Article
Engineering, Marine
Bo Liu, Rui Li, Ji Wang, Yujun Liu, Sheng Li
Summary: An automated subassembly partition method is proposed in this study, which defines an assembly information model, establishes an optimization model, and employs a two-dimensional coding discrete particle swarm optimization (PSO) algorithm. The results demonstrate the feasibility and applicability of the proposed method in improving the efficiency and quality of the assembly work.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Z. H. Che, Tzu-An Chiang, Tzu-Ting Lin
Summary: This study focuses on the supplier selection problem in multiple assembly plants producing multiple products, using a multi-objective algorithm to find optimal supplier combinations and production resource allocation. The new algorithm W-NSGA2 incorporates task time and quantity mechanisms in the initial solution generation, improving efficiency and outperforming existing algorithms in office furniture assembly tasks.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Chemical
M. V. A. Raju Bahubalendruni, Bhavasagar Putta
Summary: The recent advances in Industry 4.0 have led to the adoption of augmented reality (AR), virtual reality (VR), and mixed reality (MR) in manufacturing industries for visualization and training purposes. This research presents a novel method for automated assembly task simulation that improves geometric feasibility testing. The proposed framework uses textual instructions to generate a virtual assembly task plan and ensures collision-free operations. The framework is implemented and validated for various products to ensure its correctness and completeness.
Article
Engineering, Industrial
Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Chi-Hsin Wang
Summary: The study proposes a Cyber-Physical Assembly System (CPAS)-based metaheuristic for fully-automated assembly sequence planning, considering the physical characteristics of robotic arms. Illustrative case examples demonstrate the effectiveness of the method compared to traditional approaches, showing that single-arms robots can effectively execute assembly plans.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Plant Sciences
Yunzhi Lin, Chen Ye, Xingzhu Li, Qinyao Chen, Ying Wu, Feng Zhang, Rui Pan, Sijia Zhang, Shuxia Chen, Xu Wang, Shuo Cao, Yingzhen Wang, Yi Yue, Yongsheng Liu, Junyang Yue
Summary: A high-quality genome is important for studying functional, evolutionary, and comparative genomics. However, there is a lack of bioinformatic tools for automatically constructing and characterizing T2T genomes. The quarTeT toolkit addresses this issue by providing user-friendly modules for genome assembly and characterization.
HORTICULTURE RESEARCH
(2023)
Article
Computer Science, Software Engineering
Ziqi Wang, Florian Kennel-Maushart, Yijiang Huang, Bernhard Thomaszewski, Stelian Coros
Summary: This article presents a computational framework for planning the assembly sequence of custom frame structures. The authors propose an optimization-based approach that models sequence planning using topology optimization problems. They demonstrate improved performance and computational time over greedy search algorithms, and show that their algorithm can handle assembly with static or dynamic supports.
ACM TRANSACTIONS ON GRAPHICS
(2023)
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
Automation & Control Systems
Miguel Neves, Pedro Neto
Summary: This paper proposes an approach to using deep reinforcement learning (DRL) in assembly sequence planning (ASP). The approach introduces parametric actions and two different reward signals, and compares the performance of different deep RL algorithms in different scenarios, while also comparing them to tabular Q-Learning. The results demonstrate the potential of deep reinforcement learning in assembly sequence planning problems with human interaction.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Automation & Control Systems
Xiaojun Zhu, Zhigang Xu, Junyi Wang, Xiao Yang, Linlin Fan
Summary: This paper introduces the priority graph model, develops a subassembly recognition method, and designs a selection algorithm with feedback weights for assembly line design in topological sequencing. The method is shown to quickly plan a satisfactory sequence compared to heuristic algorithms. Different satisfactory assembly sequences with different adaptability can be planned based on the weights of assembly line designers. This work has implications for the co-design of assembly sequence planning and assembly line design.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Industrial
Zi-Yue Wang, Cong Lu
Summary: This paper proposes an integrated approach combining job shop scheduling and assembly sequence planning to optimize part processing and assembly sequences for discrete manufacturing. Using a non-dominated sorting genetic algorithm-II, the method aims to minimize total production completion time and part inventory time, showing improved production efficiency and cost-saving through case studies and comparison tests.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Jiazhen Pang, S. K. Ong, A. Y. C. Nee
Summary: This study proposes a stage identification method based on on-site assembly sequence and stage model sequence, which can effectively improve the accuracy and efficiency of assembly stage identification by using time and assembly progress logic.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Automation & Control Systems
Xiaolin Shi, Xitian Tian, Jianguo Gu, Gangfeng Wang, Dongping Zhao, Liping Ma
Summary: This paper introduces a combination of case-based reasoning (CBR) method and ontology theory for decision making in assembly sequence planning (ASP). The CBR approach allows for a unified representation of previous and target cases by utilizing the ontology that unifies different sources of assembly sequence-related knowledge. The similarity between the target ASP case and previous ASP cases is calculated based on the similarity measure of classes and properties in ontology theory, considering various factors such as connection type, motion-transmission type, and location-support type. The combination of ontology and CBR enables flexible and high-quality assembly sequence decisions, and a rule-based reasoning (RBR) method based on ontology is also used as a supplement to CBR. The effectiveness of the proposed method is validated through a reducer case.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Marcin Suszynski, Katarzyna Peta
Summary: The proposed model uses neural networks to predict the optimal assembly time of mechanical parts for planning assembly sequences. By testing various training methods and activation functions, the most effective network was determined.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Shipei Li, Dunbing Tang, Deyi Xue, Qi Wang, Haihua Zhu
Summary: This study proposes a new approach for assembly sequence planning (ASP) based on structure cells (SCs) provided by co-designers in open design (OD). By describing the assembly features and matching rules of SCs, a method for calculating the assembly relationships between SCs is developed. The optimal assembly sequence is achieved using a particle swarm optimization algorithm.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Automation & Control Systems
Anil Kumar Gulivindala, M. V. A. Raju Bahubalendruni, Anil Kumar Inkulu, S. S. Vara Prasad Varupala, K. SankaranarayanaSamy
Summary: This paper compares different subassembly identification methods and proposes an improved method for solving problems in assembly sequence planning. The cut-set method is considered suitable for enhancing workability, while predicate consideration is identified as a key factor influencing solution generation.
ASSEMBLY AUTOMATION
(2021)