Journal of Intelligent Manufacturing最新文献

{"title":"An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documents","authors":"Youzi Xiao, Shuai Zheng, Jiewu Leng, Ruibo Gao, Zihao Fu, Jun Hong","doi":"10.1007/s10845-024-02423-1","DOIUrl":"https://doi.org/10.1007/s10845-024-02423-1","url":null,"abstract":"<p>Assembly is an essential stage in industrial electronic equipment manufacturing and needs to meet the complexity of manufacturing. Therefore, the assembly process planning for industrial electronic equipment still relies on the experiences of planners. The advent of knowledge graphs brings an opportunity to achieve automated assembly process planning. Thus, extracting process knowledge from historical assembly process documents and constructing assembly process knowledge graphs are indispensable. However, the complexity of industrial electronic equipment manufacturing leads to assembly process documents containing more complex assembly relations, longer texts, and high-density assembly entities. These characteristics pose challenges to assembly process knowledge extraction and knowledge graph modeling. The confidentiality of assembly process documents further hinders the development of this field. To address these challenges, we propose a pipeline for achieving assembly process planning from historical assembly process documents. First, we construct an assembly process dataset using historical assembly process documents from an industrial electronic equipment enterprise. Then, we propose a global relation-driven bidirectional extraction model, which automatically constructs the assembly process knowledge graph. In addition, we also propose a knowledge graph-based matching and searching method to support process planning. The proposed model is evaluated on the constructed dataset and a publicly accessible equipment fault diagnostic dataset, achieving F1-scores of 92.9% and 87.9%, respectively. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on both datasets. Furthermore, we construct an assembly process knowledge graph for industrial electronic equipment and perform assembly process planning, which validates the feasibility of our pipeline.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"25 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141255777","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":"Development of a cascaded multitask physics-informed neural network (CM-PINN) to construct the muti-physical field model of rubber bushing press fitting","authors":"Yiru Chen, Jianfu Zhang, Pingfa Feng, Zhongpeng Zheng, Xiangyu Zhang, Jianjian Wang","doi":"10.1007/s10845-024-02427-x","DOIUrl":"https://doi.org/10.1007/s10845-024-02427-x","url":null,"abstract":"<p>The real-time and accurate prediction of the stress–strain and deformations field of material is a vital function for the intelligent press fitting system of the rubber bushing. The physics-informed neural network (PINN) provide an efficient approach to constructing physical fields with high robustness and interpretability in real time. However, currently, PINN usually solves problems under known boundary conditions, which are not given explicitly in most realistic engineering problems. This study proposes a cascaded multitask PINN (CM-PINN) that divides the problem solving of rubber bushing interference fit into two phases: boundary computation and forward solving of the physical field. In CM-PINN, one sub-network is used for boundary computation, while two other sub-networks are used for computing the physical fields of hyperelastic material, rubber. In both stages, physical constraints are incorporated into the sub-networks. These subnetworks are trained hybridly through the cascaded framework using data obtained from the finite element model (FEM), which was verified by experimental results. In order to validate the CM-PINN model, FEM data are used as a reference solution for comparison with conventional PINN. To evaluate the advantages of CM-PINN, ablation tests are conducted by randomly selecting training samples with different sizes. It is found that CM-PINN has higher accuracy and convergence compared to hybrid output PINNs. CM-PINN shows remarkable improvement in its generalization ability in the case of small sample size, underscoring its robust applicability across different data scenarios.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"43 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141255753","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 reference framework for the digital twin smart factory based on cloud-fog-edge computing collaboration","authors":"Zhiyuan Li, Xuesong Mei, Zheng Sun, Jun Xu, Jianchen Zhang, Dawei Zhang, Jingyi Zhu","doi":"10.1007/s10845-024-02424-0","DOIUrl":"https://doi.org/10.1007/s10845-024-02424-0","url":null,"abstract":"<p>Digital twin (DT) is an important approach for the factory to achieve intelligence. Due to the different scenarios and definitions, the generalization of frameworks for DT-based smart factories is weak, slowing down the overall process of industrial intelligence. Meanwhile, the pressure of data transmission and processing increases dramatically because of data explosion, which poses a challenge to the rational allocation of computing resources. In addition, more advanced strategies for training and running models are needed to support more sophisticated services. This paper proposes a reference framework that combines DT and cloud-fog-edge computing collaboration (CFE). First, the DT fuses physical and virtual spaces. The virtual-real fusion provides more information for operations, and the virtual space gives more accurate and timely decisions based on the constantly refreshed state. Secondly, by introducing CFE, suitable operating platforms for each layer of the DT-based smart factory are set, which enhances data interaction and reduces the dependence on cloud computing. The DT-CFE framework is well generalized. This paper first introduces the definition of the DT-based smart factory and its components. Then the methodology of the DT-CFE-based smart factory is proposed, and the network topology and operation mechanism are introduced. In this framework, the transmission and response performance of its data interaction is tested, and the interference of dynamic events occurring through scheduling is studied to illustrate the effectiveness and superiority of the framework.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"52 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141255751","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":"Explainable artificial intelligence and multi-stage transfer learning for injection molding quality prediction","authors":"Chung-Yin Lin, Jinsu Gim, Demitri Shotwell, Mong-Tung Lin, Jia-Hau Liu, Lih-Sheng Turng","doi":"10.1007/s10845-024-02436-w","DOIUrl":"https://doi.org/10.1007/s10845-024-02436-w","url":null,"abstract":"<p>High-precision optical products made of polymeric materials have been surging in recent years due to the prevalence of smartphones and their camera modules. Manufacturing fast-changing generations of high-precision optical lenses with accurately predicted qualities is a challenging task. Simulations and modern artificial intelligence (AI) techniques play crucial roles in accelerating precise process development. Coupled with computer simulation, this research employs a fusion of explainable AI (XAI) and multi-stage transfer learning (TL) approaches with artificial neural network (ANN) models to predict the surface profile variation of injection-molded polycarbonate (PC) lenses. The proposed method efficiently bridges preliminary simulations to injection molding experiments, covering a complete process development workflow from feature selection, process modeling, to experimental investigation in the same modeling domain. Only one model from scratch is required, which carries knowledge to the final quality prediction model. When compared with the conventional TL and the naïve model, the multi-stage TL approach provides better predictions with a maximum reduction of 64% and 43% in simulation and actual manufacturing data requirement, respectively. This research demonstrates a viable connection between each stage in the injection molding (IM) process development in predicting the qualities of high-precision optical lenses. Meanwhile, the combined usage of XAI and (multi-stage) TL confirms model explanations and pinpoints a potential pathway to assess future TL capabilities from the modeling perspectives.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"37 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187791","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":"Workplace performance measurement: digitalization of work observation and analysis","authors":"Janusz Nesterak, Marek Szelągowski, Przemysław Radziszewski","doi":"10.1007/s10845-024-02419-x","DOIUrl":"https://doi.org/10.1007/s10845-024-02419-x","url":null,"abstract":"<p>Process improvement initiatives require access to frequently updated and good quality data. This is an extremely difficult task in the area of production processes, where the lack of a process digital footprint is a very big challenge. To solve this problem, the authors of this article designed, implemented, and verified the results of a new work measurement method. The Workplace Performance Measurement (WPM) method is focused not only on the measurement of task duration and frequency, but also on searching for potential anomalies and their reasons. The WPM method collects a wide range of workspace parameters, including workers' activities, workers' physiological parameters, and tool usage. An application of Process Mining and Machine Learning solutions has allowed us to not only significantly increase the quality of analysis (compared to analog work sampling methods), but also to implement an automated controlling solution. The genuine value of the WPM is attested to by the achieved results, like increased efficiency of production processes, better visibility of process flow, or delivery of input data to MES solutions. MES systems require good quality, frequently updated information, and this is the role played by the WPM, which can provide this type of data for Master Data as well as for Production Orders. The presented authorial WPM method reduces the gap in available scholarship and practical solutions, enabling the collection of reliable data on the actual flow of business processes without their disruption, relevant for i.a. advanced systems using AI.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"33 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166862","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":"Digital twin and predictive quality solution for insulated glass line","authors":"Gülcan Aydin, Mehmet Tezcan, Bayram Ozgen, Tuğçe Nur Özkan","doi":"10.1007/s10845-024-02426-y","DOIUrl":"https://doi.org/10.1007/s10845-024-02426-y","url":null,"abstract":"<p>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.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"1 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166633","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":"Enhancing robustness to novel visual defects through StyleGAN latent space navigation: a manufacturing use case","authors":"Spyros Theodoropoulos, Dimitrios Dardanis, Georgios Makridis, Patrik Zajec, Jože M. Rožanec, Dimosthenis Kyriazis, Panayiotis Tsanakas","doi":"10.1007/s10845-024-02415-1","DOIUrl":"https://doi.org/10.1007/s10845-024-02415-1","url":null,"abstract":"<p>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.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"33 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166680","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 data-driven time-sequence feature-based composite network of time-distributed CNN-LSTM for detecting pore defects in laser penetration welding","authors":"Shenghong Yan, Bo Chen, Caiwang Tan, Xiaoguo Song, Guodong Wang","doi":"10.1007/s10845-024-02391-6","DOIUrl":"https://doi.org/10.1007/s10845-024-02391-6","url":null,"abstract":"<p>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.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"48 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150386","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":"Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process","authors":"Mochamad Denny Surindra, Gusti Ahmad Fanshuri Alfarisy, Wahyu Caesarendra, Mohamad Iskandar Petra, Totok Prasetyo, Tegoeh Tjahjowidodo, Grzegorz M. Królczyk, Adam Glowacz, Munish Kumar Gupta","doi":"10.1007/s10845-024-02410-6","DOIUrl":"https://doi.org/10.1007/s10845-024-02410-6","url":null,"abstract":"<p>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.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"53 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063206","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":"Three-dimensional fabric smoothness evaluation using point cloud data for enhanced quality control","authors":"Zhijie Yuan, Binjie Xin, Jing Zhang, Yingqi Xu","doi":"10.1007/s10845-024-02367-6","DOIUrl":"https://doi.org/10.1007/s10845-024-02367-6","url":null,"abstract":"<p>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.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"38 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063173","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}