Computers in Industry最新文献_第3页

Dual-mode guided reinforcement learning for decentralized lifelong path planning of multiple automated guided vehicles in robotic mobile fulfillment systems 机器人移动履约系统中多自动导引车分散终身路径规划的双模引导强化学习

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-11-17 DOI: 10.1016/j.compind.2025.104416

Jianbin Xin , Peiyan Guo , Hongbo Li , Andrea D’Ariano , Yanhong Liu

The robotic mobile fulfillment system (RMFS) has revolutionized the manufacturing and logistics industries by enhancing the efficiency of automated storage and order fulfillment through automated guided vehicles (AGVs). However, existing multi-AGV path planning methods in RMFS typically decouple path planning from conflict resolution, thereby simplifying the problem but limiting system performance, especially in dynamic and complex operational environments. To address this challenge, we introduce a novel learning-based hierarchical framework for lifelong multi-AGV path planning. Our framework integrates a dual-mode heuristic global guidance planner with a local reinforcement learning planner, leveraging asynchronous proximal policy optimization and a recurrent neural network to achieve fully decentralized, online navigation. Critically, our dual-mode guidance mechanism adapts to multi-phase transport tasks by enabling unloaded AGVs to travel beneath stationary pods—a key distinction from conventional methods. This approach mitigates congestion in narrow corridors and boosts overall system throughput. Experimental results demonstrate that our method outperforms state-of-the-art centralized and decentralized approaches in large-scale deployments, achieving higher success rates and throughput while significantly reducing computational costs. This research thus offers a scalable and efficient solution to the complex path-planning challenges inherent in RMFS.

机器人移动履行系统（RMFS）通过自动引导车辆（agv）提高自动化存储和订单履行的效率，彻底改变了制造业和物流业。然而，RMFS中现有的多agv路径规划方法通常将路径规划与冲突解决解耦，从而简化了问题，但限制了系统性能，特别是在动态和复杂的操作环境中。为了解决这一挑战，我们引入了一种新的基于学习的分层框架，用于终身多agv路径规划。我们的框架集成了双模式启发式全局导航规划器和局部强化学习规划器，利用异步近端策略优化和循环神经网络来实现完全分散的在线导航。关键的是，我们的双模制导机制通过使卸载的agv在固定吊舱下行驶来适应多相运输任务，这是与传统方法的一个关键区别。这种方法减轻了狭窄走廊的拥堵，提高了整个系统的吞吐量。实验结果表明，我们的方法在大规模部署中优于最先进的集中式和分散式方法，实现了更高的成功率和吞吐量，同时显着降低了计算成本。因此，这项研究为RMFS固有的复杂路径规划挑战提供了一个可扩展和有效的解决方案。

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引用次数: 0

Enhanced three-dimensional displacement monitoring: Integrating the KAZE detector with digital image correlation for scenario-free anti-disturbance analysis 增强三维位移监测：将KAZE探测器与数字图像相关相结合，进行无场景抗干扰分析

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-11-12 DOI: 10.1016/j.compind.2025.104412

Shanshan Yu , Jian Zhang , Xiaoyuan He

In this study, a robust binocular stereo vision method based on modified three-dimensional digital image correlation is proposed to address challenging measurement conditions, including camera motion and poor correlation quality. To mitigate camera motion-induced errors, which can distort the external imaging geometry of binocular systems and generate false displacement measurements, a flexible self-calibration approach is introduced. This method employs an inverse-depth parameterized coordinate framework to overcome the planar constraints inherent in conventional techniques. For resolving poor correlation issues caused by sparse texture, surface reflections, and perspective differences, a feature-guided subset construction strategy is developed. This approach emphasizes key gray-scale features while integrating an M-estimator-based correlation criterion to effectively exclude subsets with abnormal gray variations. Experimental validation through a wing model loading test demonstrates the proposed method's superior capability in capturing comprehensive deformation fields, showcasing significant improvements over existing approaches.

本研究提出了一种基于改进三维数字图像相关的鲁棒双目立体视觉方法，以解决相机运动和相关质量差等测量条件的挑战。为了消除摄像机运动引起的误差，使双目系统的外部成像几何变形和产生错误的位移测量，提出了一种灵活的自校准方法。该方法采用反深度参数化坐标框架，克服了传统方法固有的平面约束。为了解决稀疏纹理、表面反射和视角差异引起的相关性差问题，提出了一种特征引导子集构建策略。该方法强调了关键的灰度特征，同时集成了基于m估计的相关准则，有效地排除了具有异常灰度变化的子集。通过机翼模型加载试验的实验验证表明，该方法在捕获综合变形场方面具有优越的能力，与现有方法相比有了显着改进。

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引用次数: 0

A real-time lightweight laser welding defect inspection algorithm based on deep learning 一种基于深度学习的实时轻量化激光焊接缺陷检测算法

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-11-12 DOI: 10.1016/j.compind.2025.104413

Li Zhang, Yunjie He, Tao Diao, Ziming Liu

In recent years, the growing demand for laser welding products has led to higher quality requirements for welded products, making the precise and timely inspection of welding defects a critical issue. This paper focuses on detecting laser welding defects in the safety vent of electric vehicle batteries. We collected welding defect images from automated laser spot welding machines in manufacturing workshops and built a dataset for analysis. We propose a real-time lightweight defect detection algorithm, named Welding Defect Classification Network, based on a deep compression convolutional neural network. The proposed method employs an improved MobileNetV2 model combined with the Efficient Channel Attention module and Teacher-Free Knowledge Distillation technology. In experiments conducted on the welding defect dataset, our model achieved a prediction accuracy of 96.50%, outperforming some well-known lightweight networks while maintaining low model complexity. Furthermore, we deployed the model on a Raspberry Pi 4B with the support of Intel’s Neural Computing Stick, achieving an inference speed of 29.89 ms per defect prediction, meeting the real-time requirements of actual industries.

近年来，对激光焊接产品的需求不断增长，对焊接产品的质量要求也越来越高，焊接缺陷的精确、及时检测成为一个关键问题。本文主要研究了电动汽车电池安全孔激光焊接缺陷的检测。我们收集了制造车间自动激光点焊机的焊接缺陷图像，并建立了数据集进行分析。提出了一种基于深度压缩卷积神经网络的实时轻量级缺陷检测算法——焊接缺陷分类网络。该方法采用了一种改进的MobileNetV2模型，结合了高效通道关注模块和无教师知识蒸馏技术。在焊接缺陷数据集上进行的实验中，该模型的预测准确率达到96.50%，在保持较低模型复杂度的同时，优于一些知名的轻量级网络。在Intel的Neural Computing Stick的支持下，我们将模型部署在Raspberry Pi 4B上，每个缺陷预测的推理速度达到29.89 ms，满足了实际行业的实时性要求。

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引用次数: 0

Integrating static and dynamic hierarchical clustering and its application to retail segmentation 结合静态和动态层次聚类及其在零售细分中的应用

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-11-12 DOI: 10.1016/j.compind.2025.104410

Aitor Izuzquiza , Miguel A. Patricio , Juan J. Cuadrado-Gallego , Antonio Berlanga , José M. Molina

This paper focuses on an approach to address large-scale data gathered from heterogeneous sources by integrating static and dynamic data in hierarchical clusterization, and its application to the analysis of retail branches. Traditionally, branch clustering analysis has relied on static information and the utilization of statistical measures to extract relevant features from the dynamic data and incorporate them into the static dataset; however, the application of this approach presents several challenges. This research proposes a solution that addresses these disadvantages while aiming to maintain the success achieved when applying unsupervised machine learning algorithms. The paper presents an approach based on the integration of static attributes and time series data in a hierarchical clustering manner that enables the identification of key performance indicators and offers insight into factors that influence branch performance over time. The results show the potential to optimize resource allocation, inventory management, and customer service strategies. The proposed approach is demonstrated using retail shop data from a Spanish telecommunications company (Grupo Masmovil), highlighting its effectiveness in enhancing cluster profiling and offering meaningful insights beyond the prevailing approaches. This method presents significant enrichment for clustering analysis that can be applied to different domains.

本文重点研究了一种基于分层聚类的静态和动态数据集成方法来处理来自异构数据源的大规模数据，并将其应用于零售分支机构分析。传统的分支聚类分析依赖于静态信息，利用统计度量从动态数据中提取相关特征，并将其整合到静态数据集中；然而，这种方法的应用带来了一些挑战。本研究提出了一种解决方案，解决了这些缺点，同时旨在保持应用无监督机器学习算法时取得的成功。本文提出了一种基于以分层聚类方式集成静态属性和时间序列数据的方法，该方法能够识别关键绩效指标，并深入了解随时间推移影响分支机构绩效的因素。结果显示了优化资源分配、库存管理和客户服务策略的潜力。本文使用西班牙电信公司（Grupo Masmovil）的零售商店数据来演示所提出的方法，突出了其在增强集群分析方面的有效性，并提供了超越主流方法的有意义的见解。该方法为聚类分析提供了显著的丰富性，可应用于不同的领域。

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引用次数: 0

Robust non-contact material recognition for robots in extreme and dynamic environments 机器人在极端和动态环境下的鲁棒非接触材料识别

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-11-10 DOI: 10.1016/j.compind.2025.104411

Bo Zhu , Tao Geng , Baoyi Wang , Haoxuan Li , Xianhong Zhang

Accurate recognition of material properties (such as hardness, texture, and strength) is essential for enabling robots to successfully interact with diverse objects. This task becomes particularly challenging in complex, dynamic, and extreme environments, where robots must continuously adapt to fluctuating conditions to perceive their surroundings accurately and perform tasks efficiently. Ultrasonic signals are well-suited for such settings due to their robustness against environmental interference and reliable data transmission under harsh conditions. However, ultrasonic echoes collected in dynamic scenarios often exhibit complex multi-scale and multi-semantic characteristics, which present significant challenges for conventional signal processing methods. To address these issues, we propose a novel material recognition method based on ultrasonic echoes using a Global Frequency Filter-based Pyramidal Dynamic Convolutional Network (GFF-PDCN). The proposed model incorporates three specialized modules: a pyramidal dynamic convolution module, a global frequency filter module, and a non-local attention module, which work collaboratively to capture and process intricate features in ultrasonic signals. Extensive experiments are conducted using a robotic system, including real-world validation in extreme environments for identifying wall material properties. The results demonstrate that our GFF-PDCN model achieves an average recognition accuracy of 95%. Our approach significantly enhances a robot’s capability to acquire, interpret, and process critical environmental information under complex operational conditions. The implementation code is available at: https://github.com/drama-bo/GFF-PDCN.

准确识别材料属性（如硬度、纹理和强度）对于使机器人成功地与各种物体进行交互至关重要。在复杂、动态和极端的环境中，这项任务变得特别具有挑战性，因为机器人必须不断适应波动的条件，以准确地感知周围环境并有效地执行任务。超声波信号非常适合这样的设置，因为它们对环境干扰的鲁棒性和在恶劣条件下可靠的数据传输。然而，动态场景下采集的超声回波往往具有复杂的多尺度和多语义特征，这对传统的信号处理方法提出了重大挑战。为了解决这些问题，我们提出了一种基于全局频率滤波器的锥体动态卷积网络（GFF-PDCN）的超声回波材料识别方法。该模型包含三个专用模块：金字塔动态卷积模块、全局频率滤波模块和非局部注意模块，它们协同工作以捕获和处理超声信号中的复杂特征。使用机器人系统进行了大量的实验，包括在极端环境中识别墙体材料特性的真实验证。结果表明，GFF-PDCN模型的平均识别准确率达到95%。我们的方法显著提高了机器人在复杂操作条件下获取、解释和处理关键环境信息的能力。实现代码可从https://github.com/drama-bo/GFF-PDCN获得。

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引用次数: 0

Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems 迈向可信赖的人工智能决策：知识和数据驱动的人工智能系统的生命周期视角

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-31 DOI: 10.1016/j.compind.2025.104409

Emiel Miedema, Sabine Waschull, Christos Emmanouilidis

Organisations increasingly use data-driven artificial intelligence (AI) systems in their decision-making processes. These AI systems may operate autonomously, support human decision-makers or increasingly act as collaborative team members. However, data-driven AI systems often function as black boxes, lacking interpretability. This poses a challenge in decision-making, as stakeholders involved in or impacted by the decision-making process frequently need to understand the rationale behind decisions. Moreover, data-driven AI systems operate without leveraging structured domain knowledge. As a result, data-driven AI systems may generate outputs that are misaligned with the decision context, objectives, or constraints, potentially leading to poor decisions or reduced trust in AI systems among users. Consequently, recent years have seen an increasing interest in integrating domain knowledge with data-driven AI. This is evident in neuro-symbolic AI, a subfield of AI that combines neural networks with symbolic AI. While this approach shows promise for enhancing the trustworthiness of AI systems in decision-making, the specific mechanisms by which domain knowledge integration contributes to dimensions of trustworthiness remain insufficiently explored. Therefore, this study reviews and integrates recent knowledge- and data-driven AI literature, along with relevant concepts for decision-making. Building on this foundation, it proposes a lifecycle framework for integrated knowledge- and data-driven AI systems for decision-making, and demonstrates its application through a healthcare application example. It further analyses the dimensions of trustworthiness for knowledge- and data-driven AI systems using the proposed lifecycle framework and application example. In doing so, this study advances the discourse on trustworthy AI for decision-making.

组织越来越多地在决策过程中使用数据驱动的人工智能（AI）系统。这些人工智能系统可以自主运行，支持人类决策者或越来越多地作为协作团队成员。然而，数据驱动的人工智能系统往往像黑盒子一样运作，缺乏可解释性。这对决策提出了挑战，因为参与决策过程或受决策过程影响的利益相关者经常需要了解决策背后的基本原理。此外，数据驱动的人工智能系统在不利用结构化领域知识的情况下运行。因此，数据驱动的人工智能系统可能会产生与决策上下文、目标或约束不一致的输出，从而可能导致糟糕的决策或降低用户对人工智能系统的信任。因此，近年来人们对将领域知识与数据驱动的人工智能相结合的兴趣越来越大。这在神经符号人工智能中很明显，这是人工智能的一个子领域，将神经网络与符号人工智能结合在一起。虽然这种方法有望提高人工智能系统在决策中的可信度，但领域知识集成有助于可信度维度的具体机制仍未得到充分探索。因此，本研究回顾并整合了最新的知识和数据驱动的人工智能文献，以及决策的相关概念。在此基础上，提出了用于决策的集成知识和数据驱动的人工智能系统的生命周期框架，并通过医疗保健应用示例演示了其应用。它使用所提出的生命周期框架和应用示例进一步分析了知识和数据驱动的人工智能系统的可信度维度。在此过程中，本研究推进了关于可信赖的人工智能决策的论述。

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引用次数: 0

Personalized safety training for construction workers: A large language model-driven multi-agent framework integrated with knowledge graph reasoning 建筑工人个性化安全培训：集成知识图推理的大型语言模型驱动多智能体框架

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-18 DOI: 10.1016/j.compind.2025.104399

Qihua Chen , Xianfei Yin , Beifei Yuan , Qirong Chen

Construction sites are inherently high-risk environments, making safety training for workers crucial to enhancing operational skills, reinforcing safety awareness, and reducing accident risks. Traditional centralized training often fails to engage workers due to monotonous nature and lack of relevance, leading to low efficiency. Moreover, critical resources such as operating instructions, safety guidelines, and accident reports are frequently mismanaged or underutilized. Therefore, this study proposes ConSTRAG, an innovative personalized construction safety training framework. By integrating large language model-empowered agents with knowledge graph reasoning, ConSTRAG generates tailored training materials, significantly improving the relevance and effectiveness of safety training. Validation tests conducted on a dataset of 11,020 questions achieved an average score of 81.25, exceeding the benchmark by 6.94. The generated personalized training materials were evaluated through an expert questionnaire survey, with an average score of 4.16 out of 5 across five dimensions. This research contributes to overcoming individual heterogeneity in construction safety training, enhances training efficiency and effectiveness, and holds potential for extension to other personnel training industries.

建筑工地本身就是高风险环境，因此对工人进行安全培训对于提高操作技能、增强安全意识、降低事故风险至关重要。传统的集中式培训由于性质单调，缺乏相关性，往往不能调动员工的积极性，导致培训效率低下。此外，诸如操作说明、安全指南和事故报告等关键资源经常管理不善或未得到充分利用。因此，本研究提出了一种创新的个性化建筑安全培训框架——contrag。通过将大型语言模型授权的智能体与知识图推理相结合，ConSTRAG生成定制的培训材料，显著提高了安全培训的相关性和有效性。在11020个问题的数据集上进行验证测试，平均得分为81.25，超过基准6.94分。生成的个性化培训材料通过专家问卷调查进行评估，五个维度的平均得分为4.16分（满分为5分）。本研究有助于克服建筑安全培训的个体异质性，提高培训效率和效果，并具有推广到其他人才培训行业的潜力。

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引用次数: 0

Automated configuration for cost-effective digital solutions 自动化配置，实现经济高效的数字解决方案

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-10 DOI: 10.1016/j.compind.2025.104397

Zhengyang Ling , Duncan McFarlane , Sam Brooks , Lavindra de Silva , Gregory Hawkridge , Alan Thorne

Low-cost digital solutions have been proposed as a means of helping Small and Medium-sized Enterprises (SMEs) in manufacturing. To reduce development costs and enable SMEs to create digital solutions for their specific requirements, workers should be able to configure their own solutions. However, such an approach can be problematic – and at times infeasible – as the SME may not have access to staff with the necessary software skills. Hence, this paper proposes an automated configuration approach for the preparation, customisation, and automatic generation of low-cost digital solutions. This approach was implemented in the development of an Automated Solution Configurator (ASC) platform. The ASC specifically makes use of a particular reference architecture (the so-called Shoestring approach) as a foundation for the design of low-cost digital solutions. These solutions are composed of modules of key functions (referred to as a “Service Module”), which themselves integrate “Building Blocks” (BBs) of low cost technology elements. The paper presents an overview of the ASC platform; its usefulness and usability are evaluated via (a) three industrial application studies, (b) a user study with seven participants and (c) a direct comparison between ASC-based and expert-prepared solutions. The evaluations demonstrate that users with a range of expertise can rapidly create low-cost solutions using the ASC platform. Comparing the ASC-generated code side by side with that written by an expert, the ASC code tends to be longer than a solution developed by an expert but still operates effectively. It is also demonstrated that the ASC approach can support simple solution reuse by reconfiguring technology BBs for different digital solutions.

低成本的数字化解决方案已经被提出作为帮助中小企业（SMEs）在制造业中的一种手段。为了降低开发成本并使中小企业能够为其特定需求创建数字解决方案，员工应该能够配置自己的解决方案。然而，这种方法可能会有问题——有时是不可行的——因为中小企业可能无法获得具有必要软件技能的员工。因此，本文提出了一种用于准备，定制和自动生成低成本数字解决方案的自动化配置方法。该方法在自动化解决方案配置器（ASC）平台的开发中实现。ASC特别使用了一个特定的参考架构（所谓的Shoestring方法）作为设计低成本数字解决方案的基础。这些解决方案由关键功能模块（称为“服务模块”）组成，这些模块本身集成了低成本技术元素的“构建块”（BBs）。本文介绍了ASC平台的概况；其有用性和可用性通过(a)三个工业应用研究，(b)七个参与者的用户研究和(c)基于asc和专家准备的解决方案之间的直接比较来评估。评估表明，具有一系列专业知识的用户可以使用ASC平台快速创建低成本的解决方案。将ASC生成的代码与专家编写的代码进行比较，ASC代码往往比专家开发的解决方案更长，但仍然有效地运行。还证明了ASC方法可以通过为不同的数字解决方案重新配置技术论坛来支持简单的解决方案重用。

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引用次数: 0

A cross-domain few-shot remaining useful life estimation framework based on model-agnostic meta-learning with task embeddings 基于模型不可知元学习和任务嵌入的跨领域少镜头剩余使用寿命估计框架

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-09 DOI: 10.1016/j.compind.2025.104396

Xinyu Shang , Jie Shang , Mingyu Li , Haobo Qiu , Liang Gao , Danyang Xu

Remaining useful life (RUL) estimation aims to predict the time until system failure based on monitoring data, facilitating proactive maintenance actions. Precise RUL estimation can significantly enhance system reliability and safety. However, when new system failures emerge, predictive models trained on historical failures data often encounter difficulties in accurate estimation. The distribution shift between historical and new failures data, coupled with extremely few new failures data, results in cross-domain few-shot prognostic scenarios, posing a significant challenge to many deep-learning-based RUL estimation methods. In response to the challenge, this paper proposes a novel cross-domain few-shot RUL estimation framework based on model-agnostic meta-learning (MAML) with task embeddings. First, a segmentation strategy is adopted to construct more meta-tasks, which can capture more comprehensive degradation information for efficient meta knowledge extraction. Then, task embeddings that are independent of backbone network are designed to encode task-specific degradation knowledge into efficient low-dimensional vectors, which alleviates overfitting caused by limited labeled data, thus improving RUL estimation performance. Moreover, the encoded degradation knowledge is only injected into feature extractor, making representation change dominant for better cross-domain adaptability. Experimental results on turbofan engine and wind turbine gearbox datasets reveal the effectiveness and superiority of the proposed framework. Estimation results evaluated by RMSE and Score improve 9 % and 31 %, respectively.

剩余使用寿命（RUL）评估的目的是根据监控数据预测系统故障发生前的时间，便于采取主动维护措施。精确的RUL估计可以显著提高系统的可靠性和安全性。然而，当新的系统故障出现时，基于历史故障数据训练的预测模型往往难以准确估计。历史故障数据和新故障数据之间的分布变化，加上新故障数据极少，导致了跨域的少量预测场景，这对许多基于深度学习的RUL估计方法提出了重大挑战。针对这一挑战，本文提出了一种基于任务嵌入的模型不可知元学习（MAML）的跨域少镜头规则学习估计框架。首先，采用分段策略构建更多的元任务，捕获更全面的退化信息，实现高效的元知识提取；然后，设计独立于骨干网的任务嵌入，将特定于任务的退化知识编码为高效的低维向量，缓解了标记数据有限导致的过拟合问题，从而提高了RUL估计的性能。此外，编码的退化知识只被注入到特征提取器中，使表征变化占主导地位，具有更好的跨域适应性。在涡扇发动机和风力发电机齿轮箱数据集上的实验结果表明了该框架的有效性和优越性。RMSE和Score评估的估计结果分别提高了9 %和31 %。

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引用次数: 0

A confident learning-based coordinate attention-guided fusion vision transformer for mixed-type wafer map defect detection 一种基于自信学习的坐标注意引导融合视觉转换器，用于混合型晶圆图缺陷检测

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-09 DOI: 10.1016/j.compind.2025.104391

Xiangyan Zhang , Xuexiu Liang , Jian Li , Shimin Wei

Wafer defect detection is crucial for quality assurance in semiconductor manufacturing. Current methods often overlook two key challenges: the adverse effects of mislabeled data on model reliability, and the significant correlation between defects and their spatial locations. To address these issues, we propose a novel confident learning-based coordinate attention-guided vision Transformer framework. Our approach includes: (1) automatic mislabel data identification and dataset cleaning using confident learning, and (2) a mixed-type wafer defect detection network that fuses convolutional operations, coordinate attention, and self-attention mechanisms. The architecture enables effective local–global feature extraction with positional awareness, and a decoupled classifier further improves detection performance. Evaluated on the clean MixedWM38 dataset (with 192 mislabeled noisy samples removed via confident learning), our framework achieves 99.60% accuracy while maintaining computational efficiency, outperforming advanced wafer defect detection methods. These results demonstrate its strong potential for industrial applications.

晶圆缺陷检测是保证半导体制造质量的关键。目前的方法往往忽略了两个关键挑战：错误标记数据对模型可靠性的不利影响，以及缺陷与其空间位置之间的显著相关性。为了解决这些问题，我们提出了一种新的基于自信学习的坐标注意引导视觉转换框架。我们的方法包括：(1)使用自信学习的自动误标数据识别和数据集清理；(2)融合卷积操作、协调注意和自注意机制的混合型晶圆缺陷检测网络。该结构能够有效地提取具有位置感知的局部-全局特征，并且解耦分类器进一步提高了检测性能。在干净的MixedWM38数据集（通过自信学习去除192个错误标记的噪声样本）上进行评估，我们的框架在保持计算效率的同时达到99.60%的准确率，优于先进的晶圆缺陷检测方法。这些结果证明了它在工业应用方面的强大潜力。

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引用次数: 0