Article
Acoustics
Lili Li, Kun Chen, Jianmin Gao, Zhiyong Gao, Junkong Liu
Summary: This paper introduces the first-order bending dispersion, first-order torque dispersion, and gravitational moment difference of rotor blades as selection criteria, and proposes an intelligent selection algorithm and improved simulated annealing algorithm to achieve efficient utilization and assembly of rotating blades.
SHOCK AND VIBRATION
(2021)
Article
Engineering, Industrial
Wenbo Wu, Zhengdong Huang, Jiani Zeng, Kuan Fan
Summary: This paper proposes a deep reinforcement learning approach to solve the assembly sequence planning (ASP) problem, aiming to improve response speed by leveraging the reusability and expandability of past decision-making experiences. Through steps like instance generation algorithm, mask algorithm, and Monte Carlo sampling method, the assembly cost is minimized effectively under precedence constraints. It is demonstrated that the method accurately and efficiently solves the ASP problem in an environment with dynamic resource changes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Robotics
Haobo Luo, Tin Lun Lam
Summary: This article focuses on the connection planning of self-reconfiguration based on multiple in-degree single out-degree modules. An auto-optimizing connection planning method is proposed, which combines rapidity and optimality using configuration pointers. The polynomial-time algorithm reduces reconfiguration steps by leveraging the interchangeability of connection points, while the exponential-time algorithm further optimizes the solutions to the optimum through a new branching strategy and stage cost.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Automation & Control Systems
Ye Bai, Sheng-Jen Hsieh
Summary: Programming by demonstration (PBD) is used in industry for human-robot collaboration in assembly tasks, but its application in commercial electronic product assembly is limited due to a lack of trajectory planning optimization. This research proposes a framework with custom algorithms to preprocess and classify contactless demonstration performance, enabling the generation of optimal motion paths based on distance, smoothness, and trajectory variance criteria. Machine learning methods, such as CNN, ANN, and SVM, achieve an accuracy range of 80% to 85% in predicting the best motion path, with CNN (specifically DarkNet) achieving the highest accuracy. Future work involves developing hybrid CNN/ANN algorithms for higher prediction accuracy and applying the proposed algorithms to robots with dual assembly arms and complex assemblies resembling human arms.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Industrial
Ning Huang, Qiang Du, Libiao Bai, Qian Chen
Summary: This study proposes a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective. The research builds a mathematical model by analyzing the relationship between construction enterprises and incorporates the decision-makers' psychological preferences to determine the optimal resource input in different situations.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Kristine Wilhelm Lund, Mikkel Liep Nielsen, Erik Skov Madsen
Summary: Decommissioned wind turbine blades made from difficult-to-recycle composite material present a significant problem, necessitating the establishment of sustainable end-of-life value chains. This study proposes a three-step framework, 3-SuDeM, to assess and improve technologies through sustainable decision making. The framework was applied and validated in cooperation with a Danish waste management company, evaluating four cutting and sectioning technologies for wind turbine blades. The results concluded that the framework adds value to practical selection and contributes to the literature on sustainable value chains and multi criteria decision making.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Automation & Control Systems
Sheng-Wen Zhang, Zhan Wang, De-Jun Cheng, Xi-Feng Fang
Summary: This paper proposes an intelligent decision-making system for the assembly process planning of complex products based on machine learning. The system analyzes the characteristics and variations of the assembly process and establishes a hierarchical model. A machine learning model is constructed to optimize the decision-making process, improving efficiency and accuracy. The system is validated using a case study and its machine learning performance and industrial applicability are discussed.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Review
Engineering, Multidisciplinary
Buxin Zhang, Shujing Wu, Dazhong Wang, Shanglei Yang, Feng Jiang, Changhe Li
Summary: With the continuous improvement of thrust-to-weight ratio and endurance in advanced aero-engines, higher requirements are being imposed on blade surface quality. Robot abrasive belt grinding offers excellent flexibility, convenient scheduling, strong adaptability, and low cost advantages. However, factors such as low repeated positioning accuracy, weak structural stiffness, and elastic deformation during belt grinding significantly influence the surface quality and contour accuracy of robot belt grinding.
Article
Engineering, Marine
Lei Li, Qinghui Chen, Honggen Zhou, Chunjin Li, Qiang He
Summary: The assembly of ship blocks is a crucial stage in shipbuilding, however, manual assembly efficiency is low and collision is common. Therefore, there is a need to research automated docking of ship blocks. This study proposes a high-precision matching method for measuring point sets to estimate the attitude of the ship block. A seventh-degree polynomial is used for trajectory translation, while a nonlinear weighted improved particle swarm optimization method optimizes time, energy consumption, and shock degree in the trajectory planning process. Simulation analysis confirms the accuracy of the matching optimization and demonstrates the effectiveness of the proposed methods.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT
(2023)
Article
Automation & Control Systems
Bo Gao, Shichao Zhang, Hao Sun, Chengwu Ma
Summary: This study proposes an assembly sequence planning method based on adaptive gravitational search algorithm, which takes into account factors such as geometric feasibility and priority constraints. The method utilizes dynamic adjustment coefficients and new exchange rules to achieve fast convergence of the optimal assembly sequence. Experimental results demonstrate the superiority of this method.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Engineering, Manufacturing
Shuai Tao, Duan-Yan Wang, Sheng-Wen Zhang
Summary: This paper studies the assembly path planning technology of complex products based on geometric features and assembly sequence information. By converting the assembly problem into a disassembly problem, the complexity of assembly path planning is reduced. The optimized Rapidly-exploring Random Trees (RRT) algorithm quickly generates the optimal assembly path.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
(2023)
Article
Engineering, Industrial
Kai Guo, Rui Liu, Guijiang Duan, Jiajun Liu, Pengyong Cao
Summary: Assembly sequence planning is a critical challenge in the manufacturing industry, and this paper proposes a deep reinforcement learning approach to address this problem. The proposed method learns optimal assembly strategies through interactions with the environment, and experimental results show its effectiveness and advantages.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Weibo Ren, Xiaonan Yang, Yan Yan, Yaoguang Hu
Summary: This paper proposes a decision-making framework for the task planning problem in human-robot collaborative assembly systems. It considers task decomposition, resource evaluation, operation scheduling, and collaboration between humans and robots. A joint optimization model is developed to minimize competition time and production costs while improving the automation degree of the hybrid system. Computational results based on industrial cases demonstrate the performance and feasibility of the proposed methodology.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2023)
Article
Computer Science, Artificial Intelligence
Rui Xu, Yuting Zhao, Zhaoyu Li, Shengying Zhu, Zixuan Liang, Yue Gao
Summary: This paper proposes a hierarchical multi-agent planning method based on two-stage two-sided matching (HMAP-TTM) to solve the problem of flexible assembly of large-scale lunar facilities by lunar robots. The simulation results show that the HMAP-TTM can generate plans with shorter mission time and require smaller communication costs than the baseline methods.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Civil
Stephen Muzira, Wenxin Qiao
Summary: Investment in road infrastructure is crucial for the development of any country. When deciding which roads to pave, a holistic approach that considers both economic benefits and hard-to-quantify factors is needed.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Computer Science, Artificial Intelligence
Fangyu Chen, Yongchang Wei, Hongchang Ji, Gangyan Xu
Summary: This paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management and validates its effectiveness through a case study. The research findings suggest that key standards often encompass a wider array of risks, providing suggestions for revising construction standards.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Minghao Li, Qiubing Ren, Mingchao Li, Ting Kong, Heng Li, Huijing Tian, Shiyuan Liu
Summary: This study proposes a method using digital twin technology to construct a collision early warning system for marine piling. The system utilizes a five-dimensional model and four independently maintainable development modules to maximize its effectiveness. The pile positioning algorithm and collision early warning algorithm are capable of providing warnings for complex pile groups.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Seokhyun Ryu, Sungjoo Lee
Summary: This study proposes the use of patent information to develop a robust technology tree and applies it to the furniture manufacturing process. Through methods such as clustering analysis, semantic analysis, and association-rule mining, technological attributes and their relationships are extracted and analyzed. This approach provides meaningful information to improve the understanding of a target technology and supports research and development planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Shuai Ma, Kechen Song, Menghui Niu, Hongkun Tian, Yanyan Wang, Yunhui Yan
Summary: This paper proposes a feature-based domain disentanglement and randomization (FDDR) framework to improve the generalization of deep models in unseen datasets. The framework successfully addresses the appearance difference issue between training and test images by decomposing the defect image into domain-invariant structural features and domain-specific style features. It also utilizes randomly generated samples for training to further expand the training sample.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Fang Xu, Tianyu Zhou, Hengxu You, Jing Du
Summary: This study explores the impact of AR-based egocentric perspectives on indoor wayfinding performance. The results reveal that participants using the egocentric perspective demonstrate improved efficiency, reduced cognitive load, and enhanced spatial awareness in indoor navigation tasks.
ADVANCED ENGINEERING INFORMATICS
(2024)
Review
Computer Science, Artificial Intelligence
Yujie Lu, Shuo Wang, Sensen Fan, Jiahui Lu, Peixian Li, Pingbo Tang
Summary: Image-based 3D reconstruction plays a crucial role in civil engineering by bridging the gap between physical objects and as-built models. This study provides a comprehensive summary of the field over the past decade, highlighting its interdisciplinary nature and integration of various technologies such as photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. The proposed 3D reconstruction knowledge framework outlines the essential elements, use phases, and reconstruction scales, and identifies eight future research directions. This review is valuable for scholars interested in the current state and future trends of image-based 3D reconstruction in civil engineering, particularly in relation to deep learning methods.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang
Summary: This paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework enhances the effectiveness of intersecting machining feature segmentation by leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning. Experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Chao Zhao, Weiming Shen
Summary: This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ching-Chih Chang, Teng-Wen Chang, Hsin-Yi Huang, Shih-Ting Tsai
Summary: Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shusheng Zhang, Hang Zhang, Yajun Zhang, Jiachen Liang, Rui Huang, Bo Huang
Summary: This research proposes a novel approach for machining feature process planning using graph convolutional neural networks. By representing part information with attribute graphs and constructing a learning model, the proposed method achieves higher accuracy and resolves current limitations in machining feature process planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hong-Wei Xu, Wei Qin, Jin-Hua Hu, Yan-Ning Sun, You -Long Lv, Jie Zhang
Summary: Wafer fabrication is a complex manufacturing system, where understanding the correlation between parameters is crucial for identifying the cause of wafer defects. This study proposes a Copula network deconvolution-based framework for separating direct correlations, which involves constructing a complex network correlation diagram and designing a nonlinear correlation metric model. The proposed method enables explainable fault detection by identifying direct correlations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yida Hong, Wenqiang Li, Chuanxiao Li, Hai Xiang, Sitong Ling
Summary: An adaptive push method based on feature transfer is proposed to address sparsity and cold start issues in product intelligent design. By constructing a collaborative filtering algorithm model and transforming the rating model, the method successfully alleviates data sparsity and cold start problems.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hairui Fang, Jialin An, Bo Sun, Dongsheng Chen, Jingyu Bai, Han Liu, Jiawei Xiang, Wenjie Bai, Dong Wang, Siyuan Fan, Chuanfei Hu, Fir Dunkin, Yingjie Wu
Summary: This work proposes a model for real-time fault diagnosis and distance localization on edge computing devices, achieving lightweight design and high accuracy in complex environments. It also demonstrates a high frame rate on edge computing devices, providing a novel solution for industrial practice.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yujun Jiao, Xukai Zhai, Luyajing Peng, Junkai Liu, Yang Liang, Zhishuai Yin
Summary: This paper proposes a digital twin-based motion forecasting framework that predicts the future trajectories of workers on construction sites, accurately predicting workers' motions in potential risk scenarios.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ling-Zhe Zhang, Xiang-Dong Huang, Yan-Kai Wang, Jia-Lin Qiao, Shao-Xu Song, Jian-Min Wang
Summary: Time-series DBMSs based on the LSM-tree have been widely applied in various scenarios. The characteristics of time-series data workload pose challenges to efficient queries. To address issues like query latency and inaccurate range, we propose a novel compaction algorithm called Time-Tiered Compaction.
ADVANCED ENGINEERING INFORMATICS
(2024)