Advanced Engineering Informatics最新文献_第7页

Mitigating potential risk via counterfactual explanation generation in blast-based tunnel construction

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-06 DOI: 10.1016/j.aei.2025.103227

Fenghua Liu , Wenli Liu , Jiajing Liu , Botao Zhong , Jun Sun

{"title":"Mitigating potential risk via counterfactual explanation generation in blast-based tunnel construction","authors":"Fenghua Liu , Wenli Liu , Jiajing Liu , Botao Zhong , Jun Sun","doi":"10.1016/j.aei.2025.103227","DOIUrl":"10.1016/j.aei.2025.103227","url":null,"abstract":"<div><div>Machine learning and deep learning have significantly enhanced the ability to mitigate risks in blast-based tunnel construction. However, most studies fall short in model constraints, data quality, and explainability, making non-robust risk mitigation strategies. Therefore, this study aims to investigate the following questions: <em>how to accurately assess risk for blast-based tunnel construction using limited data, and develop effective risk mitigation strategies?</em> This research leverages counterfactual explanation generation, a key technique of explainable artificial intelligence, along with data augmentation to develop a framework for guiding risk mitigation, which includes:</div><div>(1) a two-stage data augmentation technique to address data shortage and imbalance; (2) a novel counterfactual explanation generation algorithm to optimize blasting parameters and reduce risk; and (3) a post-hoc explainable approach to provide insights on feature importance. A railway tunnel in Hubei is conducted as a case study to test the validity of the proposed method. The results show that the proposed method accurately predict overbreak, achieving the highest <em>R</em><sup>2</sup> (0.883) and the lowest RMSE (1.335) compared to baseline models. Additionally, it effectively optimizes the blasting parameters to mitigate risk, reducing the average overbreak in six scenarios. The explainable analytic identifies key factors (e.g., periphery hole spacing) influencing construction risk, thereby enhancing personnel’s understanding of complex construction systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103227"},"PeriodicalIF":8.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552927","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}

引用次数: 0

Fast detection of short circuits in copper electrolytic refining with PCA and a branching perceptron

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-06 DOI: 10.1016/j.aei.2025.103239

Yusi Dai , Chunhua Yang , Hongqiu Zhu , Can Zhou , Xi Wang

{"title":"Fast detection of short circuits in copper electrolytic refining with PCA and a branching perceptron","authors":"Yusi Dai , Chunhua Yang , Hongqiu Zhu , Can Zhou , Xi Wang","doi":"10.1016/j.aei.2025.103239","DOIUrl":"10.1016/j.aei.2025.103239","url":null,"abstract":"<div><div>Short circuit faults greatly affect the current efficiency and product quality of copper electrolytic refining. Irregular cloth covers and uneven heating make it difficult to detect short-circuit faults of copper electrolytic refining with infrared images, so complex object detection models believed to do well in feature digs were previously used. Yet, such methods have high computation costs, which limits the detection efficiency and hinders the function expansion to portable devices. We find that with proper feature extraction, the model can be succinct, and the checkbox output of object detection models is not the most applicable form. Therefore, this paper proposes a fast multilabel classification method to detect the short circuit faults of copper electrolytic refining with principal components analysis (PCA) and a branching perceptron. In the method, PCA reduces data dimensions in an unsupervised and reversible way according to the maximum projection variance principle since faults appear as discrepancy signals in images. Then, a branching perceptron is presented for fault identification. Each branch corresponds with a pair of anode and cathode electrodes and the output of the branch predicts the state of the pair. Reversible low-dimensional features obtained with PCA can reduce the pressure on data transfer and storage, and support a more succinct detection model to fasten the training and detection. The binary sequence form of the designed output is more convenient for on-site fault removal and other purposes. The proposed PCA-Perceptron method is verified on real-world data of electrolytic refining of recycled copper.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103239"},"PeriodicalIF":8.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552704","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}

引用次数: 0

A multiple-criteria sensor selection framework based on qualitative physical models

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-05 DOI: 10.1016/j.aei.2025.103228

Xiaoxu Diao, Md Ragib Rownak, Samuel Olatubosun, Pavan Kumar Vaddi, Carol Smidts

{"title":"A multiple-criteria sensor selection framework based on qualitative physical models","authors":"Xiaoxu Diao, Md Ragib Rownak, Samuel Olatubosun, Pavan Kumar Vaddi, Carol Smidts","doi":"10.1016/j.aei.2025.103228","DOIUrl":"10.1016/j.aei.2025.103228","url":null,"abstract":"<div><div>Sensor selection is critical for designing effective online monitoring systems for safety–critical applications. This paper proposes a novel sensor selection framework that utilizes qualitative system models to evaluate various sensor configurations based on multiple criteria. The criteria assess capabilities like fault diagnostics, risk reduction, observability, functionality, integrability, and cost. The framework uses the Integrated System Failure Analysis to generate signal features from qualitative system models. These features are used to evaluate sensor configurations against the selection criteria. The criteria can be applied as constraints or objectives for optimization. The Non-dominated Sorting Genetic Algorithm handles the multi-objective optimization to find Pareto optimal sensor deployment solutions. The framework is demonstrated on a reactor cavity cooling system case study, generating optimal configurations considering temperature, flow, pressure, density, and radiation sensors. The framework aids online monitoring system design by recommending sensor deployment configurations that balance critical capabilities. Qualitative models provide effective analysis despite the lack of operational data. The flexible criteria and multi-objective optimization enable extensive exploration of configurations in early development stages.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103228"},"PeriodicalIF":8.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552174","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}

引用次数: 0

Crafting user-centric prompts for UI generations based on Kansei engineering and knowledge graph

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-05 DOI: 10.1016/j.aei.2025.103217

Xuejing Feng , Huifang Du , Jun Ma , Haofen Wang , Lijuan Zhou , Meng Wang

{"title":"Crafting user-centric prompts for UI generations based on Kansei engineering and knowledge graph","authors":"Xuejing Feng , Huifang Du , Jun Ma , Haofen Wang , Lijuan Zhou , Meng Wang","doi":"10.1016/j.aei.2025.103217","DOIUrl":"10.1016/j.aei.2025.103217","url":null,"abstract":"<div><div>Text-to-image (T2I) models are emerging as a powerful tool for designers to create user interface (UI) prototypes from natural language inputs (i.e., prompts). However, the discrepancy between designer inputs and model-preferred prompts makes it challenging for designers to consistently deliver effective results to end users. To bridge this gap, we introduce a novel hybrid method that assists designers in crafting user-centric prompts for T2I models, ensuring that the generated UIs align with end-user expectations. First, this method merges text mining and Kansei Engineering (KE) to analyze online user reviews and construct a Knowledge Graph (KG), mapping the intricate relationships between diverse affective requirements of users, design features, and corresponding text prompts for UI generation. Then, our approach automatically transforms designer inputs into model-preferred prompts through entity mention recognition and entity linking during the human-AI collaborative design process. Finally, we validate the proposed approach with a case study on automotive human–machine interface design. Experimental results demonstrate that our approach achieves high performance in perceived efficiency, satisfaction, and expectation disconfirmation. Overall, this study represents a step forward in integrating human and AI contributions in design and innovation within engineering disciplines, enabling AI to inspire, develop, and reinforce human creativity from a human factors perspective.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103217"},"PeriodicalIF":8.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552175","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}

引用次数: 0

Predicting the performance status of aero-engines using a spatio-temporal decoupled digital twin modeling method

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-04 DOI: 10.1016/j.aei.2025.103218

Dasheng Xiao, Shuo Song, Hong Xiao, Zhanxue Wang

{"title":"Predicting the performance status of aero-engines using a spatio-temporal decoupled digital twin modeling method","authors":"Dasheng Xiao, Shuo Song, Hong Xiao, Zhanxue Wang","doi":"10.1016/j.aei.2025.103218","DOIUrl":"10.1016/j.aei.2025.103218","url":null,"abstract":"<div><div>The digital twin model for predicting engine performance enhances engine health management. Key indicators such as exhaust gas temperature (EGT) and thrust are essential for evaluating engine performance. This study focuses on extracting and integrating complex spatio-temporal features from multiple sensors to construct an effective prediction model. A data-driven modeling method that combines the physical structure of an engine while achieving spatio-temporal feature decoupled was proposed. This method is based on Long Short-Term Memory (LSTM) and a self-attention mechanism, and incorporates time-variant parameter derivatives into the model’s input using first-order backward differences. Case studies were conducted on the EGT and thrust predictions. The mean absolute relative error (<span><math><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></math></span>) was used to evaluate the accuracy of each test, whereas the average <span><math><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></math></span> (<span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></msub></math></span>) across ten tests was used to assess the accuracy of each model. The results show that the spatio-temporal decoupled modeling method improves prediction accuracy and stability, achieving a minimum <span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>M</mi><mi>A</mi><mi>R</mi><mi>E</mi></mrow></msub></math></span> of 0.64% for the EGT and 0.277% for the normalized thrust. Furthermore, to test the method’s robustness against varying sampling frequencies during deployment, the sampling intervals of the test data were adjusted to simulate changes in sampling frequency. The results demonstrate that the proposed method exhibits excellent stability.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103218"},"PeriodicalIF":8.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552929","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}

引用次数: 0

Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-04 DOI: 10.1016/j.aei.2025.103195

Jiaxuan Shi , Fei Qiao , Juan Liu , Yumin Ma , Dongyuan Wang , Chen Ding

{"title":"Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning","authors":"Jiaxuan Shi , Fei Qiao , Juan Liu , Yumin Ma , Dongyuan Wang , Chen Ding","doi":"10.1016/j.aei.2025.103195","DOIUrl":"10.1016/j.aei.2025.103195","url":null,"abstract":"<div><div>Effective manufacturing in flexible job shops often requires collaboratively organizing production and logistics activities. This necessitates a thorough exploration of corresponding collaborative scheduling problem. However, extant studies remain relatively preliminary, not only neglecting the inevitable disturbances in real-world but also failing to satisfy the essential need for collaboration, that is, to simultaneously optimize both activities’ objectives. Therefore, this study proposes a novel production-logistics collaborative scheduling problem for dynamic flexible job shops, in which the common yet underappreciated disturbance of logistics equipment breakdowns is meticulously considered, and two typical objectives individually pursued by two activities are optimized simultaneously. To solve the proposed problem, a nested-hierarchical deep reinforcement learning method is developed. In this method, a new nested-hierarchical framework that rationally deploys multiple agents is designed to facilitate the required multi-objective optimization while ensuring the practicality of decision-making process. Based on this framework, appropriate state features, actions, and reward functions are devised for each agent, and a training mechanism based on multi-agent proximal policy optimization is proposed to train agents effectively. Experiments in an aviation component production shop are conducted to confirm the effectiveness of proposed method and problem.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103195"},"PeriodicalIF":8.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552930","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}

引用次数: 0

Multi-scale 4D localized spatio-temporal graph convolutional networks for spatio-temporal sequences forecasting in aluminum electrolysis

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-03 DOI: 10.1016/j.aei.2025.103222

Yubo Sun , Xiaofang Chen , Weihua Gui , Lihui Cen , Yongfang Xie , Zhong Zou

{"title":"Multi-scale 4D localized spatio-temporal graph convolutional networks for spatio-temporal sequences forecasting in aluminum electrolysis","authors":"Yubo Sun , Xiaofang Chen , Weihua Gui , Lihui Cen , Yongfang Xie , Zhong Zou","doi":"10.1016/j.aei.2025.103222","DOIUrl":"10.1016/j.aei.2025.103222","url":null,"abstract":"<div><div>Spatio-temporal sequences forecasting fulfills a vital role in the intelligent advancement of aluminum electrolysis production process. The localized spatio-temporal correlations contained in spatio-temporal sequences, caused by the dynamicity of regional working conditions, have complex and diverse (multi-scale) characteristics. The existing deep learning-based prediction methods are difficult to capture the multi-scale localized spatio-temporal correlations, and the adverse effects of industrial noise on spatio-temporal correlation acquisition have not been considered. In this article, we propose the multi-scale 4D localized spatio-temporal graph convolutional networks (Ms-4D-LStGCN) to address the above issues. Concretely, we propose a data-driven accurate similarity search method and fuse the prior knowledge to construct the spatio-temporal graph. Then,a novel 4D localized spatio-temporal graph convolution module is proposed to capture the complex localized spatio-temporal correlations. Finally, the multi-scale 4D localized spatio-temporal graph convolution framework is designed to obtain the multi-scale and multi-depth localized spatio-temporal correlation features. Illustrative examples on 16 real-world industrial aluminum electrolysis datasets attest that our method has superior prediction performance compared with state-of-the-art methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103222"},"PeriodicalIF":8.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529440","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}

引用次数: 0

A physics-guided approach for accurate battery SOH estimation using RCMHCRE and BatteryPINN

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-03 DOI: 10.1016/j.aei.2025.103211

Yaming Liu , Haolin Chen , Ligang Yao , Jiaxin Ding , Shiqiang Chen , Zhenya Wang

{"title":"A physics-guided approach for accurate battery SOH estimation using RCMHCRE and BatteryPINN","authors":"Yaming Liu , Haolin Chen , Ligang Yao , Jiaxin Ding , Shiqiang Chen , Zhenya Wang","doi":"10.1016/j.aei.2025.103211","DOIUrl":"10.1016/j.aei.2025.103211","url":null,"abstract":"<div><div>Accurate monitoring of a battery’s state of health (SOH) is crucial for ensuring reliable operation. Data-driven methods for SOH estimation often involve complex feature extraction strategies and models that are difficult to interpret, limiting their generalizability. To overcome these challenges, this paper presents a battery SOH estimation method based on the refined composite multiscale Hilbert cumulative residual entropy algorithm (RCMHCRE) and the battery physical information neural network (BatteryPINN). First, the proposed RCMHCRE algorithm is applied to automatically extract high-quality health features from the battery’s voltage and current data, serving as the feature engineering in this study. Second, the network structure of BatteryPINN is developed for SOH prediction, based on the mathematical theory of solid electrolyte interphase (SEI) membrane growth. The proposed strategy enables BatteryPINN to be constrained by the battery aging mechanism during training, thereby ensuring that the network adheres to the underlying physical laws during propagation. To validate the effectiveness of the proposed method, a four-month battery aging experiment is conducted, and a dataset is constructed. Experimental results from three datasets demonstrate that the proposed method offers significant advantages in health feature extraction and SOH estimation compared to other state-of-the-art battery SOH estimation methods, achieving prediction accuracies of less than 1% for both RMSE and MAPE metrics.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103211"},"PeriodicalIF":8.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529494","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}

引用次数: 0

A new performance evaluation model based on approximate belief rule base with local uncertainty

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-03 DOI: 10.1016/j.aei.2025.103225

Jie Wang , Pengyun Ning , Zhijie Zhou , Peng Zhang

{"title":"A new performance evaluation model based on approximate belief rule base with local uncertainty","authors":"Jie Wang , Pengyun Ning , Zhijie Zhou , Peng Zhang","doi":"10.1016/j.aei.2025.103225","DOIUrl":"10.1016/j.aei.2025.103225","url":null,"abstract":"<div><div>Performance evaluation is of vital significance in guaranteeing the reliable operation of complex systems. During the process of performance evaluation, the limitations of expert knowledge and insufficient observation data pose challenges in differentiating adjacent performance states of complex systems. As such, a new performance evaluation model based on the approximate belief rule base with local uncertainty (ABRB-LU) is proposed in this paper. Regarding the model inference, the local uncertainty is assigned to the predefined vague state, which can effectively address the difficulty of distinguishing adjacent performance states. Subsequently, the multiple belief rules incorporating local uncertainty are fused by employing the evidential reasoning (ER) rule, contributing to establishing the evaluation model based on ABRB-LU. Meanwhile, an optimization objective is set to improve the evaluation accuracy. Regarding the model analysis, starting from two belief rules, a rigorous mathematical derivation is carried out to obtain the sensitivity factor of the evaluation results concerning the local uncertainty. On this basis, the analysis process is extended to multiple belief rules, forming a generalized method for sensitivity analysis. This can provide a scientific basis for decision-makers to locate weak links. An engineering example of servo mechanism is carried out to verify the validity of the proposed model.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103225"},"PeriodicalIF":8.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529441","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}

引用次数: 0

Deep-learning-driven intelligent tool wear identification of high-precision machining with multi-scale CNN-BiLSTM-GCN

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-03-02 DOI: 10.1016/j.aei.2025.103234

Zhicheng Xu , Baolong Zhang , Louis Luo Fan , Edward Hengzhou Yan , Dongfang Li , Zejia Zhao , Wai Sze Yip , Suet To

{"title":"Deep-learning-driven intelligent tool wear identification of high-precision machining with multi-scale CNN-BiLSTM-GCN","authors":"Zhicheng Xu , Baolong Zhang , Louis Luo Fan , Edward Hengzhou Yan , Dongfang Li , Zejia Zhao , Wai Sze Yip , Suet To","doi":"10.1016/j.aei.2025.103234","DOIUrl":"10.1016/j.aei.2025.103234","url":null,"abstract":"<div><div>In high-precision machining, the inevitable tool wear will significantly affect the surface quality. Traditional tool wear modeling is complicated due to the complex mathematical reasoning process and the identification of numerous unknown parameters linked to the wear mechanism. Data-driven modeling for tool wear prediction can avoid the above issues but suffers from high-cost and low-efficiency because of amounts of expensive tool consumption and time-consumed experiments for generating the training dataset. To fill this gap, this study proposed an innovative approach to accurately identifying tool wear in high-precision machining effortlessly. Firstly an unsupervised Modified Toeplitz Inverse Covariance Clustering (MTICC) algorithm was first proposed to objectively categorize tool wear phase from multi-channel time-series data to break through traditional manual-experience-based division, whose effectiveness was validated by the well-designed experiments. Then, a hybrid deep learning model with a multi-scale CNN-BiLSTM-GCN and cross-attention structures was developed to deeply extract spatial–temporal features from multi-channel signals by first considering the interdependencies of the sensor network for higher accuracy. After hyperparameters optimization, features importance analysis was conducted to identify the most important features, which are “X-force”, “Y-force” and “Phase1 Active Power”, and the hyperparameters importance quantitatively analyze the contribution of CNN, BiLSTM, and GCN modules, respectively. Through the comparative studies, the proposed multi-scale CNN-BiLSTM-GCN model performed better with a weighted average F1 score of 0.987 than other models. The proposed model was finally employed on the intelligent IoT platform and successfully achieved the real-time identification of tool wear in the HPM process.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103234"},"PeriodicalIF":8.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527434","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}

引用次数: 0