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Digital twin and predictive quality solution for insulated glass line 中空玻璃生产线的数字孪生和预测性质量解决方案
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-27 DOI: 10.1007/s10845-024-02426-y
Gülcan Aydin, Mehmet Tezcan, Bayram Ozgen, Tuğçe Nur Özkan

This study is an integral part of an international research and development initiative investigating the application of digital twins and predictive quality solutions to enhance quality control and streamline production processes within the insulating glass manufacturing industry. The critical factor influencing the transformation of insulating glass into a high-quality, energy-efficient product is the gas filling rate. Therefore, this study focuses on the real-time monitoring and analysis of the gas filling process. Concurrently, predictive quality solutions are implemented to improve product quality and reduce defects. Consequently, it is evident that these technologies hold significant potential to advance the quality of insulating glass production and promote sustainable production practices on an international scale.

本研究是一项国际研发计划的重要组成部分,该计划旨在调查数字双胞胎和预测性质量解决方案在中空玻璃制造业中的应用,以加强质量控制和简化生产流程。影响中空玻璃转化为优质节能产品的关键因素是气体填充率。因此,本研究重点关注气体填充过程的实时监控和分析。同时,实施预测性质量解决方案,以提高产品质量,减少缺陷。因此,这些技术在提高中空玻璃生产质量和促进国际范围内的可持续生产实践方面显然具有巨大潜力。
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引用次数: 0
Enhancing robustness to novel visual defects through StyleGAN latent space navigation: a manufacturing use case 通过 StyleGAN 潜在空间导航增强对新型视觉缺陷的稳健性:制造业用例
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-27 DOI: 10.1007/s10845-024-02415-1
Spyros Theodoropoulos, Dimitrios Dardanis, Georgios Makridis, Patrik Zajec, Jože M. Rožanec, Dimosthenis Kyriazis, Panayiotis Tsanakas

Visual Quality Inspection is an integral part of the manufacturing process that is becoming increasingly automated with the advent of Industry 4.0. While very beneficial, AI-driven Computer Vision Algorithms and Deep Neural Networks face several issues that may impede their adoption in practical real-life settings such as a manufacturing shop floor. One such issue arising during an AI classifier’s continuous operation is the frequent lack of robustness to novel defects appearing for the first time. Such unanticipated inputs can pose a significant risk to cyber-physical applications as a resulting out-of-context decision could compromise the integrity of the production process. While recent Machine Learning methods can theoretically tackle this problem from different angles (e.g., open-set recognition, semi-supervised learning, intelligent data augmentation), applying them to a real-life setting with a small, imbalanced dataset and high inter-class similarity can be challenging. This paper confronts such a use case aiming at the automation of the visual quality inspection of shaver shell brand prints from the electronics industry and characterized by data scarcity and the existence of small local defects. To that end, we introduce a novel data augmentation approach based on the latent space manipulation of StyleGAN, where defect data is intentionally synthesized to simulate novel inputs that can help form a boundary of the model’s knowledge. Our approach shows promising results compared to well-established open-set recognition and semi-supervised methods applied to the same problem, while its consistent performance across classifier embeddings indicates lower coupling to the final classifier.

视觉质量检测是制造过程中不可或缺的一部分,随着工业 4.0 的到来,其自动化程度也在不断提高。虽然人工智能驱动的计算机视觉算法和深度神经网络非常有益,但它们也面临着一些问题,这些问题可能会阻碍它们在制造车间等实际现实环境中的应用。人工智能分类器在持续运行过程中出现的一个问题是,对于首次出现的新缺陷经常缺乏鲁棒性。这种意料之外的输入会给网络物理应用带来巨大风险,因为由此产生的断章取义的决策可能会损害生产流程的完整性。虽然最近的机器学习方法可以从不同角度(如开放集识别、半监督学习、智能数据增强)从理论上解决这一问题,但将它们应用到数据集小、不平衡且类间相似度高的现实环境中可能具有挑战性。本文面对的就是这样一个使用案例,其目的是对电子行业的剃须刀外壳品牌印花进行自动化视觉质量检测,其特点是数据稀缺和存在小的局部缺陷。为此,我们引入了一种基于 StyleGAN 潜在空间操作的新型数据增强方法,在这种方法中,缺陷数据被有意合成,以模拟有助于形成模型知识边界的新型输入。与应用于同一问题的成熟的开放集识别和半监督方法相比,我们的方法显示出了良好的效果,而它在不同分类器嵌入中的一致表现表明,它与最终分类器的耦合度较低。
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引用次数: 0
A data-driven time-sequence feature-based composite network of time-distributed CNN-LSTM for detecting pore defects in laser penetration welding 基于数据驱动的时间序列特征的时间分布 CNN-LSTM 复合网络,用于检测激光熔透焊接中的孔隙缺陷
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-25 DOI: 10.1007/s10845-024-02391-6
Shenghong Yan, Bo Chen, Caiwang Tan, Xiaoguo Song, Guodong Wang

The pore in laser penetration welding significantly deteriorates the mechanical property, and is an important criterion for evaluating the product quality. The intelligent diagnosis of welding can guide the optimization of process parameters to inhibit the pore formation. Considering that the signals in laser welding have time-sequence features and abundant implicitness information may cause high computational effort and information misidentify, an intelligent pore defect diagnosis method based on time–frequency feature extraction and a combined neural network of Convolutional Neural Networks (CNN) and Long short-term memory (LSTM) was proposed. Firstly, the visual signal results of vapor plume demonstrated that the pore formation was accompanied by irregular and continuous variation in vapor plume morphology during the subsequent period. Secondly, this denoising, decomposition, and restructuring of signals were performed by wavelet packet transform, and it was found that the sustaining fluctuation of frequency could localize the pore formation in the corresponding position of weld metal. Therefore, the signal was finely segmented to construct a cube time–frequency spectrogram data with the time-sequence characteristics. Finally, a combined classification model of CNN and LSTM was constructed for recognizing the temporal-spatial information of cube spectrogram data, realizing the online monitoring of pore defect. The results indicated that the proposed method was a promising solution for monitoring pore defect in laser penetration welding and improving product quality.

激光熔透焊接中的气孔会严重恶化机械性能,是评价产品质量的重要标准。焊接智能诊断可以指导工艺参数的优化,从而抑制气孔的形成。考虑到激光焊接信号具有时序特征,丰富的隐含信息可能导致计算量大和信息误判,提出了一种基于时频特征提取和卷积神经网络(CNN)与长短期记忆(LSTM)相结合的孔隙缺陷智能诊断方法。首先,蒸汽羽流的视觉信号结果表明,孔隙的形成伴随着随后一段时间蒸汽羽流形态的不规则连续变化。其次,通过小波包变换对信号进行去噪、分解和重组,发现频率的持续波动可以将孔隙的形成定位在焊缝金属的相应位置。因此,对信号进行精细分割,构建出具有时序特征的立方体时频谱图数据。最后,构建了 CNN 和 LSTM 的组合分类模型,用于识别立方体频谱数据的时空信息,实现了孔隙缺陷的在线监测。结果表明,所提出的方法在激光熔透焊接的孔隙缺陷监测和提高产品质量方面是一种很有前途的解决方案。
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引用次数: 0
Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process 在机械臂打磨过程中使用机器学习模型监测砂带状态
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-18 DOI: 10.1007/s10845-024-02410-6
Mochamad Denny Surindra, Gusti Ahmad Fanshuri Alfarisy, Wahyu Caesarendra, Mohamad Iskandar Petra, Totok Prasetyo, Tegoeh Tjahjowidodo, Grzegorz M. Królczyk, Adam Glowacz, Munish Kumar Gupta

Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments.

尽管影响砂带磨削性能和劣化的因素已众所周知,但对机械臂磨削过程中砂带的磨损进行预测仍是一项挑战。砂带表面粗粒的大量磨损会严重影响工具的完整性,并降低成品的表面质量。使用特殊工具的传统磨损状态监测策略会导致生产流程停止,这有时需要很长时间,而且高度依赖人力。砂带不稳定的磨损行为要求制造业的加工过程配备智能决策方法。在这项研究中,为了保持工具的均匀运动,在机械臂的末端执行器上安装了砂带磨削装置,以磨削低碳钢工件的表面。同时,加速度计和力传感器被集成到系统中,以实时记录其振动和力。来自工件和工具的振动信号反应反映了砂带的磨损程度,从而监测工具的状况。研究了使用 K-Nearest Neighbor (KNN)、Support Vector Machine (SVM)、Multi-Layer Perceptron (MLP) 和 Decision Tree (DT) 等几种机器学习算法对砂带磨削状况进行智能监控。使用 DT、随机森林(RF)和 XGBoost 找到了具有优化超参数的机器学习模型,这些模型能产生最高的平均测试准确率。同时,DT 和 RF 获得了最低的延迟。基于决策树的分类器可能是解决砂带磨削预测问题的一个有前途的模型。在未来的研究活动中,各种算法的应用将是我们研究团队的重点,我们将研究如何在实际工业环境中应用所选方法。
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引用次数: 0
Three-dimensional fabric smoothness evaluation using point cloud data for enhanced quality control 利用点云数据进行三维织物平滑度评估,加强质量控制
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-18 DOI: 10.1007/s10845-024-02367-6
Zhijie Yuan, Binjie Xin, Jing Zhang, Yingqi Xu

Assessing the smoothness appearance of fabrics, especially in three-dimensional forms, is vital for quality control. Existing methods often lack objectivity or fail to consider the full 3D structure of the fabric. In this study, we introduce an innovative system that harnesses point cloud data to overcome these limitations. We use a 3D scanning system to capture a multi-directional point cloud representation of the textile surface. The data undergoes stitching and filtering to obtain an optimized point cloud model for feature extraction. We propose the 3D and 2D alpha-shape area ratio as a novel feature parameter for determining surface smoothness. Validation was conducted with 730 point clouds from 146 fabric samples, achieving an impressive 95.81%, recognition accuracy, which aligns with expert subjective evaluations. This research not only presents a dependable method for 3D textile smoothness grading but also indicates its applicability in other industries where surface evaluation is pivotal.

评估织物(尤其是三维织物)的平滑外观对于质量控制至关重要。现有的方法往往缺乏客观性,或者没有考虑到织物的完整三维结构。在本研究中,我们介绍了一种利用点云数据克服这些局限性的创新系统。我们使用三维扫描系统捕捉纺织品表面的多方位点云表示。数据经过拼接和过滤后,就能获得用于特征提取的优化点云模型。我们提出将三维和二维阿尔法形状面积比作为确定表面平滑度的新特征参数。我们对来自 146 个织物样本的 730 个点云进行了验证,识别准确率达到了令人印象深刻的 95.81%,这与专家的主观评价相吻合。这项研究不仅提出了一种可靠的三维纺织品平滑度分级方法,还表明它适用于对表面评估至关重要的其他行业。
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引用次数: 0
Multi-modal background-aware for defect semantic segmentation with limited data 利用有限数据进行多模态背景感知缺陷语义分割
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-18 DOI: 10.1007/s10845-024-02373-8
Dexing Shan, Yunzhou Zhang, Shitong Liu

Visual defect detection is widely used in intelligent manufacturing to achieve intelligent detection of product quality. Two main challenges remain in industrial applications. One is the scarcity of defect samples and the other is the weak texture variation of industrial defects. The above problems lead to the application of RGB image-based industrial defect segmentation. To this end, we propose a multi-modal background-aware network (MMBA-Net) for few-shot defect (2D+3D) segmentation with limited data, which can segment texture and structural defects in unseen and seen domains (objects). To synthesize the perception capabilities of different imaging conditions, MMBA-Net exploits the point cloud to provide spatial information for the RGB images. Furthermore, we found that background regions are perceptually consistent within an industrial image, which can be leveraged to discriminate between foreground and background regions. To implement this idea, we model correlation learning between multi-modal query samples and multi-modal normal (defect-free) samples as an optimal transport problem, establishing robust multi-modal background correlations between query and normal samples across different modalities. Experiments were conducted on real-world industrial products and food datasets, demonstrating that the proposed method can perform effective base learning and meta-learning on a small number of defective samples (approximately 15–25 defective training samples) to achieve effective segmentation of defects in the seen and unseen domains.

视觉缺陷检测被广泛应用于智能制造领域,以实现产品质量的智能检测。在工业应用中仍然存在两大挑战。其一是缺陷样本稀缺,其二是工业缺陷的纹理变化较弱。上述问题导致了基于 RGB 图像的工业缺陷分割的应用。为此,我们提出了一种多模态背景感知网络(MMBA-Net),用于在数据有限的情况下进行少镜头缺陷(2D+3D)分割,它可以分割未见域和可见域(物体)中的纹理和结构缺陷。为了综合不同成像条件下的感知能力,MMBA-Net 利用点云为 RGB 图像提供空间信息。此外,我们还发现,在工业图像中,背景区域在感知上是一致的,这可以用来区分前景和背景区域。为了实现这一想法,我们将多模态查询样本和多模态正常(无缺陷)样本之间的相关性学习建模为一个最优传输问题,在不同模态的查询样本和正常样本之间建立稳健的多模态背景相关性。在真实世界的工业产品和食品数据集上进行的实验表明,所提出的方法可以在少量缺陷样本(约 15-25 个缺陷训练样本)上进行有效的基础学习和元学习,从而实现对可见和未知领域中缺陷的有效分割。
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引用次数: 0
Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning 从 MBD 模型中提取加工特征,定义特征级数字孪生工艺模型,用于智能工艺规划
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-14 DOI: 10.1007/s10845-024-02406-2
Jingjing Li, Guanghui Zhou, Chao Zhang, Junsheng Hu, Fengtian Chang, Andrea Matta

The booming development of emerging technologies and their integration in process planning provide new opportunities for solving the problems in traditional trial-and-error process planning. Combining digital twin with 3D computer vision, this paper defines a novel feature-level digital twin process model (FL-DTPM) by extracting machining features from model-based definition models. Firstly, a multi-dimensional FL-DTPM framework is defined by fusing on-site data, quality information, and process knowledge, where the synergistic mechanism of its virtual and physical processes is revealed. Then, 3D computer vision-enabled machining features extraction method is embedded into the FL-DTPM framework to support the reuse of process knowledge, which involves the procedures of data pre-processing, semantic segmentation, and instance segmentation. Finally, the effectiveness of the proposed features extraction method is verified and the application of FL-DTPM in machining process is presented. Oriented to the impeller process planning, a prototype of FL-DTPM is constructed to explore the potential application scenarios of the proposed method in intelligent process planning, which could provide insights into the industrial implementation of FL-DTPM for aerospace manufacturing enterprises.

新兴技术的蓬勃发展及其与工艺规划的整合为解决传统试错工艺规划中的问题提供了新的机遇。本文将数字孪生与三维计算机视觉相结合,通过从基于模型的定义模型中提取加工特征,定义了一种新颖的特征级数字孪生工艺模型(FL-DTPM)。首先,通过融合现场数据、质量信息和工艺知识,定义了多维 FL-DTPM 框架,揭示了其虚拟和物理工艺的协同机制。然后,将三维计算机视觉加工特征提取方法嵌入到 FL-DTPM 框架中,以支持工艺知识的重用,其中涉及数据预处理、语义分割和实例分割等程序。最后,验证了所提出的特征提取方法的有效性,并介绍了 FL-DTPM 在机械加工过程中的应用。针对叶轮工艺规划,构建了一个 FL-DTPM 原型,以探索所提方法在智能工艺规划中的潜在应用场景,从而为 FL-DTPM 在航空航天制造企业的工业实施提供启示。
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引用次数: 0
A novel method based on deep learning algorithms for material deformation rate detection 基于深度学习算法的材料变形率检测新方法
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-14 DOI: 10.1007/s10845-024-02409-z
Selim Özdem, İlhami Muharrem Orak

Given the significant influence of microstructural characteristics on a material’s mechanical, physical, and chemical properties, this study posits that the deformation rate of structural steel S235-JR can be precisely determined by analyzing changes in its microstructure. Utilizing advanced artificial intelligence techniques, microstructure images of S235-JR were systematically analyzed to establish a correlation with the material’s lifespan. The steel was categorized into five classes and subjected to varying deformation rates through laboratory tensile tests. Post-deformation, the specimens underwent metallographic procedures to obtain microstructure images via an light optical microscope (LOM). A dataset comprising 10000 images was introduced and validated using K-Fold cross-validation. This research utilized deep learning (DL) architectures ResNet50, ResNet101, ResNet152, VGG16, and VGG19 through transfer learning to train and classify images containing deformation information. The effectiveness of these models was meticulously compared using a suite of metrics including Accuracy, F1-score, Recall, and Precision to determine their classification success. The classification accuracy was compared across the test data, with ResNet50 achieving the highest accuracy of 98.45%. This study contributes a five-class dataset of labeled images to the literature, offering a new resource for future research in material science and engineering.

鉴于微观结构特征对材料的机械、物理和化学性质有重大影响,本研究认为可以通过分析 S235-JR 结构钢的微观结构变化来精确确定其变形率。利用先进的人工智能技术,对 S235-JR 的微观结构图像进行了系统分析,以建立与材料寿命的相关性。钢材被分为五类,并通过实验室拉伸试验承受不同的变形率。变形后,试样经过金相程序,通过光学显微镜(LOM)获得微观结构图像。引入了一个包含 10000 张图像的数据集,并使用 K-Fold 交叉验证进行了验证。本研究利用深度学习(DL)架构 ResNet50、ResNet101、ResNet152、VGG16 和 VGG19,通过迁移学习对包含变形信息的图像进行训练和分类。我们使用一系列指标(包括准确率、F1-分数、召回率和精确率)对这些模型的有效性进行了细致的比较,以确定它们的分类成功率。在对所有测试数据的分类准确率进行比较后,ResNet50 的准确率最高,达到 98.45%。这项研究为文献提供了五类标注图像数据集,为材料科学与工程领域的未来研究提供了新的资源。
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引用次数: 0
MSOA: A modular service-oriented architecture to integrate mobile manipulators as cyber-physical systems MSOA:将移动机械手整合为网络物理系统的面向服务的模块化架构
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-13 DOI: 10.1007/s10845-024-02404-4
Nooshin Ghodsian, Khaled Benfriha, Adel Olabi, Varun Gopinath, Esma Talhi, Lucas Hof, Aurélien Arnou

In the evolving landscape of the fourth industrial revolution, the integration of cyber-physical systems (CPSs) into industrial manufacturing, particularly focusing on autonomous mobile manipulators (MMs), is examined. A comprehensive framework is proposed for embedding MMs into existing production systems, addressing the burgeoning need for flexibility and adaptability in contemporary manufacturing. At the heart of this framework is the development of a modular service-oriented architecture, characterized by adaptive decentralization. This approach prioritizes real-time interoperability and leverages virtual capabilities, which is crucial for the effective integration of MMs as CPSs. The framework is designed to not only accommodate the operational complexities of MMs but also ensure their seamless alignment with existing production control systems. The practical application of this framework is demonstrated at the Platform 4.0 research production line at Arts et Métiers. An MM named MoMa, developed by OMRON Company, was integrated into the system. This application highlighted the framework’s capacity to significantly enhance the production system's flexibility, autonomy, and efficiency. Managed by the manufacturing execution system (MES), the successful integration of MoMa exemplifies the framework's potential to transform manufacturing processes in alignment with the principles of Industry 4.0.

在第四次工业革命不断发展的背景下,本研究探讨了将网络物理系统(CPS)融入工业制造的问题,尤其侧重于自主移动机械手(MMs)。本文提出了一个将移动机械手嵌入现有生产系统的综合框架,以满足当代制造业对灵活性和适应性的迫切需求。该框架的核心是开发模块化的面向服务架构,其特点是自适应分散。这种方法优先考虑实时互操作性和利用虚拟能力,这对于将 MMs 有效集成为 CPS 至关重要。该框架的设计不仅考虑到 MM 的操作复杂性,还确保它们与现有的生产控制系统无缝对接。艺术与技术学院的平台 4.0 研究生产线演示了该框架的实际应用。欧姆龙公司开发的名为 MoMa 的 MM 被集成到了系统中。这一应用凸显了该框架显著提高生产系统灵活性、自主性和效率的能力。在制造执行系统(MES)的管理下,MoMa 的成功集成体现了该框架根据工业 4.0 原则改造制造流程的潜力。
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引用次数: 0
Selecting subsets of source data for transfer learning with applications in metal additive manufacturing 为转移学习选择源数据子集,并将其应用于金属增材制造
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-12 DOI: 10.1007/s10845-024-02402-6
Yifan Tang, Mostafa Rahmani Dehaghani, Pouyan Sajadi, G. Gary Wang

Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and limited target datasets. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. This method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that (1) the source data selection method is general and supports integration with various TL methods and distance metrics, (2) compared with using all source data, the proposed method can find a subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and (3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains.

考虑到金属增材制造(AM)中的数据不足,人们采用了迁移学习(TL)技术,从源域(如已完成的印刷)中提取知识,以提高目标域(如新的印刷)的建模性能。目前的应用是在 TL 中直接使用所有可访问的源数据,而不考虑源数据和目标数据之间的相似性。本文提出了一种系统方法,可根据源数据集和有限目标数据集之间的相似性找到适当的源数据子集。这种相似性由空间距离和模型距离度量表征。本文开发了一种基于帕累托前沿的源数据选择方法,通过迭代选择位于由两个相似性距离指标定义的帕累托前沿上的源数据。这种方法被集成到基于实例的 TL 方法(决策树回归模型)和基于模型的 TL 方法(微调人工神经网络)中。然后,在金属 AM 的若干回归任务中对这两种模型进行了测试。比较结果表明:(1) 源数据选择方法具有通用性,支持与各种 TL 方法和距离度量的集成;(2) 与使用所有源数据相比,在涉及不同流程和机器的金属 AM 回归任务中,所提出的方法可以从同一域中找到具有更好 TL 性能的源数据子集;(3) 当存在多个源域时,源数据选择方法可以从一个源域中找到子集,从而获得与使用所有源域数据构建的模型相当或更好的 TL 性能。
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引用次数: 0
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Journal of Intelligent Manufacturing
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