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Hybrid manufacturing of a 3D-shaped fiber metal laminate

JOURNAL OF MANUFACTURING processES 2025年 145卷 85-98页

作者： Merzkirch, Matthias Juin, Erwan Eman, Jesper Pettersson, Jocke Juntikka, Magdalena Ahlqvist, Fredrik Safvenberg, Olof RISE Res Inst Sweden AB Box 104 SE-43122 Molndal Sweden RISE Res Inst Sweden AB Fibervagen 2 SE-43122 Ojebyn Sweden

This contribution presents a methodology for designing, manufacturing, and testing of a multi-material solution demonstrator of a lower control arm for electric vehicle (EV) chassis made of a three-dimensional Fiber reinforced polymer-Metal Laminate (FML). The Integrated Computational Materials Engineering (ICME) approach includes simulation methodology for process modeling, i.e. forming and draping, and part performance with the aim to reduce the developing time and related trial and errors. The challenges, besides a limited availability of resources and material input data for numerical models, include the combination of different forming methods for Glass Fiber Reinforced Polymers (GFRP) and sheet metals (aluminum alloy) with the aim of simultaneous forming of both materials. Especially the sheet metal forming needed several improvement steps regarding heat treatment state to increase the ductility and reduce crack propagation, as well as optimization of the shape of the blanks to be formed into an asymmetric, threedimensional geometry. Assembly includes adhesive bonding of the flat FML to the curved structure, and adapters for the testing to be performed. The quasi-static misuse testing is in good agreement to the results obtained from the simulated structural performance, with the weakest location being the adhesive bond line. An outlook on potential improvements regarding process simulation for manufacturing Fiber Metal Laminates, including necessary input data, is provided.

关键词： Fiber metal laminate (FML) Forming Compaction testing AA6181 AA6082 Finite element analysis (FEA) Glass fiber reinforced polymer (GFRP)

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DCLCSE: Dynamic Curriculum Learning Based Contrastive Learning of Sentence Embeddings

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IEEE TRANSACTIONS ON BIG data 2025年第2期11卷 635-647页

作者： Liu, Chang Zhang, Dacao Wang, Meng Hefei Univ Technol Sch Comp & Informat Hefei 230029 Anhui Peoples R China

Recently, Contrastive Learning (CL) has made impressive progress in natural language processing, especially in sentence representation learning. Plenty of data augmentation methods have been proposed for the generation of positive samples. However, due to the highly abstract nature of natural language, these augmentations cannot maintain the quality of generated positive samples, e.g., too easy or hard samples. To this end, we propose to improve the quality of positive examples from a data arrangement perspective and develop a novel model-agnostic approach: Dynamic Curriculum Learning based Contrastive Sentence Embedding framework (DCLCSE) for sentence embeddings. Specifically, we propose to incorporate a curriculum learning strategy to control the positive example usage. At the early learning stage, easy samples are selected to optimize the CL-based model. As the model's capability increases, we gradually select harder samples for model training, ensuring the learning efficiency of the model. Furthermore, we design a novel difficulty measurement module to calculate the difficulty of generated positives, in which the model's capability is considered for the accurate sample difficulty measurement. Based on this, we develop multiple arrangement strategies to facilitate the model learning process based on learned difficulties. Finally, extensive experiments over multiple representative models demonstrate the superiority of DCLCSE. As a byproduct, we have released the codes to facilitate other researchers.

关键词： Task analysis Contrastive learning Training Representation learning data augmentation Computational modeling Semantics curriculum learning sample difficulty sentence representation

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IF-ASS: An interpretable framework for arterial stiffness screening using photoplethysmography conduction analysis and feature pooling algorithm

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BIOMEDICAL SIGNAL processING AND control 2025年 106卷

作者： Li, Yunlong Ma, Xueya Gao, Lisheng Ma, Zuchang Sun, Yining Chinese Acad Sci Hefei Inst Phys Sci Inst Intelligent Machines Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Bengbu Med Coll Sch Nursing Bengbu 233030 Anhui Peoples R China

Early screening of arterial stiffness plays a crucial role in reducing cardiovascular risk and mortality, as well as promoting healthy aging. However, asymptomatic patients are frequently overlooked due to the lack of specialized interpretation and the costly hospital-based equipment required by traditional clinical methods, potentially leading to adverse consequences. This study presents IF-ASS, an interpretable framework designed to facilitate widespread screening of arterial stiffness at an earlier stage by utilizing routinely acquired photoplethysmography (PPG) signals. The data was collected from 205 participants (aged 21-82 years) through two recruitment phases. A novel vascular volume analysis (VVA) of PPG conduction was proposed to expanded the methods for extracting general PPG features. And subsequently 216 features were derived. The features, characterized by their meaningful functionalities, were combined using the proposed feature pooling (FP) algorithm. This method attained the highest dimensionality reduction rate (DRR = 94.9 %, 15.17 % improvement), while offering scalable combinations for model enhancement. The optimal trade-off between effectiveness and interpretability was achieved by modeling an improved probability risk explanation method based on eXtreme Gradient Boosting and Shapley Additive exPlanations. Among multidimensional comparisons and validations, IF- ASS demonstrated superior stability, delivering the highest area under the receiver operating characteristic curve (AUC) of 0.924, and accuracy (ACC) of 0.850 (3.25 % mean improvement). Attributively, inspired explorations significantly boosted the trustworthiness of IF-ASS. In conclusion, the IF-ASS could be considered a reliable and cost-effective tool for screening latent arterial stiffness in ubiquitous settings, particularly in the trends of disease onset at younger ages and an aging population.

关键词： Arterial Stiffness Early Screening Feature Pooling Interpretability Vascular Volume analysis Photoplethysmography Conduction analysis

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modeling Dynamics of Short Video Dissemination in Social Media Environments Considering data Chain 4

Modeling Dynamics of Short Video Dissemination in Social Med...

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4th International conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Guanghua, Zhou Shandong Management University Shandong Jinan250357 China

ISBN: (纸本)9798331523923

With the rapid development of social media, short videos have become an important carrier of the information dissemination. For example, the dissemination network analysis of short videos is an important research branch of social network analysis. Hence, modeling dynamics of short video dissemination in social media environments considering data chain is studied. The core of this study is to explore the mechanism of short video information dissemination in social media environment. On the theoretical level, this study constructed a short video dissemination chain model by analyzing the density of social networks. At the same time, it also broke through the impact of social networks on short video dissemination by combining user interaction and content dissemination behavior. In terms of video information processing, this study uses a video data compression algorithm based on H.265 and combines it with a deep learning optimization algorithm to compress and process short video dissemination data. In terms of social network construction, this study uses a heterogeneous graph attention network across social media networks. In theory, a multi-feature fusion method is proposed to optimize the network structure based on the heterogeneity and dynamics of user information on different social media platforms. At the same time, this study studied the information diffusion sequence and network topology in short video dissemination by constructing a data chain co-evolution mechanism. On this basis, a dissemination prediction model based on deep learning was proposed. The experiment verified the proposed framework from the network simulation level. © 2025 IEEE.

关键词： Video analysis

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A Practical Approach to data Digitalization Application for Boosting Production Enhancement Activities in Malaysia's Aging Brown Fields Following the Feasibility Study of Electrical Submersible Pump at the National Level

A Practical Approach to Data Digitalization Application for ...

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2025 International Petroleum Technology conference, IPTC 2025

作者： Segaran, T. Chandra Sidek, M.A.M. Trjangganung, K. Kamaruddin, I.I. Ahnap, M.A.S. Universiti Teknologi Malaysia Johor Skudai Malaysia Mise Energy Sdn. Bhd. Kuala Lumpur Malaysia

ISBN: (纸本)9781959025436

With over 80% of Malaysia's fields relying on gas lift, a late life crisis is evident, prompting secondary and tertiary production enhancements to sustain oil production. Challenges like gas lift gas shortages, aging facilities, and increased water cut possess efficient oil recovery limitations, which fueled the demand for an alternative lift technology in offshore Malaysia brown fields. However, replacing a gas lift system is a tedious task. This study proposes an automated screening method swiftly identify candidates, reducing the time and workforce needed to select from hundreds of wells and streamlining production optimization activities for brown fields selected in offshore Malaysia. This data input and analysis automation system will be tested with data from multiple fields to pre-screen the strings and establish a basket of opportunities before proceeding with screening using well modeling. From the vast number of attributes used in this study, essential parameters that will be used for candidate screening have been identified. The automation of this data for screening enables narrowing down the number of potential candidates for modeling and detailed analysis. Electrical submersible pump (ESP) design and economic analysis will be done once the candidate has been finalized through well modelling. Critical parameters such as remaining reserve, absolute open flow, well angle, dog leg severity, the latest liquid rate, the latest oil rate, the latest water cut, the latest gas oil ratio, sand count and identified integrity issues are essential for a production enhancement activity. By digitalizing this data, automation will ease the path to identify a pool of production enhancement candidates out of hundreds of strings in a field. The identified candidate will proceed to the maturation process where well modeling, design, and economic analysis will be conducted. This process saves time and increases the number of candidates available for production enhancement act

关键词： Gas lifts

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Research on Medical Efficiency of Beijing-Tianjin-Hebei Based on SBM Model

Research on Medical Efficiency of Beijing-Tianjin-Hebei Base...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Bai, Yanxin Dong, Yaru Ding, Haozhe Zhao, Chengyu Yang, Zixi Liu, Shuo Zhang, Xinpeng Song, Weiyou School of Economics and Management Hebei University of Technology 300131 China School of Artificial Intelligence and Data Science Hebei University of Technology 300131 China School of Mathematics and Statistics Hebei University of Economics and Trade 050064 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Based on the data of Beijing-Tianjin-Hebei region from 2011 to 2019,this paper measures the medical efficiency of Beijing-Tianjin-Hebei region in recent ten years by using the super efficiency SBM model, and uses the ML index decomposition method to conduct a dynamic analysis of medical efficiency, and further discusses the calculation results through convergence analysis, The medical efficiency in Beijing-Tianjin-Hebei region was studied. The study found that the overall medical efficiency in Beijing-Tianjin-Hebei region showed an increasing trend, but the increase was not large, and there was greater room for progress;The convergence of ML index changes is also high, indicating the overall development of Beijing-Tianjin-Hebei region. © 2025 SPIE.

关键词： Medical research data modeling Analytical research data acquisition Medical equipment Diseases and disorders Systems modeling Biomedical applications Education and training Energy efficiency

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An Adaptive Continual Learning Method for Nonstationary Industrial Time Series Prediction

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1160-1169页

作者： Wu, Mengqing Zhou, Xiaofeng Li, Shuai Shi, Haibo Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Deep learning models have gained significant attention and application in recent years to improve the accuracy and efficiency of industrial time series prediction. However, the dynamic changes in industrial processes present a key challenge for data-driven models. Specifically, the performance of deployed models deteriorates over time and fails to adapt to new operating conditions. Currently, two common update methods exist: Retraining the model using historical and new operating data, which incurs high computation and storage costs, or incrementally fine-tuning the model solely using new data, which leads to catastrophic forgetting of learned patterns. To address these issues, this article proposes an adaptive continual learning method for nonstationary industrial time series prediction. Our approach tackles the problems by hint-based network parameter learning to retain the dark knowledge from previous tasks and avoid catastrophic forgetting of accumulated knowledge. In addition, we design a soft buffer to aid memory and learning of key patterns under the current operating condition. Lastly, a time-sensitive activation function is proposed to endow the neural network with time-evolving properties, thereby enhancing the model's generalization ability. Compared with other update methods and different continual learning methods, the superiority of our method is validated on solar power generation data and real data of grinding and grading process.

关键词： Continuing education Adaptation models Predictive models data models Training Time series analysis Computational modeling Production Optimization Service robots Adaptive continual learning catastrophic forgetting generalization nonstationary industrial process time series prediction

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Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals

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APPLIED ENERGY 2025年 377卷

作者： Sifat, Md. Mhamud Hussen Choudhury, Safwat Mukarrama Das, Sajal K. Pota, Hemanshu Yang, Fuwen Rajshahi Univ Engn & Technol Rajshahi 6204 Bangladesh Univ New South Wales Canberra ACT Australia Griffith Univ Gold Coast Qld Australia

Managing the current high penetration of renewable energy sources globally poses a significant challenge due to the distributed and diverse nature of generation components. To maximize real-time power generation, these resources need to perform optimally. Digital twin technology offers a comprehensive framework for managerial support by replicating grid features in a digital environment. This research creates a digital twin of the microgrid to optimize power generation, focusing on computational efficiency and self-healing control. The framework is tested in a laboratory microgrid, with modeling performed using a polynomial regression algorithm. Optimization is achieved through a gradient descent algorithm, and the self-healing model is implemented using a logistic regression algorithm. Real-time data extracted from the microgrid drives this process. The results can be utilized for predictive analysis before deploying a microgrid or to optimize generation in existing systems using the digital twin model. Even though the research focuses on a single microgrid unit, it introduces a framework proposal for extensively distributed microgrids integrating multiple renewable energy sources.

关键词： Digital twin microgrid control Energy management Self-healing Microgrid optimization

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Defect Detection in Industrial Soldering processes Using Machine Learning: A Critical Literature Review

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IEEE ACCESS 2025年 13卷 41533-41558页

作者： Eisentraut, Luca Hosch, Johanna Roytenberg, Maksym Benecke, Amelie Penava, Pascal Buettner, Ricardo Helmut Schmidt Univ Univ Fed Armed Forces Hamburg Chair Hybrid Intelligence D-22043 Hamburg Germany

As electrical devices take on more life-critical roles, such as in autonomous driving, ensuring the quality of solder joints during production becomes increasingly important. Recently, there has been a growing interest in using machine learning techniques for this purpose. However, current research lacks a comprehensive overview that categorizes and analyzes relevant studies based on their specific intervention points within the production process. This literature review aims to examine and evaluate research coverage along three dimensions: intervention points in the process, non-destructive testing methods, and machine learning techniques employed. For this review, 112 conference papers and journal articles published since 2010 were selected from three databases using the PRISMA methodology. These publications were classified into the three dimensions previously mentioned, summarized, and analyzed. Furthermore, the literature core is critically evaluated to identify research gaps and limitations. The analysis shows that most studies focus on solder joint control, with few addressing intervention points in solder paste and component placement. Visual imaging and neural networks are the dominant techniques for non-destructive testing and machine learning, respectively. Despite a variety of literature that uses high-performance neural networks, meeting industrial detection standards often requires tolerating high false alarm rates. The findings contribute to structuring existing research and identifying research needs, particularly in validating these systems and integrating data from various testing methods and intervention points.

关键词： Systematic literature review Soldering Defect detection Testing Quality control Visualization Production process control Inspection databases Critical literature review defect detection machine learning solder joint

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data-driven estimation of impedance of inverter-based resources for efficient stability evaluation

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Mohammed, Nabil Zhou, Weihua Ramasubramanian, Deepak Bahrani, Behrooz Dutta, Sudipta Bello, Mobolaji Monash Univ Clayton Vic Australia Elect Power Res Inst Golden CO USA

Inverter-based resources (IBRs) are key enabling technologies for integrating renewable energy sources and providing ancillary services in modern power systems. However, their dynamic behavior, defined by output impedance models, can pose a threat to power system stability. The primary challenge is that impedance models, typically derived at specific operating points, exhibit limited accuracy under varying conditions. Additionally, the lack of detailed vendor information on commercial IBR control structures complicates the accurate derivation of these models. To address these issues, this paper first investigates the effects of grid parameters and variations in IBR operating points on IBR's impedance model. Afterwards, a data-driven algorithm using Gaussian process regression (GPR) is then proposed to predict impedance models in the dq reference frame, achieving accurate results with a minimal dataset, thus reducing the cost and complexity of data collection for stability evaluation. The proposed approach is validated through case studies that compare predicted impedance models with analytical solutions for various IBR configurations and grid scenarios, including both grid-following and grid-forming inverters. Its superiority over artificial neural network (ANN)- based approaches is demonstrated using the same training dataset. The predicted impedance model is employed to evaluate IBR stability in the frequency domain, with findings validated through time-domain simulations using an electromagnetic transient (EMT) model when connected to grids of varying strengths. A promising application of the proposed GPR-based impedance modeling is its integration into IBR-based power system stability analysis and simulation tools, facilitating the study of emerging low-frequency oscillation phenomena.

关键词： Gaussian process regression Grid-connected inverters Grid impedance Impedance measurement Nyquist Operating point Stability System identification

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