing still relies heavilylargely depends on manual assembly. This is due to the significant challenges posed by the process, such as the complexity of perceiving and manipulating flexible materials and the high degree of customization required. As a result, existing solutions only address specific assembly tasks, rather than the entire processare fragmented, unable to scale to full production, and remain economically unviable for high-mix scenarios. To bridge this gap, this paper presents a novel robotic system for fully automating wire harness assembly. The system adopts a task-level programming methodology that leverages process knowledge to enable fast and easy reconfiguration. Additionally, it incorporates specific solutions to address key challenges in multi-branch wire harness manipulation, such as cable separation and entanglement prevention. The system’s performance was evaluated in two real-world assembly scenarios using a dual-arm robot. Experimental results demonstrate the system’s effectiveness and ease of reconfiguration, achieving success rates of 55% and 73% in two complex multi-branch wire harness assembly processes, and highlight areas of improvement, which will be further investigated in future works. The system repository is openly available allowing other researchers to build their solutions upon the proposed methodology.

线束是现代车辆的关键部件，负责向传感器和执行器传输电信号和电力。尽管汽车工业自动化程度很高，但线束制造仍然在很大程度上依赖于人工组装。这是由于该过程带来的重大挑战，例如感知和操纵柔性材料的复杂性以及所需的高度定制。因此，现有的解决方案只能解决特定的组装任务，而不是整个过程的碎片化，无法扩展到完整的生产，并且在高混合场景中仍然不具有经济可行性。为了弥补这一差距，本文提出了一种全新的自动化线束装配机器人系统。该系统采用任务级编程方法，利用过程知识实现快速简便的重新配置。此外，它还包含特定的解决方案，以解决多分支线束操作中的关键挑战，例如电缆分离和防止缠结。该系统的性能在使用双臂机器人的两个真实装配场景中进行了评估。实验结果证明了该系统的有效性和可重构性，在两个复杂的多支路线束装配过程中实现了55%和73%的成功率，并突出了改进的领域，将在未来的工作中进一步研究。系统存储库是公开可用的，允许其他研究人员在建议的方法上构建他们的解决方案。

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