Journal of Manufacturing Systems最新文献_第3页

Enhancing manual inspection in semiconductor manufacturing with integrated augmented reality solutions 利用集成增强现实解决方案加强半导体制造中的人工检测

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-11 DOI: 10.1016/j.jmsy.2024.10.028

Chih-Hsing Chu, Chen-Yu Weng, Yu-Tzu Chen

On-site routine inspection often remains a manual operation in the semiconductor manufacturing industry because implementing automated solutions can be costly and technically challenging in such a highly controlled and complex environment. The manual inspection is prone to errors due to the impact of demanding physical and mental workloads. This paper presents an integrated Augmented Reality (AR) solution developed to assist manual inspection tasks in the supporting areas of semiconductor manufacturing, referred to as the sub-fab. The solution is accessible to a human worker wearing an AR headset during the inspection process at the location. We propose a system framework to deploy computational intelligences of varying granularity provided by the solution across cloud, edge, and device levels, accommodating constraints within the sub-fab. A machine maintenance module helps estimate and monitor the health condition of running scrubbers. Incorrect intentions performed by the worker on the scrubber control panel are detected through hand gesture recognition. This instantly prompts warning messages in the AR headset to prevent subsequent wrong actions. The solution can also identify abnormal device states through 6D pose estimation of objects enabled by machine learning models. A test scenario demonstrates how these functional features enhance the inspection efficiency and quality by reducing human workloads. This work demonstrates that semiconductor manufacturing may require AR-assisted functions different from those needed or common in other industrial sectors. It also highlights the potential of AR technology for reducing operational human errors in manual tasks.

在半导体制造业中，现场例行检查通常仍是人工操作，因为在这样一个高度受控的复杂环境中，实施自动化解决方案不仅成本高昂，而且在技术上具有挑战性。由于高强度的体力和脑力劳动的影响，人工检测很容易出错。本文介绍了一种集成的增强现实（AR）解决方案，用于辅助半导体制造辅助区域（称为子工厂）的人工检测任务。佩戴 AR 头显的人类工人可在现场检测过程中使用该解决方案。我们提出了一个系统框架，用于在云端、边缘和设备层面部署该解决方案提供的不同粒度的计算智能，以适应子工厂内的各种限制。机器维护模块有助于估计和监控运行中的洗涤器的健康状况。通过手势识别，可检测到工人在洗地机控制面板上执行的不正确意图。这会立即在 AR 头显中提示警告信息，以防止后续的错误操作。该解决方案还可以通过机器学习模型对物体进行 6D 姿态估计，识别异常设备状态。一个测试场景演示了这些功能特性如何通过减少人工工作量来提高检测效率和质量。这项工作表明，半导体制造所需的 AR 辅助功能可能不同于其他工业部门所需或常见的功能。它还凸显了 AR 技术在减少人工任务中人为操作失误方面的潜力。

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引用次数: 0

A new cause-mechanism independence estimation based cross-domain learning method for machining deformation prediction 一种基于原因机制独立性估计的新型跨域学习方法，用于加工变形预测

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-09 DOI: 10.1016/j.jmsy.2024.11.002

Yang Ni , Yingguang Li , Changqing Liu , Xu Liu

Monitoring data-based machining deformation prediction is fundamental for accurate deformation control and product quality guarantee. For problems where involved unobservable variables like residual stress that can lead to data distribution bias, causal cross-domain learning methods have prominent advantages over other pure data-driven methods by shifting cause distributions and mechanisms. However, existing causal methods are based on the hypothesis that cause and mechanism are independent, which ignores the corresponding changes of mechanism across domains and can limit accuracies. This paper proposes a new causal cross-domain learning method based on cause-mechanism independence estimation, where the hypothesis is broken by taking the dependence of cause and mechanism into consideration. A cause-mechanism independence estimator is established by introducing the structural integral of mechanism derivative multiplies cause distribution, and the estimation value can measure the cross-domain changes of mechanism. As a result, the proposed method based predicting model can make efficient distribution shifts according to the estimation. The machining of aero-engine casings is taken as a case study, and experimental results show that the proposed method could predict the deformation well with limited target domain data. Besides, the proposed method can be readily extended to other cross-domain regression problems involved with unobservable variables.

基于数据监测的加工变形预测是精确控制变形和保证产品质量的基础。对于涉及残余应力等不可观测变量、可能导致数据分布偏差的问题，跨域因果学习方法通过转移原因分布和机制，与其他纯数据驱动方法相比具有突出优势。然而，现有的因果学习方法都是基于原因和机制相互独立的假设，忽略了机制在不同领域间的相应变化，会限制学习的准确性。本文提出了一种基于原因-机制独立性估计的新型因果跨域学习方法，该方法通过考虑原因和机制的依赖性打破了这一假设。通过引入机制导数乘以原因分布的结构积分，建立了原因-机制独立性估计器，其估计值可以衡量机制的跨域变化。因此，所提出的基于预测模型的方法可以根据估计值进行有效的分布转移。以航空发动机壳体的加工为例，实验结果表明所提出的方法能在有限的目标域数据下很好地预测变形。此外，提出的方法还可扩展到其他涉及不可观测变量的跨域回归问题。

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引用次数: 0

Simulation-based Digital Twin for enhancing human-robot collaboration in assembly systems 基于仿真的数字孪生系统，用于加强装配系统中的人机协作

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-09 DOI: 10.1016/j.jmsy.2024.10.024

Antonio Cimino , Francesco Longo , Letizia Nicoletti , Vittorio Solina

The advent of new technologies and paradigms such as the Internet of Things (IoTs), Digital Twin (DT), Human-Robot Collaboration (HRC), is offering immense opportunities to improve the performance of manufacturing systems, but also opening new challenges. The current scientific literature highlights the presence of numerous theoretical studies, but limited real-life applications, and the need to address interoperability issues, with the aim of valorizing the data continuously generated by humans, robots, machines. This research presents a novel simulation-based DT, designed for supporting HRC optimization in assembly systems. The proposed approach is tested and validated, through a case study in the automotive sector, specifically focusing on an assembly line for car front doors. The results show that it is possible to achieve HRC improvements through the assessment of different working configurations. Furthermore, it is explained how the simulation-based DT, by leveraging the FIWARE/FIROS paradigm, can effectively and efficiently interact with other systems, to enable real-time data exchange, which is nowadays one of the main open research challenges.

物联网（IoTs）、数字孪生（DT）、人机协作（HRC）等新技术和新模式的出现，为提高制造系统的性能提供了巨大的机遇，同时也带来了新的挑战。目前的科学文献强调了大量理论研究的存在，但现实生活中的应用却很有限，而且需要解决互操作性问题，目的是使人类、机器人和机器不断产生的数据发挥价值。本研究提出了一种新颖的基于模拟的 DT，旨在支持装配系统中的热轧卷优化。通过对汽车行业的案例研究，特别是对汽车前门装配线的研究，对所提出的方法进行了测试和验证。结果表明，通过评估不同的工作配置，可以实现 HRC 的改进。此外，还解释了基于仿真的 DT 如何利用 FIWARE/FIROS 范式，有效地与其他系统进行交互，从而实现实时数据交换，这也是当今主要的开放式研究挑战之一。

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引用次数: 0

LogicLSTM: Logically-driven long short-term memory model for fault diagnosis in gearboxes LogicLSTM：用于齿轮箱故障诊断的逻辑驱动长短期记忆模型

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-06 DOI: 10.1016/j.jmsy.2024.10.003

Eduard Hogea , Darian M. Onchiş , Ruqiang Yan , Zheng Zhou

This article introduces LogicLSTM, a hybrid neuro-symbolic model obtained by logically guiding a pretrained Long Short-Term Memory (LSTM) network with the support of a customized Logic Tensor Network (LTN). The model is further optimized by explainable AI techniques, for a refined fault classification of time-series data coming from industrial gearboxes. The framework leverages the intrinsic strengths of LSTMs deep recurrent networks for temporal data processing with logical reasoning capabilities, to improve prediction accuracy and interpretability of the classification. Our approach addresses the challenges of extracting relevant data features and integrating connectionist and symbolic methodologies to form a cohesive predictive model. Results from extensive testing show that our model significantly outperforms traditional LSTM models, particularly in complex fault scenarios where conventional methods may fail. Specifically, the hybrid model demonstrates a 16.03% average improvement in accuracy over standard LSTM models under conditions of sufficient data availability, and a 8.56% improvement in scenarios where data is scarce. This research not only demonstrates the potential of hybrid models in industrial applications but also highlights the importance of explainability in AI systems for critical decision-making processes. The proposed model’s ability to interpret and explain its predictions makes it a valuable tool for advancing predictive maintenance strategies within the Industry 4.0 framework.

本文介绍的 LogicLSTM 是一种混合神经符号模型，它是在定制的逻辑张量网络（LTN）的支持下，通过逻辑引导预训练的长短期记忆（LSTM）网络而获得的。该模型通过可解释人工智能技术进一步优化，可对来自工业齿轮箱的时间序列数据进行精细故障分类。该框架利用 LSTMs 深度递归网络在时间数据处理方面的固有优势和逻辑推理能力，提高了预测的准确性和分类的可解释性。我们的方法解决了提取相关数据特征、整合连接主义和符号方法以形成一个内聚预测模型的难题。大量测试结果表明，我们的模型明显优于传统的 LSTM 模型，尤其是在传统方法可能失效的复杂故障场景中。具体来说，在数据充足的条件下，混合模型比标准 LSTM 模型的平均准确率提高了 16.03%，而在数据稀缺的情况下，平均准确率提高了 8.56%。这项研究不仅证明了混合模型在工业应用中的潜力，还强调了人工智能系统在关键决策过程中可解释性的重要性。所提出的模型能够解释和说明其预测结果，这使其成为在工业 4.0 框架内推进预测性维护策略的重要工具。

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引用次数: 0

Towards data-driven quality monitoring for advanced metal inert gas welding processes in body-in-white 对先进的白车身金属惰性气体焊接工艺进行数据驱动的质量监测

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-05 DOI: 10.1016/j.jmsy.2024.10.013

Michael Luttmer , Matthias Weigold , Heiko Thaler , Jürgen Dongus , Anton Hopf

In recent years, numerous monitoring approaches have been developed in the field of intelligent welding manufacturing to predict quality-related characteristics using process data and artificial intelligence-based techniques. While most investigations have focused on welding steel with conventional gas metal arc welding processes, the welding of aluminum and its alloys using advanced process variants has been less explored. This work addresses this gap by investigating data-driven methods for fault diagnosis and detection in an advanced metal inert gas welding process commonly used in body-in-white manufacturing. To this end, electrical, acoustic, and spectroscopic signals were recorded from numerous welding tests simulating typical fault causes. Various predictive models, ranging from traditional machine learning algorithms to state-of-the-art deep learning techniques, were trained and evaluated for classifying faulty seams and identifying their root causes. The results demonstrate that combining sensor data enhances the performance of predictive models compared to using individual sensors alone. However, a deep learning approach based solely on electrical signals emerged as the best solution for both use cases, considering both the results and practical aspects. Overall, the experiments highlight the significant potential of data-driven techniques to enhance quality monitoring in advanced MIG welding processes, promoting their more widespread adoption in body-in-white manufacturing.

近年来，在智能焊接制造领域开发了许多监测方法，利用过程数据和基于人工智能的技术预测与质量相关的特性。大多数研究都集中在使用传统气体金属弧焊工艺焊接钢材方面，而对使用先进工艺变体焊接铝及其合金的研究则较少。本研究针对这一空白，研究了白车身制造中常用的先进金属惰性气体焊接工艺的故障诊断和检测的数据驱动方法。为此，从模拟典型故障原因的大量焊接测试中记录了电气、声学和光谱信号。从传统的机器学习算法到最先进的深度学习技术，对各种预测模型进行了训练和评估，以对故障焊缝进行分类并确定其根本原因。结果表明，与单独使用单个传感器相比，结合传感器数据可提高预测模型的性能。不过，考虑到结果和实际情况，仅基于电信号的深度学习方法成为这两种使用情况下的最佳解决方案。总之，实验凸显了数据驱动技术在加强先进 MIG 焊接工艺质量监控方面的巨大潜力，从而促进了其在白车身制造中的更广泛应用。

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引用次数: 0

Joint production, maintenance, and quality control in manufacturing systems with imperfect inspection 不完善检验制造系统中的联合生产、维护和质量控制

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-04 DOI: 10.1016/j.jmsy.2024.10.020

Abdessamad Ait El Cadi , Ali Gharbi , Karem Dhouib , Abdelhakim Artiba

This paper proposed a joint production control, preventive maintenance, and inspection policy for manufacturing systems prone to failures, quality degradation and quality inspection errors. A stochastic mathematical model is developed taking into account all possible scenarios contingent to imperfect quality inspection errors, while integrating age-based preventive maintenance, dynamic production rates, and sampling inspection plans. The model accounts for both Type I and Type II inspection errors and optimizes the joint policy key parameters, including safety stock levels, preventive maintenance thresholds, and inspection sample size. The model is validated using a 95 % confidence interval obtained from experiments with simulation model that imitates the studied system dynamics when it is controlled by the proposed joint policy. A sensitivity analysis is carried out to give a deeper comprehension of the problem and the complex interactions at play. The study explores the impact of system’s parameters on the new joint policy that accounts for inspection errors, thereby contributing valuable insights to the field of manufacturing systems management. Ultimately, a comprehensive comparative analysis seeks to establish the superiority of the proposed joint policy over existing ones documented in the literature. The proposed policy consistently outperformed alternative approaches, with an overall cost reduction of up to 87 %.

本文针对容易发生故障、质量下降和质量检验错误的制造系统，提出了一种生产控制、预防性维护和检验联合政策。该论文建立了一个随机数学模型，考虑了所有可能出现的质量检验误差不完善的情况，同时整合了基于年龄的预防性维护、动态生产率和抽样检验计划。该模型考虑了 I 类和 II 类检验误差，并优化了联合政策的关键参数，包括安全库存水平、预防性维护阈值和检验样本量。该模型通过仿真模型实验获得 95% 的置信区间进行验证，仿真模型模仿了所研究的系统动态，并由建议的联合政策进行控制。为了更深入地理解问题和复杂的相互作用，还进行了敏感性分析。研究探讨了系统参数对考虑检测误差的新联合策略的影响，从而为制造系统管理领域提供了宝贵的见解。最后，通过全面的比较分析，力求确定所提出的联合政策优于文献中记载的现有政策。所提出的政策始终优于其他方法，总体成本降低高达 87%。

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引用次数: 0

Sustainable management of electric vehicle battery remanufacturing: A systematic literature review and future directions 电动汽车电池再制造的可持续管理：系统文献综述与未来方向

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-04 DOI: 10.1016/j.jmsy.2024.10.006

Alessandro Neri , Maria Angela Butturi , Rita Gamberini

The increasing adoption of electric vehicles (EVs) and the corresponding surge in lithium-ion battery (LIB) production have intensified the focus on sustainable end-of-life (EOL) management strategies (i.e., reuse, repurpose, remanufacture, and recycle). This paper presents a systematic literature review of the entire remanufacturing process of LIBs, aiming to offer a cohesive perspective on the approach that reduces the environmental impact of LIB waste by prolonging their lifecycle for reuse in their original EV applications. It reveals major issues from EOL collection to renewed batteries, clustering results into six research streams, and proposes a research agenda to develop integrative, data-driven models that incorporate technical, economic, and environmental considerations. Key findings highlight the need for standardised, non-damaging joining techniques, enhanced safety protocols for disassembly, and scalable cathode re-functionalisation methods. Recommendations include leveraging advanced technologies such as AI, machine learning, IoT, and blockchain to optimise remanufacturing processes and enhance supply chain transparency and efficiency. This comprehensive review aims to foster the development of sustainable remanufacturing practices, contributing to the circular economy and supporting the growth of the EV industry.

随着电动汽车（EV）的日益普及和锂离子电池（LIB）产量的相应激增，人们更加关注可持续的报废（EOL）管理策略（即再利用、再用途、再制造和再循环）。本文对锂电池的整个再制造过程进行了系统的文献综述，旨在从一个统一的视角探讨如何通过延长锂电池的生命周期来减少其废弃物对环境的影响，从而将其重新用于最初的电动汽车应用中。报告揭示了从 EOL 收集到电池更新的主要问题，将研究成果归纳为六个研究流，并提出了一个研究议程，以开发综合的、数据驱动的模型，将技术、经济和环境因素纳入其中。主要研究结果强调了对标准化、无损伤连接技术、强化拆卸安全协议和可扩展阴极再功能化方法的需求。建议包括利用人工智能、机器学习、物联网和区块链等先进技术来优化再制造流程，提高供应链的透明度和效率。本综合评论旨在促进可持续再制造实践的发展，为循环经济做出贡献，并支持电动汽车行业的发展。

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引用次数: 0

Robotic disc grinding path planning method based on multi-objective optimization for nuclear reactor coolant pump casing 基于多目标优化的核反应堆冷却剂泵壳机器人盘磨路径规划方法

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-01 DOI: 10.1016/j.jmsy.2024.10.021

Bo Zhou , Tongtong Tian

In the nuclear industry, the finishing grinding work of the nuclear reactor coolant pump (RCP) casing is mainly performed manually. Uncontrollable grinding tasks cause the grinding disc to be easily worn during the grinding process, which will greatly affect the grinding accuracy and efficiency. This paper introduces a path planning method that can efficiently and accurately perform a disc grinding task on an RCP casing. First, we provide a wear model for rigid grinding discs and verify its accuracy through finite element simulations and experiments. It can be used to predict the wear conditions of grinding discs during grinding. Then, a series of linear geodesic offset paths with the shortest path length characteristic can be generated and converted to NURBS interpolation paths. The velocity, acceleration, and jerk of the of the NURBS interpolated path generated by the S-shaped acceleration/deceleration (ACC/DEC) feedrate planning method in Cartesian space can be converted into the corresponding angular velocity, acceleration, and jerk of each joint in joint space to ensure that the grinding tasks can be performed under appropriate kinematic constraints; Then, an improved NSGA-II algorithm is proposed and its performance is verified based on benchmark test problem suite in three indicators. The verification results showed that the solution set generated by the proposed algorithm has good distribution uniformity, is closer to the true boundary, and has good convergence compared with other advanced optimization algorithms; Furthermore, by substituting the multi-objective optimization functions and kinematic constraints into the improved NSGA-II algorithm, the compromise minimization problem of grinding time, impact, and disc wear can be solved. The simulation and experimental results demonstrate the superiority and effectiveness of the optimized geodesic grinding paths in terms of grinding precision, accuracy, stability, and efficiency. In contrast, multi-directional paths, e.g., optimized cycloid paths, will produce varying grinding contact forces and varying disc sliding velocities, which will lead to more complex material removal situations, thus affecting the accuracy of the optimization solution.

在核工业中，核反应堆冷却剂泵（RCP）外壳的精磨工作主要由人工完成。磨削任务的不可控性导致磨盘在磨削过程中容易磨损，从而极大地影响磨削精度和效率。本文介绍了一种路径规划方法，可以高效、准确地完成 RCP 外壳的圆盘磨削任务。首先，我们提供了刚性磨盘的磨损模型，并通过有限元模拟和实验验证了其准确性。该模型可用于预测磨削过程中磨盘的磨损状况。然后，生成一系列具有最短路径长度特征的线性大地偏移路径，并将其转换为 NURBS 插值路径。在直角坐标空间中，通过 S 形加速度/减速度（ACC/DEC）进给率规划方法生成的 NURBS 插值路径的速度、加速度和颠簸可转换为关节空间中各关节的相应角速度、加速度和颠簸，以确保在适当的运动学约束条件下执行磨削任务；然后，提出了一种改进的 NSGA-II 算法，并基于基准测试问题套件对其性能进行了三项指标验证。验证结果表明，与其他先进的优化算法相比，该算法生成的解集具有良好的分布均匀性，更接近真实边界，且具有良好的收敛性；此外，通过将多目标优化函数和运动学约束条件代入改进的 NSGA-II 算法，可以解决磨削时间、冲击和磨盘磨损的折中最小化问题。仿真和实验结果表明，优化后的测地线磨削路径在磨削精度、准确性、稳定性和效率方面都具有优越性和有效性。相比之下，多方向路径（如优化摆线路径）会产生不同的磨削接触力和不同的磨盘滑动速度，从而导致更复杂的材料去除情况，从而影响优化方案的准确性。

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引用次数: 0

Research on digital twin monitoring system during milling of large parts 大型部件铣削过程中的数字孪生监控系统研究

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-01 DOI: 10.1016/j.jmsy.2024.10.027

Yao Lu , Caixu Yue , Xianli Liu , Lihui Wang , Steven Y. Liang , Wei Xia , Xueping Dou

In the milling process of large-scale critical parts of energy equipment, the rigidity of the tool can be lower than that of workpieces, which makes it easy to trigger tool chatter. When the vibration is large, the tool cannot act on the workpiece and cannot effectively remove the material. In severe cases, the tool will be embedded inside the workpiece, resulting in the tool and the workpiece being scrapped at the same time. At the same time, in the event of programming errors, the tool or shank could interfere or collide with workpieces or worktable, which may damage the machine parts and reduce the machining accuracy of machine tool, leading to economic losses and even casualties. In response to the problems of tool chatter and tool collision in the milling process, this paper has done four steps as follows to improve the monitoring, modeling, and control of the machining dynamics integrity. First of all, the study constructs a digital twin monitoring system framework for the milling process of large parts, utilizes Unity 3D to build the digital twin virtual system, designs and develops the relevant functions of the virtual machine tool. Secondly, this study establishes a dynamic cutting thickness model for high-feed milling cutter and a milling dynamics model for rigid parts, and builds the stability lobe diagram (SLD) based on the modal parameters and milling force coefficients. In turn, the study obtains the chatter adaptive threshold of the digital twin monitoring system with the guidance of the stabilizing leaf petal diagram. Thirdly, this study also utilizes OPC UA protocol and LabVIEW to acquire the signals of spindle position, speed, acceleration, etc., and process them. Based on the digital twin front-end technology, it will realize user interaction, machine tool collision prevention, and cutting parameters calculation; then based on the digital twin back-end technology, it will obtain the theoretical guidance for chatter monitoring, suppression, and prediction. Finally, it proposes a driver update database based on the MySQL, and utilizes it to update the back-end model of the digital twin monitoring system. According to the experimental test of the digital twin monitoring system under realistic machining process conditions, the results show that the system has a certain improvement in processing safety and processing quality, which has carries practical value and guiding significance.

在能源设备大型关键零件的铣削过程中，刀具的刚性会低于工件的刚性，容易引发刀具颤振。当振动较大时，刀具无法作用于工件，不能有效地去除材料。严重时，刀具会嵌入工件内部，导致刀具和工件同时报废。同时，在编程错误的情况下，刀具或刀柄可能会与工件或工作台发生干涉或碰撞，从而损坏机床部件，降低机床的加工精度，造成经济损失甚至人员伤亡。针对铣削过程中刀具颤振和刀具碰撞问题，本文通过以下四个步骤来改进加工动力学完整性的监测、建模和控制。首先，本研究构建了大型零件铣削过程的数字孪生监控系统框架，利用 Unity 3D 构建了数字孪生虚拟系统，设计并开发了虚拟机床的相关功能。其次，本研究建立了高进给铣刀动态切削厚度模型和刚性零件铣削动力学模型，并根据模态参数和铣削力系数建立了稳定叶图（SLD）。进而，在稳定叶瓣图的指导下，研究获得了数字孪生监测系统的颤振自适应阈值。第三，本研究还利用 OPC UA 协议和 LabVIEW 获取主轴位置、速度、加速度等信号并进行处理。基于数字孪生前端技术，实现用户交互、机床防碰撞、切削参数计算等功能；再基于数字孪生后端技术，获得颤振监测、抑制和预测的理论指导。最后，提出基于 MySQL 的驱动更新数据库，并利用该数据库更新数字孪生监控系统的后端模型。根据数字孪生监测系统在实际加工工艺条件下的实验测试，结果表明该系统在加工安全和加工质量方面有一定的提高，具有实用价值和指导意义。

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引用次数: 0

A digital twin framework for anomaly detection in industrial robot system based on multiple physics-informed hybrid convolutional autoencoder 基于多物理信息混合卷积自动编码器的工业机器人系统异常检测数字孪生框架

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-10-30 DOI: 10.1016/j.jmsy.2024.10.016

Shijie Wang , Jianfeng Tao , Qincheng Jiang , Wei Chen , Chengjin Qin , Chengliang Liu

The robot number in industry is growing up rapidly. Building anomaly detection system for them can improve the security of these expensive devices. The article implements an anomaly detection framework based on digital twin, which are built by a hybrid convolutional autoencoder. The framework shares those neural network weight files as digital assets, users can use them to estimate the possible output from real input. It approximates the dynamic relationship between motion, current, temperature and vibration with hybrid convolution. Considering the limited generalization performance of direct data-driven methods in practical physical systems, this article introduces physical information methods to improve the constraint function of neural network. The influence of multiple physical fields on current is established by a unified neural network. Terminals detect anomaly with KL divergence between really current and estimated current. The article collects operational data from real robots and verifies it, and the experiment shows that the RMSE for current estimation is below 1.5 %, the F1-score in anomaly detection is over 98.23 %, false positive is below 1 %, false negative is below 1.7 %. The relevant technologies are gradually being promoted and applied in enterprises.

工业领域的机器人数量正在迅速增长。为它们建立异常检测系统可以提高这些昂贵设备的安全性。文章实现了一个基于数字孪生的异常检测框架，该框架由混合卷积自动编码器构建。该框架将这些神经网络权重文件作为数字资产共享，用户可以利用它们来估计真实输入可能产生的输出。它通过混合卷积逼近运动、电流、温度和振动之间的动态关系。考虑到直接数据驱动方法在实际物理系统中的泛化性能有限，本文引入了物理信息方法来改善神经网络的约束功能。通过统一的神经网络建立了多个物理场对电流的影响。终端通过实际电流与估计电流之间的 KL 偏差检测异常。文章收集了真实机器人的运行数据并进行了验证，实验结果表明，电流估计的 RMSE 低于 1.5%，异常检测的 F1 分数超过 98.23%，假阳性低于 1%，假阴性低于 1.7%。相关技术正在企业中逐步推广和应用。

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