首页 > 最新文献

Journal of Intelligent Manufacturing最新文献

英文 中文
Leveraging computer vision towards high-efficiency autonomous industrial facilities 利用计算机视觉实现高效自主工业设施
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-02 DOI: 10.1007/s10845-024-02396-1
Ibrahim Yousif, Liam Burns, Fadi El Kalach, Ramy Harik

Manufacturers face two opposing challenges: the escalating demand for customized products and the pressure to reduce delivery lead times. To address these expectations, manufacturers must refine their processes, to achieve highly efficient and autonomous operations. Current manufacturing equipment deployed in several facilities, while reliable and produces quality products, often lacks the ability to utilize advancements from newer technologies. Since replacing legacy equipment may be financially infeasible for many manufacturers, implementing digital transformation practices and technologies can overcome the stated deficiencies and offer cost-affordable initiatives to improve operations, increase productivity, and reduce costs. This paper explores the implementation of computer vision, as a cutting-edge, cost-effective, open-source digital transformation technology in manufacturing facilities. As a rapidly advancing technology, computer vision has the potential to transform manufacturing operations in general, and quality control in particular. The study integrates a digital twin application at the endpoint of an assembly line, effectively performing the role of a quality officer by utilizing state-of-the-art computer vision algorithms to validate end-product assembly orientation. The proposed digital twin, featuring a novel object recognition approach, efficiently classifies objects, identifies and segments errors in assembly, and schedules the paths through the data pipeline to the corresponding robot for autonomous correction. This minimizes the need for human interaction and reduces disruptions to manufacturing operations.

制造商面临着两大挑战:不断升级的定制产品需求和缩短交付周期的压力。为了满足这些期望,制造商必须完善流程,实现高效和自主运营。目前部署在多个工厂的制造设备虽然可靠,能生产优质产品,但往往缺乏利用最新技术的能力。对许多制造商来说,更换传统设备在经济上可能是不可行的,因此,实施数字化转型实践和技术可以克服上述不足,并提供成本可承受的措施来改善运营、提高生产率和降低成本。本文探讨了计算机视觉作为一种前沿的、具有成本效益的开源数字化转型技术在制造设备中的应用。作为一项快速发展的技术,计算机视觉有可能改变制造业的整体运营,尤其是质量控制。本研究将数字孪生应用集成到装配线的终端,利用最先进的计算机视觉算法验证终端产品的装配方向,从而有效地履行质量负责人的职责。所提出的数字孪生系统采用新颖的物体识别方法,能有效地对物体进行分类,识别和分割装配中的错误,并通过数据管道将路径安排给相应的机器人进行自主校正。这最大限度地减少了人机交互的需要,降低了对生产操作的干扰。
{"title":"Leveraging computer vision towards high-efficiency autonomous industrial facilities","authors":"Ibrahim Yousif, Liam Burns, Fadi El Kalach, Ramy Harik","doi":"10.1007/s10845-024-02396-1","DOIUrl":"https://doi.org/10.1007/s10845-024-02396-1","url":null,"abstract":"<p>Manufacturers face two opposing challenges: the escalating demand for customized products and the pressure to reduce delivery lead times. To address these expectations, manufacturers must refine their processes, to achieve highly efficient and autonomous operations. Current manufacturing equipment deployed in several facilities, while reliable and produces quality products, often lacks the ability to utilize advancements from newer technologies. Since replacing legacy equipment may be financially infeasible for many manufacturers, implementing digital transformation practices and technologies can overcome the stated deficiencies and offer cost-affordable initiatives to improve operations, increase productivity, and reduce costs. This paper explores the implementation of computer vision, as a cutting-edge, cost-effective, open-source digital transformation technology in manufacturing facilities. As a rapidly advancing technology, computer vision has the potential to transform manufacturing operations in general, and quality control in particular. The study integrates a digital twin application at the endpoint of an assembly line, effectively performing the role of a quality officer by utilizing state-of-the-art computer vision algorithms to validate end-product assembly orientation. The proposed digital twin, featuring a novel object recognition approach, efficiently classifies objects, identifies and segments errors in assembly, and schedules the paths through the data pipeline to the corresponding robot for autonomous correction. This minimizes the need for human interaction and reduces disruptions to manufacturing operations.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"28 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploring and optimizing deep neural networks for precision defect detection system in injection molding process 探索和优化注塑成型工艺中精密缺陷检测系统的深度神经网络
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-02 DOI: 10.1007/s10845-024-02394-3
Mohamed EL Ghadoui, Ahmed Mouchtachi, Radouane Majdoul

This research employs transfer learning to explore and compare pre-trained deep learning models for defect detection in injection molding processes. It introduces advanced neural network architectures, specifically Inception and ResNet50, which have not been extensively studied in this context. Through systematic evaluation using techniques such as data augmentation, architecture modification, and hyperparameter tuning, the study aims to enhance detection precision. The methodology addresses deployment challenges inherent in defect detection systems and emphasizes the importance of model selection for achieving desired goals. Comparative assessments with contemporary models highlight the effectiveness of the proposed approach in real-world production settings. Improved results obtained with the Inception model demonstrate a precision of 92.3%, recall of 100%, and F1 score of 96%, surpassing ResNet50 as well as previous studies utilizing VGG16 and Yolo v5. This underscores the reliability of the Inception model for defects detection in practical scenarios. Furthermore, beyond accuracy enhancement, the study aligns with the broader goal of advancing sustainable manufacturing by integrating smarter defect detection mechanisms. The findings not only offer a robust framework for selecting optimal detection models but also lay the groundwork for future research endeavors aimed at improving adaptability and efficiency in defect detection systems across various industrial applications. This contributes to the evolution of intelligent manufacturing processes, balancing quality and profitability objectives.

本研究利用迁移学习来探索和比较用于注塑成型工艺缺陷检测的预训练深度学习模型。它引入了先进的神经网络架构,特别是 Inception 和 ResNet50,这些架构在这方面尚未得到广泛研究。通过使用数据增强、架构修改和超参数调整等技术进行系统评估,该研究旨在提高检测精度。该方法解决了缺陷检测系统固有的部署难题,并强调了模型选择对实现预期目标的重要性。与当代模型的对比评估突出了所提方法在实际生产环境中的有效性。使用 Inception 模型获得的改进结果显示,精确度为 92.3%,召回率为 100%,F1 分数为 96%,超过了 ResNet50 以及之前使用 VGG16 和 Yolo v5 进行的研究。 这凸显了 Inception 模型在实际场景中进行缺陷检测的可靠性。此外,除了提高准确性之外,这项研究还与通过整合更智能的缺陷检测机制推进可持续制造的更广泛目标相一致。研究结果不仅为选择最佳检测模型提供了一个稳健的框架,还为未来的研究工作奠定了基础,旨在提高缺陷检测系统在各种工业应用中的适应性和效率。这有助于智能制造流程的发展,平衡质量和盈利目标。
{"title":"Exploring and optimizing deep neural networks for precision defect detection system in injection molding process","authors":"Mohamed EL Ghadoui, Ahmed Mouchtachi, Radouane Majdoul","doi":"10.1007/s10845-024-02394-3","DOIUrl":"https://doi.org/10.1007/s10845-024-02394-3","url":null,"abstract":"<p>This research employs transfer learning to explore and compare pre-trained deep learning models for defect detection in injection molding processes. It introduces advanced neural network architectures, specifically Inception and ResNet50, which have not been extensively studied in this context. Through systematic evaluation using techniques such as data augmentation, architecture modification, and hyperparameter tuning, the study aims to enhance detection precision. The methodology addresses deployment challenges inherent in defect detection systems and emphasizes the importance of model selection for achieving desired goals. Comparative assessments with contemporary models highlight the effectiveness of the proposed approach in real-world production settings. Improved results obtained with the Inception model demonstrate a precision of 92.3%, recall of 100%, and F1 score of 96%, surpassing ResNet50 as well as previous studies utilizing VGG16 and Yolo v5. This underscores the reliability of the Inception model for defects detection in practical scenarios. Furthermore, beyond accuracy enhancement, the study aligns with the broader goal of advancing sustainable manufacturing by integrating smarter defect detection mechanisms. The findings not only offer a robust framework for selecting optimal detection models but also lay the groundwork for future research endeavors aimed at improving adaptability and efficiency in defect detection systems across various industrial applications. This contributes to the evolution of intelligent manufacturing processes, balancing quality and profitability objectives.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"43 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intelligent hierarchical compensation method for industrial robot positioning error based on compound branch neural network automatic creation 基于复合分支神经网络自动创建的工业机器人定位误差智能分层补偿方法
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-02 DOI: 10.1007/s10845-024-02381-8
Jian Zhou, Lianyu Zheng, Wei Fan, Yansheng Cao

Absolute positioning accuracy is a crucial index for evaluating industrial robot performance and the foundation for motion trajectory and machining accuracy. Current positioning error compensation methods focus on achieving unified compensation within a robot’s workspace. These methods rely heavily on expert knowledge and require a significant amount of manual intervention. To realize refined error compensation and improve the autonomy and intelligence degree of a robot, an intelligent hierarchical positioning error compensation method based on a master–slave controller is proposed in this paper. Specifically, positioning error compensation is addressed through two research questions related to positioning error level diagnosis and compensated pose prediction, and the approach consists of two major processes: automatic creation of a compound branch compensation network and hierarchical positioning error compensation. For the first process, the master controller independently grades the positioning error levels and directs the diagnosis slave controller to create a positioning error level diagnosis model in terms of the robot pose error data. Then, it directs the prediction slave controller to create several compensated pose prediction models based on the pose data of different error levels. Subsequently, the diagnosis and prediction models are integrated to form a compound branch compensation network. For the second process, the master controller first activates the diagnosis branch of the compound branch compensation network to determine the positioning error level of the current robot pose. Then, it activates the prediction branch corresponding to the determined error level to generate the compensated pose. Finally, it uses the diagnosed error level to filter the compensated pose. Experimental cases of a Stäubli robot and a UR robot are applied to verify the feasibility and effectiveness of the proposed method. The experimental results show that the proposed method reduces the positioning error of the Stäubli robot from 0.848 to 0.135 mm and the UR robot from 2.11 to 0.158 mm, outperforming relevant current methods.

绝对定位精度是评价工业机器人性能的重要指标,也是运动轨迹和加工精度的基础。目前的定位误差补偿方法主要是在机器人的工作空间内实现统一补偿。这些方法严重依赖专家知识,需要大量人工干预。为了实现精细化误差补偿,提高机器人的自主性和智能化程度,本文提出了一种基于主从控制器的智能分层定位误差补偿方法。具体来说,定位误差补偿是通过定位误差等级诊断和补偿姿态预测两个相关研究问题来解决的,该方法包括两个主要过程:自动创建复合分支补偿网络和分层定位误差补偿。在第一个过程中,主控制器独立对定位误差等级进行分级,并指导诊断从控制器根据机器人姿态误差数据创建定位误差等级诊断模型。然后,它指示预测从控制器根据不同误差等级的姿态数据创建多个补偿姿态预测模型。随后,将诊断和预测模型整合在一起,形成一个复合分支补偿网络。在第二个过程中,主控制器首先激活复合分支补偿网络的诊断分支,以确定当前机器人姿势的定位误差级别。然后,激活与确定的误差水平相对应的预测分支,生成补偿姿势。最后,利用诊断出的误差水平对补偿姿态进行过滤。为了验证所提方法的可行性和有效性,我们应用了史陶比尔机器人和 UR 机器人的实验案例。实验结果表明,所提出的方法可将史陶比尔机器人的定位误差从 0.848 毫米减少到 0.135 毫米,将 UR 机器人的定位误差从 2.11 毫米减少到 0.158 毫米,优于当前的相关方法。
{"title":"Intelligent hierarchical compensation method for industrial robot positioning error based on compound branch neural network automatic creation","authors":"Jian Zhou, Lianyu Zheng, Wei Fan, Yansheng Cao","doi":"10.1007/s10845-024-02381-8","DOIUrl":"https://doi.org/10.1007/s10845-024-02381-8","url":null,"abstract":"<p>Absolute positioning accuracy is a crucial index for evaluating industrial robot performance and the foundation for motion trajectory and machining accuracy. Current positioning error compensation methods focus on achieving unified compensation within a robot’s workspace. These methods rely heavily on expert knowledge and require a significant amount of manual intervention. To realize refined error compensation and improve the autonomy and intelligence degree of a robot, an intelligent hierarchical positioning error compensation method based on a master–slave controller is proposed in this paper. Specifically, positioning error compensation is addressed through two research questions related to positioning error level diagnosis and compensated pose prediction, and the approach consists of two major processes: automatic creation of a compound branch compensation network and hierarchical positioning error compensation. For the first process, the master controller independently grades the positioning error levels and directs the diagnosis slave controller to create a positioning error level diagnosis model in terms of the robot pose error data. Then, it directs the prediction slave controller to create several compensated pose prediction models based on the pose data of different error levels. Subsequently, the diagnosis and prediction models are integrated to form a compound branch compensation network. For the second process, the master controller first activates the diagnosis branch of the compound branch compensation network to determine the positioning error level of the current robot pose. Then, it activates the prediction branch corresponding to the determined error level to generate the compensated pose. Finally, it uses the diagnosed error level to filter the compensated pose. Experimental cases of a Stäubli robot and a UR robot are applied to verify the feasibility and effectiveness of the proposed method. The experimental results show that the proposed method reduces the positioning error of the Stäubli robot from 0.848 to 0.135 mm and the UR robot from 2.11 to 0.158 mm, outperforming relevant current methods.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"14 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing 用于智能制造中在线织物缺陷检测的动态推理网络 (DI-Net)
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-02 DOI: 10.1007/s10845-024-02387-2
Shuxuan Zhao, Ray Y. Zhong, Chuqiao Xu, Junliang Wang, Jie Zhang

Online fabric defect detection plays a critical role in the quality management of textile production. However, the high-impact and low-probability characteristics of defective samples lead to redundant design of network and hinder its real-time performance. To improve the time efficiency, this paper proposes a dynamic inference network (DI-Net) which can dynamically allocate computation resources as the complexity of image. Firstly, “AND” Gates are incorporated into the backbone to control activation of network’s function modules, allowing for dynamic adjustment of network depth. Additionally, the dynamic inference module which contains several exits with inference unit is proposed to collaborate with “AND” Gates. When sample’s confidence at specific exit satisfies the early-exit policy, the inference unit will allow it to early-exit from network and output a negative value to corresponding “AND” Gate. As a result, the output of “AND” Gate will also be negative and subsequent network will not be activated. Finally, the two-stage training strategy and exit-weighted loss function are proposed to avoid crosstalk and facilitate different exits to focus on adequate samples, enabling the efficient training of DI-Net. The experiments on the fabric dataset demonstrate that the proposed DI-Net can achieve detection precision and recall over 99% for normal samples, and approximately 95% for defective samples. Besides, its detection speed has been improved by 20%, reaching 30.1 frames per second and 20.96 m/min. This indicates that the proposed DI-Net can meet the requirements of online fabric defect detection.

在线织物疵点检测在纺织品生产质量管理中起着至关重要的作用。然而,疵点样品的高影响和低概率特性导致网络设计冗余,阻碍了其实时性。为了提高时间效率,本文提出了一种动态推理网络(DI-Net),它可以根据图像的复杂度动态分配计算资源。首先,在骨干网中加入 "AND "门控制网络功能模块的激活,从而实现网络深度的动态调整。此外,动态推理模块包含多个带有推理单元的出口,可与 "AND "门协同工作。当样本在特定出口的置信度满足提前退出策略时,推理单元将允许其提前退出网络,并向相应的 "AND "门输出负值。因此,"AND "门的输出也将为负值,后续网络将不会被激活。最后,我们提出了两阶段训练策略和退出加权损失函数,以避免串扰,并促进不同的退出集中于足够的样本,从而实现 DI-Net 的高效训练。在织物数据集上的实验表明,所提出的 DI-Net 对正常样本的检测精度和召回率均超过 99%,对缺陷样本的检测精度和召回率约为 95%。此外,其检测速度提高了 20%,达到每秒 30.1 帧和每分钟 20.96 米。这表明所提出的 DI-Net 能够满足在线织物疵点检测的要求。
{"title":"A dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing","authors":"Shuxuan Zhao, Ray Y. Zhong, Chuqiao Xu, Junliang Wang, Jie Zhang","doi":"10.1007/s10845-024-02387-2","DOIUrl":"https://doi.org/10.1007/s10845-024-02387-2","url":null,"abstract":"<p>Online fabric defect detection plays a critical role in the quality management of textile production. However, the high-impact and low-probability characteristics of defective samples lead to redundant design of network and hinder its real-time performance. To improve the time efficiency, this paper proposes a dynamic inference network (DI-Net) which can dynamically allocate computation resources as the complexity of image. Firstly, “AND” Gates are incorporated into the backbone to control activation of network’s function modules, allowing for dynamic adjustment of network depth. Additionally, the dynamic inference module which contains several exits with inference unit is proposed to collaborate with “AND” Gates. When sample’s confidence at specific exit satisfies the early-exit policy, the inference unit will allow it to early-exit from network and output a negative value to corresponding “AND” Gate. As a result, the output of “AND” Gate will also be negative and subsequent network will not be activated. Finally, the two-stage training strategy and exit-weighted loss function are proposed to avoid crosstalk and facilitate different exits to focus on adequate samples, enabling the efficient training of DI-Net. The experiments on the fabric dataset demonstrate that the proposed DI-Net can achieve detection precision and recall over 99% for normal samples, and approximately 95% for defective samples. Besides, its detection speed has been improved by 20%, reaching 30.1 frames per second and 20.96 m/min. This indicates that the proposed DI-Net can meet the requirements of online fabric defect detection.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"18 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Accurate and energy efficient ad-hoc neural network for wafer map classification 用于晶片图分类的精确且节能的特设神经网络
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-01 DOI: 10.1007/s10845-024-02390-7
Ana Pinzari, Thomas Baumela, Liliana Andrade, Maxime Martin, Marcello Coppola, Frédéric Pétrot

Yield is key to profitability in semiconductor manufacturing and controlling the fabrication process is therefore a key duty for engineers in silicon foundries. Analyzing the distribution of the defective dies on a wafer is a necessary step to identify process shifts, and a major step in this analysis takes the form of a classification of these distributions on wafer bitmaps called wafer maps. Current approaches use large to huge state-of-the-art neural networks to perform this classification. We claim that given the task at hand, the use of much smaller, purpose defined neural networks is possible without much accuracy loss, while requiring two orders of magnitude less power than the current solutions. Our work uses actual foundry data from STMicroelectronics 28 nm fabrication facilities that it aims at classifying in 58 categories. We performed experiments using different low power boards for which we report accuracy, power consumption and power efficiency. As a result, we show that to classify 224(times )224 wafer maps at foundry-throughput with an accuracy above 97% using a bit more than 1 W, is feasible.

良品率是半导体制造业盈利的关键,因此控制制造工艺是硅晶圆代工厂工程师的一项重要职责。分析晶圆上缺陷芯片的分布是识别工艺转变的必要步骤,而这一分析的主要步骤是对这些分布在称为晶圆图的晶圆位图上进行分类。目前的方法是使用大型乃至超大型的先进神经网络来进行分类。我们认为,考虑到手头的任务,使用更小的、目的明确的神经网络是可行的,而且不会造成太大的精度损失,同时所需的功率也比当前的解决方案低两个数量级。我们的工作使用了意法半导体 28 纳米制造设备的实际代工数据,旨在将其分为 58 个类别。我们使用不同的低功耗电路板进行了实验,并报告了准确性、功耗和能效。结果表明,在代工厂吞吐量下对 224(times )224 个晶圆图进行分类是可行的,准确率超过 97%,耗电量略高于 1 W。
{"title":"Accurate and energy efficient ad-hoc neural network for wafer map classification","authors":"Ana Pinzari, Thomas Baumela, Liliana Andrade, Maxime Martin, Marcello Coppola, Frédéric Pétrot","doi":"10.1007/s10845-024-02390-7","DOIUrl":"https://doi.org/10.1007/s10845-024-02390-7","url":null,"abstract":"<p>Yield is key to profitability in semiconductor manufacturing and controlling the fabrication process is therefore a key duty for engineers in silicon foundries. Analyzing the distribution of the defective dies on a wafer is a necessary step to identify process shifts, and a major step in this analysis takes the form of a classification of these distributions on wafer bitmaps called <i>wafer maps</i>. Current approaches use large to huge state-of-the-art neural networks to perform this classification. We claim that given the task at hand, the use of much smaller, purpose defined neural networks is possible without much accuracy loss, while requiring two orders of magnitude less power than the current solutions. Our work uses actual foundry data from STMicroelectronics 28 nm fabrication facilities that it aims at classifying in 58 categories. We performed experiments using different low power boards for which we report accuracy, power consumption and power efficiency. As a result, we show that to classify 224<span>(times )</span>224 wafer maps at foundry-throughput with an accuracy above 97% using a bit more than 1 W, is feasible.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"20 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A multi scale meta-learning network for cross domain fault diagnosis with limited samples 利用有限样本进行跨领域故障诊断的多尺度元学习网络
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-30 DOI: 10.1007/s10845-024-02365-8
Yu Wang, Shujie Liu

In recent years, data-driven machine learning models have achieved good results in fault diagnosis of rotating machinery under different working conditions. However, in practical applications, the lack of fault samples under various working conditions makes the training of models difficult. In this paper, a multi scale meta-learning network (MS-MLN) that can be applied to few-shot cross-domain diagnosis of rotating machinery is proposed to address this issue. MS-MLN consists of a multi scale feature encoder, a metric embedding process and a classifier. The model is trained by an episodic metric meta-learning strategy under few-shot and domain shift scenarios. Extensive experiments are carried out to verify the effectiveness of MS-MLN, results show that MS-MLN outperforms most benchmark models in bearing and wind turbine gearbox fault diagnosis. Visualization is applied to the model to study its effectiveness. Ablation study is also conducted to discuss the impact of different parts of the model’s feature encoder on its performance in detail.

近年来,数据驱动的机器学习模型在不同工况下的旋转机械故障诊断中取得了良好的效果。然而,在实际应用中,由于缺乏各种工况下的故障样本,导致模型训练困难重重。本文针对这一问题,提出了一种可应用于旋转机械少次跨域诊断的多尺度元学习网络(MS-MLN)。MS-MLN 由多尺度特征编码器、度量嵌入过程和分类器组成。该模型是在少数几个镜头和领域转移的情况下,通过偶发度量元学习策略进行训练的。为了验证 MS-MLN 的有效性,进行了大量实验,结果表明 MS-MLN 在轴承和风力涡轮机齿轮箱故障诊断方面优于大多数基准模型。该模型采用了可视化技术,以研究其有效性。还进行了消融研究,详细讨论了模型特征编码器不同部分对其性能的影响。
{"title":"A multi scale meta-learning network for cross domain fault diagnosis with limited samples","authors":"Yu Wang, Shujie Liu","doi":"10.1007/s10845-024-02365-8","DOIUrl":"https://doi.org/10.1007/s10845-024-02365-8","url":null,"abstract":"<p>In recent years, data-driven machine learning models have achieved good results in fault diagnosis of rotating machinery under different working conditions. However, in practical applications, the lack of fault samples under various working conditions makes the training of models difficult. In this paper, a multi scale meta-learning network (MS-MLN) that can be applied to few-shot cross-domain diagnosis of rotating machinery is proposed to address this issue. MS-MLN consists of a multi scale feature encoder, a metric embedding process and a classifier. The model is trained by an episodic metric meta-learning strategy under few-shot and domain shift scenarios. Extensive experiments are carried out to verify the effectiveness of MS-MLN, results show that MS-MLN outperforms most benchmark models in bearing and wind turbine gearbox fault diagnosis. Visualization is applied to the model to study its effectiveness. Ablation study is also conducted to discuss the impact of different parts of the model’s feature encoder on its performance in detail.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"88 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Unbalance prediction method of aero-engine saddle rotor based on deep belief networks and GA-BP intelligent learning 基于深度信念网络和 GA-BP 智能学习的航空发动机鞍形转子不平衡预测方法
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-29 DOI: 10.1007/s10845-024-02392-5
Huilin Wu, Chuanzhi Sun, Qing Lu, Yinchu Wang, Yongmeng Liu, Limin Zou, Jiubin Tan

Aiming at the problems of complex and time-consuming process of manual adjustment of eccentricity and tilt in the evaluation of machining error of aero-engine saddle rotor, and inaccurate measurement of unbalance after multi-stage rotor assembly, this paper proposes an unbalance prediction method based on Genetic Algorithm Back Propagation (GA-BP) neural network and deep belief networks (DBN). Firstly, according to the definition of single-stage rotor machining error, the influence source of saddle rotor machining error and the evaluation of machining error are analyzed. Secondly, GA-BP neural network is established to obtain the concentricity and flatness of saddle rotors at all stages as the error source of unbalance. Then, the output of the GA-BP neural network is used as the input of the DBN to establish the unbalance prediction network model. Finally, the experimental verification is carried out based on the experimental measurement data of an engine rotor unbalance. The results show that the mean value and root mean square error (RMSE) of the unbalance are 16.72 g·mm and 32.71 g·mm respectively, and R-squared (R2) determination coefficient is 0.96 when the 80 groups of samples are tested by the prediction method of DBN. Compared with the method based on the traditional error transfer model, the proposed method based on DBN and GA-BP reduces the average error and mean square error by 86.08% and 75.97% respectively, which greatly reduces the measurement error of rotor unbalance. Therefore, this method can provide technical guidance for the optimal assembly of multi-stage rotors, thereby improving the assembly quality of multi-stage rotors.

针对航空发动机鞍形转子加工误差评估中人工调整偏心和倾斜过程复杂、耗时长,以及多级转子装配后不平衡度测量不准确等问题,本文提出了一种基于遗传算法反向传播(GA-BP)神经网络和深度信念网络(DBN)的不平衡度预测方法。首先,根据单级转子加工误差的定义,分析了鞍形转子加工误差的影响源和加工误差的评估。其次,建立 GA-BP 神经网络,获取鞍形转子各阶段的同心度和平面度作为不平衡的误差源。然后,将 GA-BP 神经网络的输出作为 DBN 的输入,建立不平衡预测网络模型。最后,根据发动机转子不平衡的实验测量数据进行了实验验证。结果表明,当采用 DBN 预测方法对 80 组样本进行测试时,不平衡度的平均值和均方根误差(RMSE)分别为 16.72 g-mm 和 32.71 g-mm,R 平方(R2)判定系数为 0.96。与基于传统误差传递模型的方法相比,基于 DBN 和 GA-BP 的拟议方法的平均误差和均方误差分别降低了 86.08% 和 75.97%,大大降低了转子不平衡度的测量误差。因此,该方法可为多级转子的优化装配提供技术指导,从而提高多级转子的装配质量。
{"title":"Unbalance prediction method of aero-engine saddle rotor based on deep belief networks and GA-BP intelligent learning","authors":"Huilin Wu, Chuanzhi Sun, Qing Lu, Yinchu Wang, Yongmeng Liu, Limin Zou, Jiubin Tan","doi":"10.1007/s10845-024-02392-5","DOIUrl":"https://doi.org/10.1007/s10845-024-02392-5","url":null,"abstract":"<p>Aiming at the problems of complex and time-consuming process of manual adjustment of eccentricity and tilt in the evaluation of machining error of aero-engine saddle rotor, and inaccurate measurement of unbalance after multi-stage rotor assembly, this paper proposes an unbalance prediction method based on Genetic Algorithm Back Propagation (GA-BP) neural network and deep belief networks (DBN). Firstly, according to the definition of single-stage rotor machining error, the influence source of saddle rotor machining error and the evaluation of machining error are analyzed. Secondly, GA-BP neural network is established to obtain the concentricity and flatness of saddle rotors at all stages as the error source of unbalance. Then, the output of the GA-BP neural network is used as the input of the DBN to establish the unbalance prediction network model. Finally, the experimental verification is carried out based on the experimental measurement data of an engine rotor unbalance. The results show that the mean value and root mean square error (RMSE) of the unbalance are 16.72 g·mm and 32.71 g·mm respectively, and R-squared (R<sup>2</sup>) determination coefficient is 0.96 when the 80 groups of samples are tested by the prediction method of DBN. Compared with the method based on the traditional error transfer model, the proposed method based on DBN and GA-BP reduces the average error and mean square error by 86.08% and 75.97% respectively, which greatly reduces the measurement error of rotor unbalance. Therefore, this method can provide technical guidance for the optimal assembly of multi-stage rotors, thereby improving the assembly quality of multi-stage rotors.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"21 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cyber-physical systems: a bibliometric analysis of literature 网络物理系统:文献计量分析
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-29 DOI: 10.1007/s10845-024-02380-9
Nitin Singh, Prabin Kumar Panigrahi, Zuopeng Zhang, Sajjad M. Jasimuddin

Recently, there is a significant growth in the use of the Cyber-Physical System (CPS). New technologies such as Internet of Things (IoT), Industry 4.0, and Analytics have become enablers of CPS implementation. Study of the development and application of CPS in the supply chain context is valuable to operations management and information systems research and practice; especially, a focus on IoT-enabled CPS in production/manufacturing is highly relevant. Knowledge about the research trends of the development and use of CPS for supply chain management supported by new innovations in IT is very limited in the extant literature. The aim of this research is to investigate the research trends of applying CPS in manufacturing. The study encompasses a scientometric analysis of research on deploying the CPS in production systems. Based on a systematic selection process, we collect a total of 245 articles from the Web of Science (WoS) database as the sample for analysis. Using appropriate software, we conduct bibliometric analyses of the sample articles that include citation, cocitation analysis, centrality co-occurrence analysis, and co-authorship analysis. From the bibliometric analysis, we discover major themes of CPS in manufacturing and their evolutions in the extant literature.

最近,网络物理系统(CPS)的使用有了显著增长。物联网(IoT)、工业 4.0 和分析等新技术已成为实施 CPS 的推动力。在供应链背景下研究 CPS 的发展和应用对运营管理和信息系统的研究与实践具有重要价值;特别是,关注生产/制造中物联网支持的 CPS 具有高度相关性。在现有文献中,有关在新的信息技术创新支持下开发和使用供应链管理 CPS 的研究趋势的知识非常有限。本研究的目的是调查在制造业中应用 CPS 的研究趋势。本研究对生产系统中部署 CPS 的研究进行了科学计量分析。通过系统筛选,我们从科学网(WoS)数据库中收集了 245 篇文章作为分析样本。利用适当的软件,我们对样本文章进行了文献计量分析,包括引文分析、共引分析、中心共现分析和合著分析。通过文献计量分析,我们发现了制造业中 CPS 的主要主题及其在现有文献中的演变。
{"title":"Cyber-physical systems: a bibliometric analysis of literature","authors":"Nitin Singh, Prabin Kumar Panigrahi, Zuopeng Zhang, Sajjad M. Jasimuddin","doi":"10.1007/s10845-024-02380-9","DOIUrl":"https://doi.org/10.1007/s10845-024-02380-9","url":null,"abstract":"<p>Recently, there is a significant growth in the use of the Cyber-Physical System (CPS). New technologies such as Internet of Things (IoT), Industry 4.0, and Analytics have become enablers of CPS implementation. Study of the development and application of CPS in the supply chain context is valuable to operations management and information systems research and practice; especially, a focus on IoT-enabled CPS in production/manufacturing is highly relevant. Knowledge about the research trends of the development and use of CPS for supply chain management supported by new innovations in IT is very limited in the extant literature. The aim of this research is to investigate the research trends of applying CPS in manufacturing. The study encompasses a scientometric analysis of research on deploying the CPS in production systems. Based on a systematic selection process, we collect a total of 245 articles from the Web of Science (WoS) database as the sample for analysis. Using appropriate software, we conduct bibliometric analyses of the sample articles that include citation, cocitation analysis, centrality co-occurrence analysis, and co-authorship analysis. From the bibliometric analysis, we discover major themes of CPS in manufacturing and their evolutions in the extant literature.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"49 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction of thin-walled workpiece machining error: a transfer learning approach 薄壁工件加工误差预测:一种迁移学习方法
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-27 DOI: 10.1007/s10845-024-02382-7
Yu-Yue Yu, Da-Ming Shi, Han Ding, Xiao-Ming Zhang

The surface error induced by low-rigid deformation and intermittent cutting is common in the milling process of thin-walled workpieces. Machining errors have a direct impact on the surface accuracy of the machined workpiece, making it crucial to monitor the milling error throughout the thin-walled workpiece machining process. This article provides a strategy for forecasting machining errors in thin-walled workpieces. The prediction strategy faces two difficulties: the flexibility variations in the different machining positions of the thin-walled workpieces and the processing information shifting with the varied machining conditions. To tackle these challenges, the knowledge-embedded parameter construction of the strategy establishes a correlation between error and process information by integrating physical constraints and data information. Transfer learning combines a small amount of real-time data with a large amount of historical data, enabling effective practical data application and reutilization. The experimental evaluations and comparisons have demonstrated the predictive performance and applicability of the machining error prediction strategy.

在薄壁工件的铣削加工过程中,由于低刚性变形和间歇切削而引起的表面误差十分常见。加工误差直接影响加工工件的表面精度,因此在整个薄壁工件加工过程中监控铣削误差至关重要。本文提供了一种预测薄壁工件加工误差的策略。该预测策略面临两个难题:薄壁工件不同加工位置的柔性变化和加工信息随加工条件变化而变化。为解决这些难题,该策略的知识嵌入式参数构造通过整合物理约束和数据信息,建立了误差与加工信息之间的相关性。迁移学习将少量实时数据与大量历史数据相结合,实现了有效的实际数据应用和再利用。实验评估和比较证明了加工误差预测策略的预测性能和适用性。
{"title":"Prediction of thin-walled workpiece machining error: a transfer learning approach","authors":"Yu-Yue Yu, Da-Ming Shi, Han Ding, Xiao-Ming Zhang","doi":"10.1007/s10845-024-02382-7","DOIUrl":"https://doi.org/10.1007/s10845-024-02382-7","url":null,"abstract":"<p>The surface error induced by low-rigid deformation and intermittent cutting is common in the milling process of thin-walled workpieces. Machining errors have a direct impact on the surface accuracy of the machined workpiece, making it crucial to monitor the milling error throughout the thin-walled workpiece machining process. This article provides a strategy for forecasting machining errors in thin-walled workpieces. The prediction strategy faces two difficulties: the flexibility variations in the different machining positions of the thin-walled workpieces and the processing information shifting with the varied machining conditions. To tackle these challenges, the knowledge-embedded parameter construction of the strategy establishes a correlation between error and process information by integrating physical constraints and data information. Transfer learning combines a small amount of real-time data with a large amount of historical data, enabling effective practical data application and reutilization. The experimental evaluations and comparisons have demonstrated the predictive performance and applicability of the machining error prediction strategy.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"11 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Steel ball surface inspection using modified DRAEM and machine vision 利用改进型 DRAEM 和机器视觉进行钢球表面检测
IF 8.3 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-27 DOI: 10.1007/s10845-024-02370-x
Chun-Chin Hsu, Ya-Chen Hsu, Po-Chou Shih, Yong-Qi Yang, Fang-Chih Tien

Precision steel balls are among the most crucial components in the industry, widely used in various equipment related to bearings, such as CNC, automotive, medical, and machinery industries. Due to the reflective surface of steel balls, flaw inspection becomes a challenging task. This paper introduces an automatic optical inspection system that employs a modified DRAEM, a reconstruction-based anomaly detection network, for examining the surface of precision steel balls. We made three modifications to the DRAEM network (Zavrtanik, V., Kristan, M., & Skoca, D. (2021). DRAEM—a discriminatively trained reconstruction embedding for surface anomaly detection. http://arXiv.org/arXiv:2108.07610[cs.CV]), including adjusting the generation process of synthesized anomalies, adding a few skip connections from the encoder to the decoder, and incorporating an attention module to enhance the quality of reconstructed images and reduce misjudgments. Experimental results demonstrate a reduction in the model's underkill rate from 8.8% to 4.6% and the overkill rate from 1.5% to 0.4%. This indicates that the proposed methods addressed the issues of reconstruction distortion and the inability to detect small and inconspicuous defects. The proposed system has been successfully implemented in a case study company, showcasing significant advantages, particularly in scenarios involving new production lines or a lack of sufficient defective samples for collection.

精密钢球是工业中最关键的部件之一,广泛应用于与轴承有关的各种设备,如数控、汽车、医疗和机械行业。由于钢球表面反光,缺陷检测成为一项具有挑战性的任务。本文介绍了一种自动光学检测系统,该系统采用了基于重构的异常检测网络 DRAEM,用于检测精密钢球的表面。我们对 DRAEM 网络进行了三处修改(Zavrtanik, V., Kristan, M., & Skoca, D. (2021)。DRAEM-a discriminatively trained reconstruction embedding for surface anomaly detection. http://arXiv.org/arXiv:2108.07610[cs.CV]),包括调整合成异常的生成过程、增加编码器到解码器之间的一些跳转连接,以及加入注意力模块以提高重建图像的质量并减少误判。实验结果表明,模型的欠杀率从 8.8% 降至 4.6%,过杀率从 1.5% 降至 0.4%。这表明,所提出的方法解决了重建失真和无法检测微小、不明显缺陷的问题。建议的系统已在一家案例研究公司成功实施,展示了其显著优势,特别是在涉及新生产线或缺乏足够缺陷样本收集的情况下。
{"title":"Steel ball surface inspection using modified DRAEM and machine vision","authors":"Chun-Chin Hsu, Ya-Chen Hsu, Po-Chou Shih, Yong-Qi Yang, Fang-Chih Tien","doi":"10.1007/s10845-024-02370-x","DOIUrl":"https://doi.org/10.1007/s10845-024-02370-x","url":null,"abstract":"<p>Precision steel balls are among the most crucial components in the industry, widely used in various equipment related to bearings, such as CNC, automotive, medical, and machinery industries. Due to the reflective surface of steel balls, flaw inspection becomes a challenging task. This paper introduces an automatic optical inspection system that employs a modified DRAEM, a reconstruction-based anomaly detection network, for examining the surface of precision steel balls. We made three modifications to the DRAEM network (Zavrtanik, V., Kristan, M., &amp; Skoca, D. (2021). DRAEM—a discriminatively trained reconstruction embedding for surface anomaly detection. http://arXiv.org/arXiv:2108.07610[cs.CV]), including adjusting the generation process of synthesized anomalies, adding a few skip connections from the encoder to the decoder, and incorporating an attention module to enhance the quality of reconstructed images and reduce misjudgments. Experimental results demonstrate a reduction in the model's underkill rate from 8.8% to 4.6% and the overkill rate from 1.5% to 0.4%. This indicates that the proposed methods addressed the issues of reconstruction distortion and the inability to detect small and inconspicuous defects. The proposed system has been successfully implemented in a case study company, showcasing significant advantages, particularly in scenarios involving new production lines or a lack of sufficient defective samples for collection.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"176 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Journal of Intelligent Manufacturing
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1