AI EDAM最新文献 - Book学术

A knowledge transfer method for human-robot collaborative disassembly of end-of-life power batteries based on augmented reality 基于增强现实技术的报废动力电池人机协作拆卸知识转移方法

AI EDAM Pub Date : 2024-09-13 DOI: 10.1017/s0890060424000088

Jie Li, Liangliang Duan, Weibin Qu, Hangbin Zheng

{"title":"A knowledge transfer method for human-robot collaborative disassembly of end-of-life power batteries based on augmented reality","authors":"Jie Li, Liangliang Duan, Weibin Qu, Hangbin Zheng","doi":"10.1017/s0890060424000088","DOIUrl":"https://doi.org/10.1017/s0890060424000088","url":null,"abstract":"The disassembly of power batteries poses significant challenges due to their complex sources, diverse types, variations in design and manufacturing processes, and diverse service conditions. Human memory capacity and robot cognitive and understanding capabilities are limited when faced with different dismantling tasks for end-of-life power batteries. Insufficient human-computer interaction capabilities greatly hinder the efficiency of human-robot collaboration (HRC) operations. The existing HRC relies heavily on the experience of operators, while the existing disassembly system fails to update new disassembly strategies in real time when facing new battery varieties. Therefore, this paper proposes an augmented reality-assisted human-robot collaboration (AR-HRC) power battery dismantling system based on transfer learning. It consists of three modules: AR-HRC knowledge modeling, dismantling subgraph similarity assessment, and strategy transfer update. The AR-HRC knowledge modeling module aims to establish an intelligent mapping from tasks to collaborative strategies based on part features. Based on the evaluation of task similarity, the mobility assessment model divides subtasks into similar and dissimilar classes. For similar subtasks, the original dismantling strategy can be applied to the current task. However, for different subtasks, operators can issue instructions to the AR-HRC system through the human-computer interaction function of AR and develop new collaborative strategies based on actual conditions. Finally, a case study of power battery dismantling is conducted, and the results show that compared to traditional pre-programmed assembly, this system can improve dismantling efficiency and reduce cognitive burden.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel intelligent fault diagnosis method of bearing based on multi-head self-attention convolutional neural network 基于多头自关注卷积神经网络的轴承智能故障诊断新方法

AI EDAM Pub Date : 2024-05-17 DOI: 10.1017/s0890060423000197

Hang Ren, Shaogang Liu, Bo Qiu, Hong Guo, Dan Zhao

{"title":"A novel intelligent fault diagnosis method of bearing based on multi-head self-attention convolutional neural network","authors":"Hang Ren, Shaogang Liu, Bo Qiu, Hong Guo, Dan Zhao","doi":"10.1017/s0890060423000197","DOIUrl":"https://doi.org/10.1017/s0890060423000197","url":null,"abstract":"Deep learning (DL) has been widely used in bearing fault diagnosis. In particular, convolutional neural networks (CNNs) improve diagnosis accuracy by extracting excellent fault features. However, CNN lacks an explicit learning mechanism to distinguish between different fault characteristics in the input signal to the diagnosis results. This article presents a new end-to-end depth framework called multi-head self-attention convolution neural network (MSA-CNN) for bearing fault diagnosis. Firstly, we adopt a data pre-processing method that directly converts one-dimensional (1D) original signals into two-dimensional (2D) grayscale images, which is simple to implement and preserves the complete information of the original signal. Secondly, multi-head self-attention (MSA) is first constructed to aggregate the global information and adaptively assign weights to the input signal's features. Thirdly, the CNN with small-scale kernels extracted detailed local features. Finally, the learned high-level representations are fed into the full connect (FC) layer for fault diagnosis. The performance of the MSA-CNN is validated on different datasets. The results show that the proposed MSA-CNN can significantly improve fault diagnosis accuracy compared with the other state-of-the-art methods and has excellent noise immunity performance.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite-element analysis case retrieval based on an ontology semantic tree 基于本体语义树的有限元分析案例检索

AI EDAM Pub Date : 2024-05-14 DOI: 10.1017/s0890060424000040

Xuesong Xu, Zhenbo Cheng, Gang Xiao, Yuanming Zhang, Haoxin Zhang, Hangcheng Meng

引用次数: 0

Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN 利用 RSM-RSMOGA-MOGANN 混合机器学习方法实现准确快速的 3D 扫描，提高矫形器快速原型制作的可靠性

AI EDAM Pub Date : 2024-05-10 DOI: 10.1017/s0890060424000064

Ashwani Kumar, Deepak Chhabra

{"title":"Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN","authors":"Ashwani Kumar, Deepak Chhabra","doi":"10.1017/s0890060424000064","DOIUrl":"https://doi.org/10.1017/s0890060424000064","url":null,"abstract":"This study aims to develop a multidisciplinary artificial hybrid machine learning (AHML) approach to reduce the scanning time (ST) of the human wrist and improve the accuracy of 3D scanning for anthropometric data collection. A systematic AHML approach was deployed to scan the human wrist distal end optimally using a portable SENSE 2.0 3D scanner. A central composite design (CCD) matrix was developed for three input variables; light intensity (LI = 12–20 W/m2), capture angle (CA = 10°–50°), and scanning distance (SD = 10–20 inches) for executing the experimental runs. For accuracy evaluation, the wrist perimeter on the distal end was checked using CREO Parametric software for wrist perimeter error (WPE). Various AHML tools were developed using: response surface methodology (RSM), multi-objective genetic algorithm RSM, and multi-objective genetic algorithm neural networking (MOGANN). The optimal process parameters recommended by the hybrid tools were experimentally validated for their prediction accuracy. The MOGANN approach combined with the Bayesian regularization algorithm (trainabr) provided the best mutual combination of optimal ST = 20.072 sec and WPE = 0.375 cm corresponding to LI = 12.001 W/m2, CA = 29.428°, and SD = 18.214 inch, with a significant percentage reduction of 55.83% in WPE. Executing 3D scanning of the human wrist over the optimized process parameters predicted by AHML tools will ensure the availability of precise scans for the rapid prototyping of customized orthotic devices in a reliable manner.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of artificial intelligence and cognitive science in design 人工智能和认知科学在设计中的应用

AI EDAM Pub Date : 2024-05-03 DOI: 10.1017/s0890060424000052

Ji Han, Peter R.N. Childs, Jianxi Luo

{"title":"Applications of artificial intelligence and cognitive science in design","authors":"Ji Han, Peter R.N. Childs, Jianxi Luo","doi":"10.1017/s0890060424000052","DOIUrl":"https://doi.org/10.1017/s0890060424000052","url":null,"abstract":"Artificial intelligence and cognitive science are two core research areas in design. Artificial intelligence shows the capability of analysing massive amounts of data which supports making predictions, uncovering patterns and generating insights in varying design activities, while cognitive science provides the advantage of revealing the inherent mental processes and mechanisms of humans in design. Both artificial intelligence and cognitive science in design research are focused on delivering more innovative and efficient design outcomes and processes. Therefore, this thematic collection on “Applications of Artificial Intelligence and Cognitive Science in Design” brings together state-of-the-art research in artificial intelligence and cognitive science to showcase the emerging trend of applying artificial intelligence techniques and neurophysiological and biometric measures in design research. Three promising future research directions: 1) human-in-the-loop AI for design, 2) multimodal measures for design, and 3) AI for design cognitive data analysis and interpretation, are suggested by analysing the research papers collected. A framework for integration of artificial intelligence and cognitive science in design, incorporating the three research directions, is proposed to inspire and guide design researchers in exploring human-centred design methods, strategies, solutions, tools and systems.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel reinforcement learning framework for disassembly sequence planning using Q-learning technique optimized using an enhanced simulated annealing algorithm 利用 Q-learning 技术的新型强化学习框架，采用增强型模拟退火算法优化拆卸顺序规划

AI EDAM Pub Date : 2024-04-01 DOI: 10.1017/s0890060424000039

Mirothali Chand, Chandrasekar Ravi

{"title":"A novel reinforcement learning framework for disassembly sequence planning using Q-learning technique optimized using an enhanced simulated annealing algorithm","authors":"Mirothali Chand, Chandrasekar Ravi","doi":"10.1017/s0890060424000039","DOIUrl":"https://doi.org/10.1017/s0890060424000039","url":null,"abstract":"The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"263 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating large-language-model chatbots to engage communities in large-scale design projects 评估大型语言模型聊天机器人，让社区参与大型设计项目

AI EDAM Pub Date : 2024-03-18 DOI: 10.1017/s0890060424000027

Jonathan Dortheimer, Nik Martelaro, Aaron Sprecher, Gerhard Schubert

{"title":"Evaluating large-language-model chatbots to engage communities in large-scale design projects","authors":"Jonathan Dortheimer, Nik Martelaro, Aaron Sprecher, Gerhard Schubert","doi":"10.1017/s0890060424000027","DOIUrl":"https://doi.org/10.1017/s0890060424000027","url":null,"abstract":"Recent advances in machine learning have enabled computers to converse with humans meaningfully. In this study, we propose using this technology to facilitate design conversations in large-scale urban development projects by creating chatbot systems that can automate and streamline information exchange between stakeholders and designers. To this end, we developed and evaluated a proof-of-concept chatbot system that can perform design conversations on a specific construction project and convert those conversations into a list of requirements. Next, in an experiment with 56 participants, we compared the chatbot system to a regular online survey, focusing on user satisfaction and the quality and quantity of collected information. The results revealed that, with regard to user satisfaction, the participants preferred the chatbot experience to a regular survey. However, we found that chatbot conversations produced more data than the survey, with a similar rate of novel ideas but fewer themes. Our findings provide robust evidence that chatbots can be effectively used for design discussions in large-scale design projects and offer a user-friendly experience that can help to engage people in the design process. Based on this evidence, by providing a space for meaningful conversations between stakeholders and expanding the reach of design projects, the use of chatbot systems in interactive design systems can potentially improve design processes and their outcomes.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140151066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems 基于多方面维护本体的安全关键型系统智能维护优化框架

AI EDAM Pub Date : 2024-01-18 DOI: 10.1017/s0890060423000215

Xiaoxu Diao, Yunfei Zhao, Pavan K. Vaddi, Michael Pietrykowski, Marat Khafizov, Carol Smidts

{"title":"Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems","authors":"Xiaoxu Diao, Yunfei Zhao, Pavan K. Vaddi, Michael Pietrykowski, Marat Khafizov, Carol Smidts","doi":"10.1017/s0890060423000215","DOIUrl":"https://doi.org/10.1017/s0890060423000215","url":null,"abstract":"Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments 创客空间生产环境中最小智能增材制造设备的优化配置

AI EDAM Pub Date : 2024-01-17 DOI: 10.1017/s0890060423000239

James Gopsill, Mark Goudswaard, Chris Snider, Lorenzo Giunta, Ben Hicks

{"title":"Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments","authors":"James Gopsill, Mark Goudswaard, Chris Snider, Lorenzo Giunta, Ben Hicks","doi":"10.1017/s0890060423000239","DOIUrl":"https://doi.org/10.1017/s0890060423000239","url":null,"abstract":"Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms 利用图像特征和机器学习算法自动识别智能焊接中的焊点类型

AI EDAM Pub Date : 2024-01-02 DOI: 10.1017/s0890060423000227

Satish Sonwane, Shital Chiddarwar

{"title":"Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms","authors":"Satish Sonwane, Shital Chiddarwar","doi":"10.1017/s0890060423000227","DOIUrl":"https://doi.org/10.1017/s0890060423000227","url":null,"abstract":"Welding is the most basic and widely used manufacturing process. Intelligent robotic welding is an area that has received much consideration owing to the widespread use of robots in welding operations. With the dawn of Industry 4.0, machine learning is substantially developing to alleviate issues around applying robotic welding intelligently. Identifying the correct weld joint type is essential for intelligent robotic welding. It affects the quality of the weldment and impacts the per-unit cost. The robot controller must change different welding parameters per joint type to attain the desired weld quality. This article presents an approach that uses image features like edges, corners, and blobs to identify different weld joint types using machine learning algorithms. Feature extractors perform the task of feature extraction. The feature extractor choice is crucial for accurate weld joint identification. The present study compares the performance of five feature extractors, namely (1) Histogram of gradients, (2) Local binary pattern, (3) ReLU3 layer, (4) ReLU4 layer, and (5) Pooling layer of ResNet18 Neural network applied to classifiers like Support Vector machines, K-Nearest Neighbor and Decision trees. We trained and tested the proposed model using the Kaggle Weld joint dataset (for Butt and Fillet Joints) and our in-house dataset (for Vee, lap, and corner joints). The experimental findings show that out of the 15 models, the pre-trained ResNet18 feature extractor with an Support Vector Machines classifier has excellent performance with a threefold recognition accuracy of 98.74% for the mentioned dataset with a computation time of 31 ms per image.","PeriodicalId":501676,"journal":{"name":"AI EDAM","volume":"123 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139077374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0