Advanced Engineering Informatics最新文献

{"title":"A data-driven metric-based proper orthogonal decomposition method with Shapley Additive Explanations for aerodynamic shape inverse design optimization","authors":"Chenliang Zhang ,&nbsp;Hongbo Chen ,&nbsp;Xiaoyu Xu ,&nbsp;Yanhui Duan ,&nbsp;Guangxue Wang","doi":"10.1016/j.aei.2025.103277","DOIUrl":"10.1016/j.aei.2025.103277","url":null,"abstract":"<div><div>In the present study, an effective optimization framework of aerodynamic shape inverse design is established based on the data-driven metric-based proper orthogonal decomposition (DMPOD) method. This framework employs a DMPOD method that filters superior data sets and POD bases using a data-driven filtering strategy with Shapley Additive Explanations (SHAP) and a modified application criterion for bases. The efficiency of the framework is improved by reduced design variables and narrowed design space which both benefit from the DMPOD method. In the DMPOD method, the data-driven filtering strategy effectively filters superior data sets, addressing the limitations of traditional methods, while geometric approximation-based sample generation method enhances dynamic change capture during the optimization process. In addition, the modified application criterion for bases selects important bases based on their relevance to the objective function and determines the necessary quantity. The effectiveness, efficiency, and robustness of the optimization framework are validated by the inverse design case of the RAE2822 airfoil. The results show that the optimization framework with DMPOD effectively enhances optimization efficiency and robustness, with improvements of 31.32% and 84.89% for superior data set, respectively, compared to the MPOD method, and have a better optimization effect.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103277"},"PeriodicalIF":8.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User needs insights from UGC based on large language model","authors":"Wei Wei,&nbsp;Chenliang Hao,&nbsp;Zixin Wang","doi":"10.1016/j.aei.2025.103268","DOIUrl":"10.1016/j.aei.2025.103268","url":null,"abstract":"<div><div>With limited resources, it is critical for companies to understand and address user needs to gain a competitive edge.The methods that utilize large-scale user-generated content (UGC) produced by the internet can analyze user needs efficiently and accurately. However, these methods have not been extensively studied.This paper proposes a framework based on large language model (LLM) to extract user’s insights into the priority of product attributes. First, product attributes are extracted from user reviews using LLM. Then, the mapping network between user reviews and satisfaction is established through sentiment analysis based on the LLM and Multi-layer Perceptron (MLP) classification. Finally, a comprehensive analysis of product importance is conducted using a proposed quantified IPA-Kano model. An empirical study on smart wearable bands is conducted to offer an intuitive and quantifiable analysis of user attention and satisfaction for each product attribute. The strengths and weaknesses of the products are highlighted, providing valuable insights that can inspire companies to adopt user-centric optimization strategies.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103268"},"PeriodicalIF":8.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating crack pattern entropy measures with synthesized learners for accumulated seismic damage evaluation in reinforced concrete frames","authors":"Mostafa Kaboodkhani,&nbsp;Mohammadjavad Hamidia,&nbsp;Hamid Bayesteh","doi":"10.1016/j.aei.2025.103271","DOIUrl":"10.1016/j.aei.2025.103271","url":null,"abstract":"<div><div>Reliable decision-making for reinforced concrete buildings affected by earthquakes relies on realistically measuring accumulated seismic damage. The limitations and uncertainties of vision-based qualitative subjective inspection highlight the necessity of quantitative assessment approaches. In this paper, an integrated method is developed for post-earthquake inspection of reinforced concrete buildings, using crack texture entropy quantification and the well-known cumulative Park-Ang damage quantification model. A comprehensive database comprising 969 crack textures is collected by the authors from cyclic-tested beam-column sub-assemblages. For beam, column, and joint crack textures, the multi-scale pixel-based Renyi entropy measures are computed through box-counting, and results are then linked to the associated experimentally computed structural accumulated damage. The dissipated energy weight, <em>β</em>, in the Park-Ang damage index is calculated by collapse state response for all specimens. Contrary to simplified speculations and error-prone <em>β</em> equations, this strategy controlled the damage index divergence from 1 at failure. The influential crack image-related measures are detected by correlation analysis and also by non-linear permutation feature importance. Two alternative scenarios are organized for the intelligent inspection of structures according to post-earthquake circumstances based on structural information accessibility/inaccessibility. Soft machine learning-based algorithms, encompassing eight diverse-structured techniques, are synthesized with Aquila optimizer to improve efficiency and generalizability. In the first alternative, the Gaussian process regression provided satisfactory prediction with the coefficient of determination equal to 0.87 despite lacking structural information merely using crack image-based measures. This score reached 0.89 by adding structural parameters to the first alternative inputs in the Categorical Boosting model. The slight improvement in the accuracy of the second alternative demonstrates crack pattern adequacy in the quantitative inspection. Afterward, the seismic damage limits of crack pattern entropy measures are extracted using the model explanation, and the efficiency of the proposed approach is proved by assessing new crack images.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103271"},"PeriodicalIF":8.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction regulatory document digitalization with layout knowledge-informed object detection and semantic text recognition","authors":"Shuyi Wang ,&nbsp;Seonghyeon Moon ,&nbsp;Yuguang Fu ,&nbsp;Jinwoo Kim","doi":"10.1016/j.aei.2025.103278","DOIUrl":"10.1016/j.aei.2025.103278","url":null,"abstract":"<div><div>Construction documents, containing extensive project information, are often stored and shared in unstructured paper formats, leading to inefficiencies in retrieval and transfer among stakeholders. There has been a pressing need for digitalizing construction documents by converting Portable Document Format documents into machine-readable, structured texts. However, current optical character recognition technologies struggle with complex layouts commonly found in construction project documents. To address this issue, we propose a construction document digitalization approach integrated with layout knowledge-informed object detection and semantic text recognition, improving recognition accuracy across various layouts and preserving the structural integrity of texts. Results show that our approach can reduce the average word error rate by 5.6 %p with the assistance of layout knowledge and achieve a structural similarity of 78.8 %, while achieving 87.4 % mAP@50 for layout analysis. These findings highlight the positive impacts of layout knowledge on digitalizing construction documents and underscore the practical viability of our approach.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103278"},"PeriodicalIF":8.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mKGMPP: A multi-layer knowledge graph integration framework and its inference method for manufacturing process planning","authors":"Zechuan Huang ,&nbsp;Xin Guo ,&nbsp;Chong Jiang ,&nbsp;Mingyue Yang ,&nbsp;Hao Xue ,&nbsp;Wu Zhao ,&nbsp;Jie Wang","doi":"10.1016/j.aei.2025.103266","DOIUrl":"10.1016/j.aei.2025.103266","url":null,"abstract":"<div><div>Manufacturing process planning is the process of organizing the production steps based on product design. It aimed at determining the process routes and formulating resource allocation strategies in response to digital model. In the process of connecting product design and manufacturing, designers and manufacturing technicians focus on different aspects of the digital model. This leads to a distortion when manufacturing technicians transform the digital model information into process information. As a result, this results in a deviation in the mapping between design intent and process intent. Such deviations can lead to disconnections in the association of process knowledge, undermine the consistency and traceability between process documents and digital models. Therefore, this study proposes a multi-layer knowledge graph for manufacturing process planning(mKGMPP) and an interactive manufacturing process planning system (IMPP system) driven by digital model and the proposed knowledge framework. The historical process schemes are analyzed using a dual-dimensional approach based on text and digital model. An extraction strategy based on structured data parsing and intelligent agent processing is employed for textual knowledge extraction. For geometric feature knowledge, the OpenCV library is employed, along with Gaussian blur, morphological operations, and the Canny detection algorithm. The intra knowledge of process schemes is integrated using the 4M1E elements, and multi-dimensional relationships between process schemes are established based on TQCSE. The geometric similarity inference module, process scheme inference module, and processing content modification module have been developed, and an interactive interface has been built based on Gradio. A manufacturing process planning of a slender shaft in the aerospace domain validates the rationality of the proposed knowledge organization framework and knowledge inference method.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103266"},"PeriodicalIF":8.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric spatial constraints network for slender and tiny surface defect detection","authors":"Chenghan Pu ,&nbsp;Jun Wang ,&nbsp;Yuan Zhang ,&nbsp;Muyuan Niu ,&nbsp;Qiaoyun Wu ,&nbsp;Ziyu Lin","doi":"10.1016/j.aei.2025.103138","DOIUrl":"10.1016/j.aei.2025.103138","url":null,"abstract":"<div><div>Detecting defects on aircraft impeller surfaces is challenging due to the thin and fragile structure of certain defects, as well as their varying scale and geometry. To address these two challenges, we propose the Geometric Spatial Constraints Network (GSCNet) for precise impeller defect detection. First, we develop an automatic image acquisition equipment to capture high-quality data of impeller surface defects. Subsequently, we introduce GSCNet, which comprises two main components: Rich Semantic Information Representation (RSIR) and Spatial Correlation Awareness (SCA) to detect surface defects. Within RSIR, we propose a geometric-constraints-guided, deformable-convolution-based module named Slender Partial Convolution (SPC), along with a Multi-Geometric Feature Fusion (MFF) module. SPC captures the features of tubular structures without redundant information by aligning the convolution kernel shape with slender defects, while MFF facilitates the fusion of various semantic features, thereby enhancing the ability to extract semantic information. In SCA, we introduce a novel attention mechanism that captures inherent spatial correlation to enhance the high-similarity defects classification capability by modeling representative spatial information. Finally, we design a similarity-enhanced loss function to further improve the detection of multiple geometric defects simultaneously, as it alleviates the scale sensitivity of IoU-based loss. Comparative experiments demonstrate that our framework outperforms all representative detection models, achieving 83.2% mAP on the AISD dataset, which surpasses the second-best model by 3.8%. The first set of ablation experiments confirms the effectiveness of each module within the framework. The second set of ablation experiments on the NEU-SEG and MT datasets validates the feature extraction and plug-and-play capability of RSIR. The generalization ability of GSCNet is further demonstrated on the NEU-DET and GC10-DET datasets.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103138"},"PeriodicalIF":8.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PA-WSDIS: A prior-aware weakly supervised defect instance segmentation model for car body surface","authors":"Yike He ,&nbsp;Yueming Wang ,&nbsp;Weiwei Jiang ,&nbsp;Songyu Hu ,&nbsp;Jianzhong Fu","doi":"10.1016/j.aei.2025.103254","DOIUrl":"10.1016/j.aei.2025.103254","url":null,"abstract":"<div><div>Car body surface defect instance segmentation is essential for ensuring product quality and setting precise size thresholds for defects during product inspection process. However, few defect instance segmentation applications has been found in industrial scenarios until now. This is due in large part to the fact that the pixel-level annotation of defects is cumbersome and labor-intensive. Although various weakly supervised methods have shown promising results, they usually lack the ability to fully explore prior information and the awareness of hierarchical semantic correlations, thereby limiting the defect instance segmentation performance. To address this issue, we propose a novel prior-aware weakly supervised defect instance segmentation (PA-WSDIS) model for car body surface, removing the need for pixel-level labeling. First, we design a box-driven coarse mask generator to obtain coarse masks, which serve as potential proposals for the subsequent refinement process. Then, we propose a boundary guided prior constraint loss, consisting of boundary alignment and pixel-pair similarity mining losses, to fully leverage prior information to enhance the discriminative ability and provide reliable refinement guidance for the model. Finally, we propose a correlative semantic calibration loss, which comprehensively perceives the rich semantic features of different dimensions from both local and global perspectives. With the collaborative constraints of these meticulously designed loss functions, precise instance segmentation results are achieved. Experimental results showcase the outstanding performance of the PA-WSDIS model with an impressive 87.4% <span><math><msubsup><mrow><mi>mAP</mi></mrow><mrow><mn>50</mn></mrow><mrow><mi>mask</mi></mrow></msubsup></math></span>, which is considerably superior to state-of-the-art methods. As far as we know, our proposed method is the first weakly supervised instance segmentation model based on bounding box labels for industrial defect detection tasks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103254"},"PeriodicalIF":8.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 transfer learning method: Universal domain adaptation with noisy samples for bearing fault diagnosis","authors":"Yi Sun,&nbsp;Hongliang Song,&nbsp;Liang Guo,&nbsp;Hongli Gao,&nbsp;Ao Cao","doi":"10.1016/j.aei.2025.103243","DOIUrl":"10.1016/j.aei.2025.103243","url":null,"abstract":"<div><div>Under the influence of frequent start-stop driving and rail launching during the service of urban rail vehicles, the source domain samples contain a large number of noise labels and noise samples. Moreover, the feature distribution and sample categories of the target domain and source domain are different because the urban rail vehicles are affected by the fluctuation of passenger flow and long-term service. This paper summarizes this real task in rail transportation as universal domain adaptation with noisy samples (UDANS). A novel multibranch convolutional neural network is proposed to solve the above problem. By optimizing the divergence of the two classifier outputs, the following objectives can be achieved: detecting noisy source samples, finding private classes in the target domain, and aligning the distribution of the source domain and the target domain. Finally, the results of the wheelset bearing dataset show that the method has advantages in rail transportation fault diagnosis.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103243"},"PeriodicalIF":8.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a self-cognitive complex product design system: A fine-grained multi-modal feature recognition and semantic understanding approach using large language models in mechanical engineering","authors":"Xinxin Liang,&nbsp;Zuoxu Wang,&nbsp;Jihong Liu","doi":"10.1016/j.aei.2025.103265","DOIUrl":"10.1016/j.aei.2025.103265","url":null,"abstract":"<div><div>Facing the promising tendency of human-artificial intelligence (AI) collaborative product design, fine-grained and multi-modal mechanical part recognition and semantic understanding have become a basic task for achieving a self-cognitive product design system. However, traditional semantic understanding approaches for mechanical parts can only handle single-modal data, which is either textual or image data, resulting in the following limitations 1) insufficient mining on fine-grained part’s functional/behavioral/structural information, and 2) ineffectiveness on multi-modal part information alignment, therefore restricting the intelligence level of the previous product design assistants. To mitigate these challenges, this paper proposes a fine-grained multimodal reasoning approach for mechanical part semantic understanding. The proposed approach utilizes a pre-trained Convolutional Neural Network (CNN) for visual feature extraction, a large language model (LLM) called LLaMA3 for advanced textual analysis, and a Unified Feature Fusion Module (UFFM) to facilitate robust cross-modal interactions. A positive and negative sample generation mechanism is implemented to refine the model’s ability to discern subtle variations in complex components. Experimental evaluations on the Industrial Part Multimodal Dataset (IPMD) demonstrate a significant improvement in classification accuracy, providing a more precise and intelligent solution for the semantic understanding in complex product design systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103265"},"PeriodicalIF":8.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using sociotechnical network modeling to analyze the impact of blockchain for supply chain on the risk of procuring counterfeit electronic parts","authors":"Hirbod Akhavantaheri ,&nbsp;Peter Sandborn ,&nbsp;Diganta Das","doi":"10.1016/j.aei.2025.103272","DOIUrl":"10.1016/j.aei.2025.103272","url":null,"abstract":"<div><div>Safety-critical, mission-critical, and infrastructure-critical systems (e.g., aerospace, transportation, defense, and power generation) are forced to source parts over exceptionally long periods of time from a supply chain that they do not control. Such systems are exposed to the dual risks of the impacts of system failure and the exposure to an unauthorized electronics marketplace over decades. Therefore, critical systems operators, manufacturers, and sustainers, must implement policies and technologies to reduce the risk of obtaining counterfeit parts.</div><div>Blockchain technology, as a distributed ledger platform, has shown promise for resolving the issues associated with a lack of trust, transparency in peer-to-peer transactional networks, and compromised supply chains. There are opportunities to apply blockchain for supply chain concepts to mitigate the risks associated with part authenticity in the electronic part supply chain.</div><div>This paper introduces a supply-chain blockchain framework resilient to aging (e.g., the loss of involvement of the original component manufacture and its authorized distributors, and loss of part transaction history). An agent-based model is introduced as a novel platform to test the impact of the proposed blockchain framework on supply-chain parties as well as the prevalence of counterfeits in the electronics supply chain. The model can validate the proposed protocol over the entire life cycle of a part (i.e., from active production to discontinuance and beyond) and predict the parties’ adoption rates, and changes in the prevalence of counterfeit parts.</div><div>Application of the model to a public participation blockchain based on Ethereum ERC- 721 protocols indicates that the participation level of independent distributors directly affects the efficacy of blockchain in the prevention of transactions containing counterfeit parts. A proposed certification-based blockchain participation approach can be effective if certifications require large enough test accuracy limits and high previous owner certification thresholds.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103272"},"PeriodicalIF":8.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}