Journal of Intelligent Manufacturing最新文献

{"title":"Allocation of geometrical errors for developing precision measurement machine","authors":"Tao Lai, Junfeng Liu, Fulei Chen, Zelong Li, Chaoliang Guan, Huang Li, Chao Xu, Hao Hu, Yifan Dai, Shanyong Chen, Zhongxiang Dai","doi":"10.1007/s10845-024-02440-0","DOIUrl":"https://doi.org/10.1007/s10845-024-02440-0","url":null,"abstract":"<p>A high-precision measurement machine tool faces the challenge of correlating the overall motion accuracy with the components form and positional accuracy. This study presents an innovative method for addressing this issue in ultra-precision measuring machines. A geometric error model based on multibody theory, and a weight model are established to predict measurement results and correlate overall motion accuracy with individual component accuracy. To validate the model, a target overall motion accuracy of 100 nm is set and the all the individual components accuracy is calculated by the geometric error weights derived from the proposed model. By fabricating a critical component, the linear guideway, to meet specific individual accuracies and incorporating it in an ultra-precise measurement machine, the study demonstrates achieving the individual accuracies with the magnetorheological polishing. Finally, the overall motion accuracy is validated by a cross test among the designed machine, DUI profilometer, and Zygo interferometer. By measuring a same optical surface, the measurement results show the surface PV differences better than 100 nm. The results demonstrate the validation of the correlation between overall motion accuracy and component accuracy established by the method described in this paper. In conclusion, this study offers an accurate design solution for determining overall motion and individual accuracies, enabling high accuracy in intelligent manufacturing equipment.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"85 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504493","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":"Prediction of crater morphology and its application for enhancing dimensional accuracy in micro-EDM","authors":"Zequan Yao, Long Ye, Ming Wu, Jun Qian, Dominiek Reynaerts","doi":"10.1007/s10845-024-02430-2","DOIUrl":"https://doi.org/10.1007/s10845-024-02430-2","url":null,"abstract":"<p>As a non-conventional machining technique, the micro electrical discharge machining (micro-EDM) process primarily involves the removal of material from the workpiece through high-frequency discharges. The machined surface is covered with multiple overlapping craters to form geometric features with specific surface quality and dimensional accuracy. Consequently, there is a significant need to explore the crater morphology induced by the discharge pulses, which contributes to the precise control of component size and shape. This study targets the identification of material removal in relation to pulse-crater matching within micro-EDM. Initially, pertinent parameters of both pulses and craters are characterized and correlated through a single pulse discharge experiment. Subsequently, accompanied by a pulse classification, a continuous pulse discharge experiment is designed to establish a one-to-one correspondence between erosion craters and the discharge pulses associated with normal discharge, effective discharge, and arc phenomena, which all contribute to material removal. The impact of different discharge pulse types on workpiece material removal is further investigated, with explanations based on energy density and the fraction of energy entering the workpiece. Employing machine learning methods, predictive models for crater-related parameters are developed based on the monitored electrical signals. A comparison of the prediction results from different regression models with various inputs confirms the profound nonlinearity and stochastic nature of the EDM process. Ultimately, the artificial neural network model shows to be optimal in predictive performance, yielding relative errors of 7.81%, 12.49%, and 18.82% for crater diameter, depth, and volume, respectively. Notably, the prediction error for cumulative material removal is only 1.64%, affirming the soundness of the proposed material removal identification for different discharge pulses. Other material removal volume calculation approaches often hinge on machining parameters or statistical distributions. Contrarily, the distinctive characteristic of this approach lies in its implementation of precise pulse-crater correlations of various discharge types based on in-process data. This method is further applied to the prediction of the total material removal volume in micro-EDM drilling. The results are promising for enhancing geometric dimension control in EDM, particularly regarding machining depth.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"52 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530842","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 digital twin approach for weld penetration prediction of tig welding with dual ellipsoid heat source","authors":"Huangyi Qu, Jianhao Chen, Yi Cai","doi":"10.1007/s10845-024-02431-1","DOIUrl":"https://doi.org/10.1007/s10845-024-02431-1","url":null,"abstract":"<p>Tungsten Inert Gas (TIG) welding is a manufacturing process that utilizes argon as a shielding gas and tungsten as an electrode to join metals at high temperatures. The weld penetration is the key to determine the quality of the weld. However, the lack of sensing technology makes weld penetration difficult to predict, which imposes a major challenge to process stability and weld quality. To address this challenge, a digital twin-driven method is proposed for characterizing the melt pool morphology and melt penetration prediction. To achieve this, an analytical model of the melt pool with time-varying welding speed under the action of a double ellipsoidal circular heat source is first derived. The analytical model is solved using the numerical integration method. The prediction of melt depth and melt width is achieved by extracting isotherms. Meanwhile, a digital reconstruction of the welding scene was achieved by implementing the Neural Radiance Fields (NeRF) method. The target rendering of the melt pool and welding scene is accomplished by constructing voxels and meshes. Furthermore, VR is utilized as the interface for human–computer interaction, and a digital twin model of the molten pool morphology and welding scene is generated. The prediction model's accuracy is verified through welding experiments using 304L steel on a robotic welding system. The results show that in the 0–4 s stage, the penetration error is controlled within 7%. In the stage of 4–16 s when the speed changes, the maximum error of penetration is 16.59%. In terms of welding scene reconstruction quality, PSNR is 33.98 and SSIM reaches 0.9032. The method allows real-life simulation of different welding conditions and parameter combinations prior to welding, assessing their impact on the welding results, in order to find the optimal configuration of process parameters. It can also be remotely realized to monitor and control the melt penetration in real-time during the welding process. This method provides a new solution and a theoretical guidance system to solve the welding penetration control problems and it plays an important role in promoting welding intelligence.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"92 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504491","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 chip inspection system based on a multiscale subarea attention network","authors":"Yun Hou, Hong Fan, Ying Chen, Guangshuai Liu","doi":"10.1007/s10845-024-02441-z","DOIUrl":"https://doi.org/10.1007/s10845-024-02441-z","url":null,"abstract":"<p>Cavities in a weld seriously affect the airtightness of the chip, which makes chip inspection a crucial step in intelligent manufacturing. In recent years, deep learning-based defect inspection models have shown significant advantages in reducing human errors. However, due to the scarcity of defective data, deep learning-based models are susceptible to overfitting. Moreover, the multiscale and uneven grayscale distribution of cavities further compound the challenges faced by these models. To address these issues, we develop a chip inspection system based on a multiscale subarea attention network (MSANet) for cavity defect detection. In the system, the segment anything model is embedded to interactively segment the weld. Furthermore, to circumvent the overfitting problem, a large-scale cavity dataset is built by splitting the segmented weld into multiple patches. Notably, a novel MSANet is proposed to precisely segment the varying cavities, and a source-to-destination Dijkstra algorithm is designed to assess the chip quality. The experimental results demonstrate that our chip inspection system achieves a 99.24% F1-score and 99.26% AUC.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"12 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504492","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":"Dual visual inspection for automated quality detection and printing optimization of two-photon polymerization based on deep learning","authors":"Ningning Hu, Lujia Ding, Lijun Men, Wenju Zhou, Wenjun Zhang, Ruixue Yin","doi":"10.1007/s10845-024-02417-z","DOIUrl":"https://doi.org/10.1007/s10845-024-02417-z","url":null,"abstract":"<p>Two-photon polymerization (TPP) has emerged as an advanced additive manufacturing technique, allowing for the creation of three-dimensional micro-nano structures with high precision based on two-photon absorption principle. Precisely control light dosage determined by the printing parameters, is crucial for inducing photopolymerization across different photocurable materials and various structures. To address the challenges of parameter optimization, deep learning models were employed to quickly obtained the ideal printing parameters through automated visual inspection during TPP printing process and after post-processing. A dataset was collected from the video recordings during printing process and the images obtained from after post-processing of samples. Data augmentation techniques were applied to enhance the dataset. For the TPP printing process, the mean prediction accuracy increasing from 95.1% to 96.8% for the 3D-CNN model and from 95.4% to 97.8% for the CNN-LSTM model. For the post-processing, the mean prediction accuracy with CNN model increases from 94.5% to 95.2%. Consequently, spatial–temporal DL models were trained based on these datasets, and the results of dual visual inspection method demonstrated a high accuracy of 93.1% and a rapid recognition time of 48 ms. And an analysis of the failure cases of the deep learning models was conducted. Additionally, the optimal printing parameter ranges was determination for various combinations of materials and structures. This system plays a crucial role in accelerating the optimization of TPP process parameters and quality inspection, effectively addressing the challenges in the industrialization process of TPP technology.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"80 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504495","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":"Cognitive manufacturing: definition and current trends","authors":"Fadi El Kalach, Ibrahim Yousif, Thorsten Wuest, Amit Sheth, Ramy Harik","doi":"10.1007/s10845-024-02429-9","DOIUrl":"https://doi.org/10.1007/s10845-024-02429-9","url":null,"abstract":"<p>Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"15 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516604","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 review and classification of manufacturing ontologies","authors":"Patrick Sapel, Lina Molinas Comet, Iraklis Dimitriadis, Christian Hopmann, Stefan Decker","doi":"10.1007/s10845-024-02425-z","DOIUrl":"https://doi.org/10.1007/s10845-024-02425-z","url":null,"abstract":"<p>One core concept of Industry 4.0 is establishing highly autonomous manufacturing environments. In the vision of Industry 4.0, the product leads its way autonomously through the shopfloor by communicating with the production assets. Therefore, a common vocabulary and an understanding of the domain’s structure are mandatory, so foundations in the form of knowledge bases that enable autonomous communication have to be present. Here, ontologies are applicable since they define all assets, their properties, and their interconnection of a specific domain in a standardized manner. Reusing and enlarging existing ontologies instead of building new ontologies facilitates cross-domain and cross-company communication. However, the demand for reusing or enlarging existing ontologies of the manufacturing domain is challenging as no comprehensive review of present manufacturing domain ontologies is available. In this contribution, we provide a holistic review of 65 manufacturing ontologies and their classification into different categories. Based on the results, we introduce a priority guideline and a framework to support engineers in finding and reusing existent ontologies of a specific subdomain in manufacturing. Furthermore, we present 16 supporting ontologies to be considered in the ontology development process and eight catalogs that contain ontologies and vocabulary services.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"74 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516605","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":"Research on salient object detection algorithm for complex electrical components","authors":"Jinyu Tian, Zhiqiang Zeng, Zhiyong Hong, Dexin Zhen","doi":"10.1007/s10845-024-02434-y","DOIUrl":"https://doi.org/10.1007/s10845-024-02434-y","url":null,"abstract":"<p>Due to the complexity of electrical components, traditional edge detection methods cannot always accurately extract key edge features of them. Therefore, this study constructs a dataset of complex electrical components and proposes a Step-by-Level Multi-Scale Extraction, Fusion, and Refinement Network (SMFRNet) that is based on the salient object detection algorithm. As detailed features includes a wealth of texture and shape characteristics that are related to edges, so the Hierarchical Deep Aggregation U-block (HDAU) is incorporated in the encoder as a means of capturing more details through hierarchical aggregation. Meanwhile, the proposed Multi-Scale Pyramid Convolutional Fusion (MPCF) and Fusion Attention Structure (FAS) achieve step-by-level feature refinement to obtain finer edges. In order to address the issues of imbalanced pixel categories and the difficulty in separating edge pixels, a hybrid loss function is also constructed. The experimental results indicate that this method outperforms nine state-of-the-art algorithms, enabling the extraction of high-precision key edge features. It provides a reliable method for key edge extraction in complex electrical components and provides important technical support for automated components measurement.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"28 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516606","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":"Framework of knowledge management for human–robot collaborative mold assembly using heterogeneous cobots","authors":"Yee Yeng Liau, Kwangyeol Ryu","doi":"10.1007/s10845-024-02439-7","DOIUrl":"https://doi.org/10.1007/s10845-024-02439-7","url":null,"abstract":"<p>Molds are assembled manually due to a shortage of skilled workers and challenges associated with automating operations, which arise from the low-volume, high-variety characteristics of mold production. This study proposed a human–robot collaborative mold assembly using two heterogeneous collaborative robots to address the ergonomic concerns. The use of two heterogeneous cobots enables the handling of different assembly requirements. The diversity of mold structure and different specifications of resources require comprehensive knowledge management to enable interaction and collaboration among resources. However, knowledge management in the domain of mold assembly is yet to be developed in a format understandable by both human and robots. Therefore, a framework of knowledge management is proposed to manage the knowledge within the human–robot collaboration (HRC) in a mold assembly domain. This framework includes an ontology-based decision making that utilizes outcomes from task assignment to decide the mold parts arrangement within the HRC workspace. A set of rules are modeled in the developed ontology for knowledge reasoning according to the use case of collaborative assembly of two-plate injection mold. In addition to part arrangement, the developed HRC ontology can be used to extract data and information based on user’s request and decisions, such as tool selection for subtask execution. The HRC mold assembly ontology serves as a stepping stone towards developing a context-based decision making for multi-resources HRC in future implementation.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"3 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504494","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 quantitative study of data aggregation for a network design problem: a case of automotive distribution","authors":"Suzanne Le Bihan, Gülgün Alpan, Bernard Penz","doi":"10.1007/s10845-024-02421-3","DOIUrl":"https://doi.org/10.1007/s10845-024-02421-3","url":null,"abstract":"<p>This paper presents a framework for a systematic analysis of the impact of data aggregation on a multi-product multi-period network design problem with batch cost. The optimization objective is to design the vehicle distribution network for an automotive manufacturer. Numerical experiments are conducted with real production data. Given the problem’s scale and complex constraints, data aggregation emerges as a natural strategy to help the convergence of resolution methods towards good solutions. We explore three aggregation dimensions: product type, spatial, and temporal, and for each of them, different levels. Addressing multiple aggregation dimensions is a novel approach that has not been extensively explored in current literature, especially within industrial settings. Our aggregation-disaggregation method reveals that data aggregation consistently leads to improved solutions within a constrained computation time, with temporal aggregation demonstrating the most significant reduction in problem size and solution improvement. Lastly, we give some managerial insights considering the industrial context.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"17 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530843","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}