Visual Computing for Industry Biomedicine and Art最新文献

Artificial intelligence-assisted diagnosis of early allograft dysfunction based on ultrasound image and data. 基于超声图像和数据的早期异体移植物功能障碍的人工智能辅助诊断。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-05-12 DOI: 10.1186/s42492-025-00192-z

Yaqing Meng, Mingyang Wang, Ningning Niu, Haoyan Zhang, Jinghan Yang, Guoying Zhang, Jing Liu, Ying Tang, Kun Wang

{"title":"Artificial intelligence-assisted diagnosis of early allograft dysfunction based on ultrasound image and data.","authors":"Yaqing Meng, Mingyang Wang, Ningning Niu, Haoyan Zhang, Jinghan Yang, Guoying Zhang, Jing Liu, Ying Tang, Kun Wang","doi":"10.1186/s42492-025-00192-z","DOIUrl":"10.1186/s42492-025-00192-z","url":null,"abstract":"Early allograft dysfunction (EAD) significantly affects liver transplantation prognosis. This study evaluated the effectiveness of artificial intelligence (AI)-assisted methods in accurately diagnosing EAD and identifying its causes. The primary metric for assessing the accuracy was the area under the receiver operating characteristic curve (AUC). Accuracy, sensitivity, and specificity were calculated and analyzed to compare the performance of the AI models with each other and with radiologists. EAD classification followed the criteria established by Olthoff et al. A total of 582 liver transplant patients who underwent transplantation between December 2012 and June 2021 were selected. Among these, 117 patients (mean age 33.5 ± 26.5 years, 80 men) were evaluated. The ultrasound parameters, images, and clinical information of patients were extracted from the database to train the AI model. The AUC for the ultrasound-spectrogram fusion network constructed from four ultrasound images and medical data was 0.968 (95%CI: 0.940, 0.991), outperforming radiologists by 30% for all metrics. AI assistance significantly improved diagnostic accuracy, sensitivity, and specificity (P < 0.050) for both experienced and less-experienced physicians. EAD lacks efficient diagnosis and causation analysis methods. The integration of AI and ultrasound enhances diagnostic accuracy and causation analysis. By modeling only images and data related to blood flow, the AI model effectively analyzed patients with EAD caused by abnormal blood supply. Our model can assist radiologists in reducing judgment discrepancies, potentially benefitting patients with EAD in underdeveloped regions. Furthermore, it enables targeted treatment for those with abnormal blood supply.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"13"},"PeriodicalIF":3.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph visualization efficiency of popular web-based libraries. 流行的网络图书馆的图形可视化效率。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-05-08 DOI: 10.1186/s42492-025-00193-y

Xin Zhao, Xuan Wang, Xianzhe Zou, Huiming Liang, Genghuai Bai, Ning Zhang, Xin Huang, Fangfang Zhou, Ying Zhao

{"title":"Graph visualization efficiency of popular web-based libraries.","authors":"Xin Zhao, Xuan Wang, Xianzhe Zou, Huiming Liang, Genghuai Bai, Ning Zhang, Xin Huang, Fangfang Zhou, Ying Zhao","doi":"10.1186/s42492-025-00193-y","DOIUrl":"https://doi.org/10.1186/s42492-025-00193-y","url":null,"abstract":"Web-based libraries, such as D3.js, ECharts.js, and G6.js, are widely used to generate node-link graph visualizations. These libraries allow users to call application programming interfaces (APIs) without identifying the details of the encapsulated techniques such as graph layout algorithms and graph rendering methods. Efficiency requirements, such as visualizing a graph with 3k nodes and 4k edges within 1 min at a frame rate of 30 fps, are crucial for selecting a proper library because libraries generally present different characteristics owing to the diversity of encapsulated techniques. However, existing studies have mainly focused on verifying the advantages of a new layout algorithm or rendering method from a theoretical viewpoint independent of specific web-based libraries. Their conclusions are difficult for end users to understand and utilize. Therefore, a trial-and-error selection process is required. This study addresses this gap by conducting an empirical experiment to evaluate the performance of web-based libraries. The experiment involves popular libraries and hundreds of graph datasets covering node scales from 100 to 200k and edge-to-node ratios from 1 to 10 (including complete graphs). The experimental results are the time costs and frame rates recorded using the libraries to visualize the datasets. The authors analyze the performance characteristics of each library in depth based on the results and organize the results and findings into application-oriented guidelines. Additionally, they present three usage cases to illustrate how the guidelines can be applied in practice. These guidelines offer user-friendly and reliable recommendations, aiding users in quickly selecting the desired web-based libraries based on their specific efficiency requirements for node-link graph visualizations.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"12"},"PeriodicalIF":3.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective. 人工智能在视网膜图像分析诊断高血压视网膜病变中的应用综述与展望。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-05-01 DOI: 10.1186/s42492-025-00194-x

Rajendra Kankrale, Manesh Kokare

{"title":"Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective.","authors":"Rajendra Kankrale, Manesh Kokare","doi":"10.1186/s42492-025-00194-x","DOIUrl":"https://doi.org/10.1186/s42492-025-00194-x","url":null,"abstract":"Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"11"},"PeriodicalIF":3.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12044089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LViT-Net: a domain generalization person re-identification model combining local semantics and multi-feature cross fusion. lvit.net：结合局部语义和多特征交叉融合的领域泛化人物再识别模型。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-04-16 DOI: 10.1186/s42492-025-00190-1

Xintong Hu, Peishun Liu, Xuefang Wang, Peiyao Wu, Ruichun Tang

{"title":"LViT-Net: a domain generalization person re-identification model combining local semantics and multi-feature cross fusion.","authors":"Xintong Hu, Peishun Liu, Xuefang Wang, Peiyao Wu, Ruichun Tang","doi":"10.1186/s42492-025-00190-1","DOIUrl":"https://doi.org/10.1186/s42492-025-00190-1","url":null,"abstract":"In the task of domain generalization person re-identification (ReID), pedestrian image features exhibit significant intra-class variability and inter-class similarity. Existing methods rely on a single feature extraction architecture and struggle to capture both global context and local spatial information, resulting in weaker generalization to unseen domains. To address this issue, an innovative domain generalization person ReID method-LViT-Net, which combines local semantics and multi-feature cross fusion, is proposed. LViT-Net adopts a dual-branch encoder with a parallel hierarchical structure to extract both local and global discriminative features. In the local branch, the local multi-scale feature fusion module is designed to fuse local feature units at different scales to ensure that the fine-grained local features at various levels are accurately captured, thereby enhancing the robustness of the features. In the global branch, the dual feature cross fusion module fuses local features and global semantic information, focusing on critical semantic information and enabling the mutual refinement and matching of local and global features. This allows the model to achieve a dynamic balance between detailed and holistic information, forming robust feature representations of pedestrians. Extensive experiments demonstrate the effectiveness of LViT-Net. In both single-source and multi-source comparison experiments, the proposed method outperforms existing state-of-the-art methods.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"10"},"PeriodicalIF":3.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation. 基于图形的视觉问答和场景图形策展的视觉可解释人工智能。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-04-07 DOI: 10.1186/s42492-025-00185-y

Sebastian Künzel, Tanja Munz-Körner, Pascal Tilli, Noel Schäfer, Sandeep Vidyapu, Ngoc Thang Vu, Daniel Weiskopf

{"title":"Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation.","authors":"Sebastian Künzel, Tanja Munz-Körner, Pascal Tilli, Noel Schäfer, Sandeep Vidyapu, Ngoc Thang Vu, Daniel Weiskopf","doi":"10.1186/s42492-025-00185-y","DOIUrl":"10.1186/s42492-025-00185-y","url":null,"abstract":"This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering (VQA) systems. The method focuses on identifying false answer predictions by the model and offers users the opportunity to directly correct mistakes in the input space, thus facilitating dataset curation. The decision-making process of the model is demonstrated by highlighting certain internal states of a graph neural network (GNN). The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset. The authors evaluated their tool through the demonstration of identified use cases, quantitative measures, and a user study conducted with experts from machine learning, visualization, and natural language processing domains. The authors' findings highlight the prominence of their implemented features in supporting the users with incorrect prediction identification and identifying the underlying issues. Additionally, their approach is easily extendable to similar models aiming at graph-based question answering.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"9"},"PeriodicalIF":3.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bootstrapping BI-RADS classification using large language models and transformers in breast magnetic resonance imaging reports. 在乳房磁共振成像报告中使用大语言模型和变压器引导BI-RADS分类。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-04-03 DOI: 10.1186/s42492-025-00189-8

Yuxin Liu, Xiang Zhang, Weiwei Cao, Wenju Cui, Tao Tan, Yuqin Peng, Jiayi Huang, Zhen Lei, Jun Shen, Jian Zheng

{"title":"Bootstrapping BI-RADS classification using large language models and transformers in breast magnetic resonance imaging reports.","authors":"Yuxin Liu, Xiang Zhang, Weiwei Cao, Wenju Cui, Tao Tan, Yuqin Peng, Jiayi Huang, Zhen Lei, Jun Shen, Jian Zheng","doi":"10.1186/s42492-025-00189-8","DOIUrl":"10.1186/s42492-025-00189-8","url":null,"abstract":"Breast cancer is one of the most common malignancies among women globally. Magnetic resonance imaging (MRI), as the final non-invasive diagnostic tool before biopsy, provides detailed free-text reports that support clinical decision-making. Therefore, the effective utilization of the information in MRI reports to make reliable decisions is crucial for patient care. This study proposes a novel method for BI-RADS classification using breast MRI reports. Large language models are employed to transform free-text reports into structured reports. Specifically, missing category information (MCI) that is absent in the free-text reports is supplemented by assigning default values to the missing categories in the structured reports. To ensure data privacy, a locally deployed Qwen-Chat model is employed. Furthermore, to enhance the domain-specific adaptability, a knowledge-driven prompt is designed. The Qwen-7B-Chat model is fine-tuned specifically for structuring breast MRI reports. To prevent information loss and enable comprehensive learning of all report details, a fusion strategy is introduced, combining free-text and structured reports to train the classification model. Experimental results show that the proposed BI-RADS classification method outperforms existing report classification methods across multiple evaluation metrics. Furthermore, an external test set from a different hospital is used to validate the robustness of the proposed approach. The proposed structured method surpasses GPT-4o in terms of performance. Ablation experiments confirm that the knowledge-driven prompt, MCI, and the fusion strategy are crucial to the model's performance.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"8"},"PeriodicalIF":3.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11968601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nucleus pulposus clamping procedures based on optimized material point method for surgical simulation systems. 基于优化物质点法的手术模拟系统髓核夹紧程序。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-04-01 DOI: 10.1186/s42492-025-00188-9

Jianlong Ni, Jingrong Li, Zhiyuan Xie, Qinghui Wang, Chunhai Li, Haoyu Wu, Yang Zhang

引用次数: 0

PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation. PCRFed：针对非独立与同分布医学图像分割的个性化联邦学习对比表征。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-03-28 DOI: 10.1186/s42492-025-00191-0

Shengyuan Liu, Ruofan Zhang, Mengjie Fang, Hailin Li, Tianwang Xun, Zipei Wang, Wenting Shang, Jie Tian, Di Dong

{"title":"PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation.","authors":"Shengyuan Liu, Ruofan Zhang, Mengjie Fang, Hailin Li, Tianwang Xun, Zipei Wang, Wenting Shang, Jie Tian, Di Dong","doi":"10.1186/s42492-025-00191-0","DOIUrl":"10.1186/s42492-025-00191-0","url":null,"abstract":"Federated learning (FL) has shown great potential in addressing data privacy issues in medical image analysis. However, varying data distributions across different sites can create challenges in aggregating client models and achieving good global model performance. In this study, we propose a novel personalized contrastive representation FL framework, named PCRFed, which leverages contrastive representation learning to address the non-independent and identically distributed (non-IID) challenge and dynamically adjusts the distance between local clients and the global model to improve each client's performance without incurring additional communication costs. The proposed weighted model-contrastive loss provides additional regularization for local models, optimizing their respective distributions while effectively utilizing information from all clients to mitigate performance challenges caused by insufficient local data. The PCRFed approach was evaluated on two non-IID medical image segmentation datasets, and the results show that it outperforms several state-of-the-art FL frameworks, achieving higher single-client performance while ensuring privacy preservation and minimal communication costs. Our PCRFed framework can be adapted to various encoder-decoder segmentation network architectures and holds significant potential for advancing the use of FL in real-world medical applications. Based on a multi-center dataset, our framework demonstrates superior overall performance and higher single-client performance, achieving a 2.63% increase in the average Dice score for prostate segmentation.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"6"},"PeriodicalIF":3.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Principal component analysis and fine-tuned vision transformation integrating model explainability for breast cancer prediction. 主成分分析与精细视觉转换整合乳癌预测模型可解释性。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-03-10 DOI: 10.1186/s42492-025-00186-x

Huong Hoang Luong, Phuc Phan Hong, Dat Vo Minh, Thinh Nguyen Le Quang, Anh Dinh The, Nguyen Thai-Nghe, Hai Thanh Nguyen

{"title":"Principal component analysis and fine-tuned vision transformation integrating model explainability for breast cancer prediction.","authors":"Huong Hoang Luong, Phuc Phan Hong, Dat Vo Minh, Thinh Nguyen Le Quang, Anh Dinh The, Nguyen Thai-Nghe, Hai Thanh Nguyen","doi":"10.1186/s42492-025-00186-x","DOIUrl":"10.1186/s42492-025-00186-x","url":null,"abstract":"Breast cancer, which is the most commonly diagnosed cancers among women, is a notable health issues globally. Breast cancer is a result of abnormal cells in the breast tissue growing out of control. Histopathology, which refers to the detection and learning of tissue diseases, has appeared as a solution for breast cancer treatment as it plays a vital role in its diagnosis and classification. Thus, considerable research on histopathology in medical and computer science has been conducted to develop an effective method for breast cancer treatment. In this study, a vision Transformer (ViT) was employed to classify tumors into two classes, benign and malignant, in the Breast Cancer Histopathological Database (BreakHis). To enhance the model performance, we introduced the novel multi-head locality large kernel self-attention during fine-tuning, achieving an accuracy of 95.94% at 100× magnification, thereby improving the accuracy by 3.34% compared to a standard ViT (which uses multi-head self-attention). In addition, the application of principal component analysis for dimensionality reduction led to an accuracy improvement of 3.34%, highlighting its role in mitigating overfitting and reducing the computational complexity. In the final phase, SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and Gradient-weighted Class Activation Mapping were used for the interpretability and explainability of machine-learning models, aiding in understanding the feature importance and local explanations, and visualizing the model attention. In another experiment, ensemble learning with VGGIN further boosted the performance to 97.13% accuracy. Our approach exhibited a 0.98% to 17.13% improvement in accuracy compared with state-of-the-art methods, establishing a new benchmark for breast cancer histopathological image classification.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"5"},"PeriodicalIF":3.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global residual stress field inference method for die-forging structural parts based on fusion of monitoring data and distribution prior. 基于监测数据融合和分布先验的模锻结构件整体残余应力场推断方法。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2025-03-06 DOI: 10.1186/s42492-025-00187-w

Shuyuan Chen, Yingguang Li, Changqing Liu, Zhiwei Zhao, Zhibin Chen, Xiao Liu

{"title":"Global residual stress field inference method for die-forging structural parts based on fusion of monitoring data and distribution prior.","authors":"Shuyuan Chen, Yingguang Li, Changqing Liu, Zhiwei Zhao, Zhibin Chen, Xiao Liu","doi":"10.1186/s42492-025-00187-w","DOIUrl":"10.1186/s42492-025-00187-w","url":null,"abstract":"Die-forging structural parts are widely used in the main load-bearing components of aircrafts because of their excellent mechanical properties and fatigue resistance. However, the forming and heat treatment processes of die-forging structural parts are complex, leading to high levels of internal stress and a complex distribution of residual stress fields (RSFs), which affect the deformation, fatigue life, and failure of structural parts throughout their lifecycles. Hence, the global RSF can provide the basis for process control. The existing RSF inference method based on deformation force data can utilize monitoring data to infer the global RSF of a regular part. However, owing to the irregular geometry of die-forging structural parts and the complexity of the RSF, it is challenging to solve ill-conditioned problems during the inference process, which makes it difficult to obtain the RSF accurately. This paper presents a global RSF inference method for the die-forging structural parts based on the fusion of monitoring data and distribution prior. Prior knowledge was derived from the RSF distribution trends obtained through finite element analysis. This enables the low-dimensional characterization of the RSF, reducing the number of parameters required to solve the equations. The effectiveness of this method was validated in both simulation and actual environments.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"4"},"PeriodicalIF":3.2,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11885777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0