Journal of Intelligent Manufacturing最新文献

{"title":"Remaining useful life prediction based on parallel multi-scale feature fusion network","authors":"Yuyan Yin, Jie Tian, Xinfeng Liu","doi":"10.1007/s10845-024-02399-y","DOIUrl":"https://doi.org/10.1007/s10845-024-02399-y","url":null,"abstract":"<p>In the domain of Predictive Health Management (PHM), the prediction of Remaining Useful Life (RUL) is pivotal for averting machinery malfunctions and curtailing maintenance expenditures. Currently, most RUL prediction methods overlook the correlation between local and global information, which may lead to the loss of important features and, consequently, a subsequent decline in predictive precision. To address these limitations, this study presents a groundbreaking deep learning framework termed the Parallel Multi-Scale Feature Fusion Network (PM2FN). This approach leverages the advantages of different network structures by constructing two distinct feature extractors to capture both global and local information, thereby providing a more comprehensive feature set for RUL prediction. Experimental results on two publicly available datasets and a real-world dataset demonstrate the superiority and effectiveness of our method, offering a promising solution for industrial RUL prediction.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"3 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928483","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}

{"title":"Continual learning for surface defect segmentation by subnetwork creation and selection","authors":"Aleksandr Dekhovich, Miguel A. Bessa","doi":"10.1007/s10845-024-02393-4","DOIUrl":"https://doi.org/10.1007/s10845-024-02393-4","url":null,"abstract":"<p>We introduce a new continual (or lifelong) learning algorithm called LDA-CP &amp;S that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to two different surface defect segmentation problems that are learned incrementally, i.e., providing data about one type of defect at a time, while still being capable of predicting every defect that was seen previously. Our method creates a defect-related subnetwork for each defect type via iterative pruning and trains a classifier based on linear discriminant analysis (LDA). At the inference stage, we first predict the defect type with LDA and then predict the surface defects using the selected subnetwork. We compare our method with other continual learning methods showing a significant improvement – mean Intersection over Union better by a factor of two when compared to existing methods on both datasets. Importantly, our approach shows comparable results with joint training when all the training data (all defects) are seen simultaneously.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"20 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886297","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}

{"title":"Robust image-based cross-sectional grain boundary detection and characterization using machine learning","authors":"Nicholas Satterlee, Runjian Jiang, Eugene Olevsky, Elisa Torresani, Xiaowei Zuo, John S. Kang","doi":"10.1007/s10845-024-02383-6","DOIUrl":"https://doi.org/10.1007/s10845-024-02383-6","url":null,"abstract":"<p>Understanding the anisotropic sintering behavior of 3D-printed materials requires massive analytic studies on their grain boundary (GB) structures. Accurate characterization of the GBs is critical to study the metallurgical process. However, it is challenging and time-consuming for sintered 3D-printed materials due to immature etching and residual pores. In this study, we developed a machine learning-based method of characterizing GBs of sintered 3D-printed materials. The developed method is also generalizable and robust enough to characterize GBs from other non-3D-printed materials. This method can be applied to a small dataset because it includes a diffusion network that generate augmented images for training. The study compared various machine learning methods commonly used for segmentation, which include UNet, ResNeXt, and Ensemble of UNets. The comparison results showed that the Ensemble of UNets outperformed the other methods for the GB detection and characterization. The model is tested on unclear GBs from sintered 3D-printed samples processed with non-optimized etching and classifies the GBs with around 90% accuracy. The model is also tested on images with clear GBs from literature and classifies GBs with 92% accuracy.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"149 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886559","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}

{"title":"Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery","authors":"Ke Wu, Wei Xu, Qiming Shu, Wenjun Zhang, Xiaolong Cui, Jun Wu","doi":"10.1007/s10845-024-02395-2","DOIUrl":"https://doi.org/10.1007/s10845-024-02395-2","url":null,"abstract":"<p>Transfer learning methods have received abundant attention and extensively utilized in cross-domain fault diagnosis, which suppose that the label sets in the source and target domains are coincident. However, the open set domain adaptation problem which include new fault modes in the target domain is not well solved. To address the problem, an unknown-class recognition adversarial network (UCRAN) is proposed for the cross-domain fault diagnosis. Specifically, a three-dimensional discriminator is designed to conduct domain-invariant learning on the source domain, target known domain and target unknown domain. Then, an entropy minimization is introduced to determine the decision boundaries. Finally, a posteriori inference method is developed to calculate the open set recognition weight, which are used to adaptively weigh the importance between known class and unknown class. The effectiveness and practicability of the proposed UCRAN is validated by a series of experiments. The experimental results show that compared to other existing methods, the proposed UCRAN realizes better diagnosis performance in different domain transfer task.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"18 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886380","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}

{"title":"Quantile regression-enriched event modeling framework for dropout analysis in high-temperature superconductor manufacturing","authors":"Mai Li, Ying Lin, Qianmei Feng, Wenjiang Fu, Shenglin Peng, Siwei Chen, Mahesh Paidpilli, Chirag Goel, Eduard Galstyan, Venkat Selvamanickam","doi":"10.1007/s10845-024-02358-7","DOIUrl":"https://doi.org/10.1007/s10845-024-02358-7","url":null,"abstract":"<p>High-temperature superconductor (HTS) tapes have shown promising characteristics of high critical current, which are prerequisites for applications in high-field magnets. Due to the unstable growth conditions in the HTS manufacturing process, however, the frequent occurrences of dropouts in the critical current impede the consistent performance of HTS tapes. To manufacture HTS tapes with large scale, high yield, and uniform performance, it is essential to develop novel data analysis approaches for modeling the dropouts and identifying the related important process parameters. Conventional methods for modeling recurrent events, such as the point process, require the extraction of events from quality measurements. As the critical current is a continuous process, it may not comprehensively represent the drop patterns by transforming the time-series measurements into a set of events. To solve this issue, we develop a novel quantile regression-enriched event modeling (QREM) framework that integrates the non-homogeneous Poisson process for modeling the occurrence of dropouts and the quantile regression for capturing the drop patterns. By incorporating the feature selection and regularization, the proposed framework identifies a set of significant process parameters that can potentially cause the dropouts of HTS tapes. The proposed method is tested on real HTS tapes produced using an advanced manufacturing process, successfully identifying important parameters that influence dropout events including the substrate temperature and voltage. The results demonstrate that the proposed QREM method outperforms the standard point process in predicting the occurrence of dropouts.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886303","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}

{"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}

{"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}

{"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}

{"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}

{"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}