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Liu, M.; Zhou, B.; Li, J.; Li, X.; Bao, J. Assembly Sequence Planning for Wind Turbines. Encyclopedia. Available online: https://encyclopedia.pub/entry/50559 (accessed on 07 July 2024).
Liu M, Zhou B, Li J, Li X, Bao J. Assembly Sequence Planning for Wind Turbines. Encyclopedia. Available at: https://encyclopedia.pub/entry/50559. Accessed July 07, 2024.
Liu, Mingfei, Bin Zhou, Jie Li, Xinyu Li, Jinsong Bao. "Assembly Sequence Planning for Wind Turbines" Encyclopedia, https://encyclopedia.pub/entry/50559 (accessed July 07, 2024).
Liu, M., Zhou, B., Li, J., Li, X., & Bao, J. (2023, October 19). Assembly Sequence Planning for Wind Turbines. In Encyclopedia. https://encyclopedia.pub/entry/50559
Liu, Mingfei, et al. "Assembly Sequence Planning for Wind Turbines." Encyclopedia. Web. 19 October, 2023.
Assembly Sequence Planning for Wind Turbines
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This entry is adapted from the peer-reviewed paper 10.3390/machines11100930

There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence.

wind turbine assembly process knowledge assembly sequence recommendation

1. Introduction

With the rapid development of intelligent advanced technologies such as big data and artificial intelligence, traditional manufacturing is gradually transforming into intelligent manufacturing [1][2]. The concept of Industry 4.0 provides an opportunity for manufacturing enterprises to integrate data at all stages of the product life cycle, so as to better meet the needs of users’ personalized and customized products [3]. As a customized electromechanical product, the manufacturing process of large wind turbines can be divided into three major parts, including manufacturing process, logistics process, and on-site process. Among them, assembly as the core link of the on-site process is the last step of wind turbine installation. The assembly quality directly affects the reliability and working performance of the wind turbine. In addition, the assembly workload of each component has always occupied a high ratio of the development workload of the whole product. The assembly time accounts for more than 40% of the manufacturing time of the whole wind turbine [4]. The development of artificial intelligence provides opportunities for assembly semantic processing and assembly sequence recommendations, which focus on analyzing and planning assembly sequences more effectively [5][6].
Assembly knowledge is constantly evolving in practice, providing a key reference for the design and manufacturing of complex products [7][8][9]. Manufacturing products are constantly iterated and upgraded. Thus, a large amount of historical data is generated. However, the lack of effective organization of these heterogeneous data makes it difficult to provide technicians with convenient knowledge services. Therefore, the search for efficient data organization and convenient knowledge acquisition methods has become an urgent problem for a long time. In addition, in traditional assembly sequence planning, the selection of assembly solutions based on different assembly requirements is often realized semi-automatically by assembly personnel based on manual experience or computer-aided tools. How to obtain the assembly object, assembly process, and other related information based on the existing assembly knowledge with a sequence planning solution is also a major difficulty in current intelligent planning.

2. Knowledge Graph Construction in Assembly

Most of the knowledge graphs in the assembly use a top-down approach to build ontologies. Chen et al. [10] proposed a semantic recognition method for the assembly process based on a Long Short-Term Memory (LSTM) network to automatically recognize the assembly semantics in the assembly process document to construct an assembly process knowledge graph. Shi et al. [11] proposed knowledge reuse of assembly resources in a complex product assembly process based on knowledge graphs. In multi-source heterogeneous data, it is becoming more common to use text as a data source to construct knowledge graphs [12].
However, multimodal knowledge graphs present a big step forward in graph construction [13]. The multimodal data are not only limited to text and images, but 3D part models [14] are also an important source of information in the assembly. More types of data have been considered in manufacturing to build multimodal knowledge graphs. Yang et al. [15] proposed a knowledge-based multimodal knowledge extraction method for visual question and answer, which associates visual objects and factual answers with implicit relations. Zhou et al. [16] proposed an end-to-end tabular and textual information extraction model. The causality event evolutionary knowledge graph of tabular and text is realized. Liu et al. [17] designed a corresponding ontology design and joint knowledge extraction model to realize the end-to-end automatic construction of knowledge graphs. The experiment confirmed the advantages of this model in the field of aerospace assembly. Wen et al. [18] established an interpretable multimodal knowledge graph answer prediction model and effectively extracted text information from images through a multimodal fine-grained entity extraction method, which achieved a better performance in multimodal link prediction.
Inspired by the previous studies related to knowledge graph construction in assembly, it provides a viable solution that multimodal knowledge graphs can integrate and utilize the multimodal data (text in process manual, image of tooling, and 3D model) of assembly for wind turbines. In this way, the effective organization of these heterogeneous data makes it possible to provide technicians with convenient knowledge services. Thus, it is considered that link knowledge from the assembly process text, on-site installation images, and 3D models can be used in a unified way to construct a knowledge graph for wind turbine assembly.

3. Assembly Sequence Planning

To shorten the assembly sequence planning time and reduce the assembly difficulty, machine learning and deep learning are used to predict the optimal assembly sequence [19]. Therefore, this section discusses the assembly sequence information model and optimization algorithm.
(1)
Assembly sequence information model
Currently, scholars propose a variety of assembly information modeling approaches. In terms of product functional assembly information modeling [20], Anthony [21] and Kopena et al. [22] adopted the SBF (structure-behavior-function) model to develop a conceptual understanding and prototyping environment to capture the assembly functional information of CAD (computer aided design) artifacts. However, the SBF model is relatively simple in describing the relationships between upper-level products and lower-level components. In the research based on product structural assembly information [23], Chen et al. [24] proposed a structural assembly design model to support a top-down product design. It utilizes a multilevel assembly model to capture the product information, which fills the gap of a hierarchical feature information model [25]. In terms of product process assembly information [26], Duan et al. [27] presented a pooling framework for RPA (relative position accuracy) measurements based on MBD (model based definition) datasets, which meets the needs of integration and efficiency in large component assembly.
In addition, some researchers have combined the new generation of information technology to develop assembly sequence information descriptions. Zhou et al. [28] utilized a graph neural network to propose knowledge graph-driven assembly process generation and evaluation for complex components. Xu et al. [29] proposed a top-N recommendation method named the collaborative knowledge-aware graph attention network (CKGAT) to accurately capture users’ potential interests.
(2)
Assembly optimization algorithm
In recent years, there have been various assembly sequence optimization algorithms proposed. To improve assembly accuracy and service safety, Champatiray et al. [30] proposed modified cat swarm optimization for optimal assembly sequence planning problems. Shen et al. [31] proposed intelligent material distribution and optimization in the assembly process of large offshore crane lifting equipment. On the other hand, Han et al. [32] presented a new multidimensional-based clustering and retrieval method for CAD assembly models, which comprehensively evaluates the similarity between assembly models by considering both part information and assembly relationship information. In addition, Cong et al. [33] introduced a simulated annealing program module into the existing genetic algorithm to form a new simulated annealing genetic algorithm, which searches for the globally optimal assembly sequence scheme more accurately and efficiently. Also, Xie et al. [34] designed an improved multi-pheromone ant colony optimization algorithm, which provides a new strategy for improving operator matching. Li et al. [35] developed an assembly sequence planning method based on the ACO (ant colony optimization) algorithm, which obtains the optimal assembly sequence by assisting the search for the optimal solution of the ACO path. In addition, some scholars have presented new insights in optimization algorithms. Chaudhari et al. [36] compared the NSGA-III (non-dominated sorting genetic algorithm III) with NSGA-II (non-dominated sorting genetic algorithm II) for the multi-objective optimization of an adiabatic styrene reactor. Ehsaeyan et al. [37] introduced a novel meta-heuristic algorithm called the fireworks optimization algorithm (FOA) with few control parameters for discrete and continuous optimization problems. Fountas et al. [38] proposed a virus-evolutionary genetic algorithm for the optimizing of selective laser sintering/melting operations.
Based on the above study, it is clear that if we obtain a better assembly sequence, it is not only necessary to model and associate the knowledge of multiple sources for wind turbine assembly, but also to design an optimization algorithm. Therefore, it is promising and valuable to explore a knowledge graph-driven approach for assembly sequence recommendations and reasoning for wind turbine assembly.

References

  1. Zheng, P.; Chen, C.-H.; Shang, S. Towards an Automatic Engineering Change Management in Smart Product-Service Systems—A DSM-Based Learning Approach. Adv. Eng. Inform. 2019, 39, 203–213.
  2. Lu, Y.; Xu, X.; Wang, L. Smart Manufacturing Process and System Automation—A Critical Review of the Standards and Envisioned Scenarios. J. Manuf. Syst. 2020, 56, 312–325.
  3. Liu, J.; Sun, Q.; Cheng, H.; Liu, X.; Ding, X.; Liu, S.; Xiong, H. The State-of-the-Art, Connotation and Developing Trends of the Products Assembly Technology. J. Mech. Eng. 2018, 54, 2–28.
  4. Ji, Z. Intelligent Manufacturing—Main Direction of “Made in China 2025”. China Mech. Eng. 2015, 26, 2273.
  5. Kumar, G.A.; Bahubalendruni, M.V.A.R.; Vara Prasad, V.S.S.; Ashok, D.; Sankaranarayanasamy, K. A Novel Geometric Feasibility Method to Perform Assembly Sequence Planning through Oblique Orientations. Eng. Sci. Technol. Int. J. 2022, 26, 100994.
  6. Tao, C.; Chunhui, L.; Hui, X.; Zhiheng, Z.; Guangyue, W. A Review of Digital Twin Intelligent Assembly Technology and Application for Complex Mechanical Products. Int. J. Adv. Manuf. Technol. 2023, 127, 4013–4033.
  7. Mahajan, R.; Sankman, B. 3D Packaging Architectures and Assembly Process Design. In 3D Microelectronic Packaging: From Fundamentals to Applications; Li, Y., Goyal, D., Eds.; Springer Series in Advanced Microelectronics; Springer International Publishing: Cham, Switzerland, 2017; pp. 17–46. ISBN 978-3-319-44586-1.
  8. Yi, Y.; Yan, Y.; Liu, X.; Ni, Z.; Feng, J.; Liu, J. Digital Twin-Based Smart Assembly Process Design and Application Framework for Complex Products and Its Case Study. J. Manuf. Syst. 2021, 58, 94–107.
  9. Eschen, H.; Kötter, T.; Rodeck, R.; Harnisch, M.; Schüppstuhl, T. Augmented and Virtual Reality for Inspection and Maintenance Processes in the Aviation Industry. Procedia Manuf. 2018, 19, 156–163.
  10. Chen, Z.; Bao, J.; Zheng, X.; Liu, T. Assembly Information Model Based on Knowledge Graph. J. Shanghai Jiaotong Univ. 2020, 25, 578–588.
  11. Shi, X.; Tian, X.; Gu, J.; Yang, F.; Ma, L.; Chen, Y.; Su, T. Knowledge Graph-Based Assembly Resource Knowledge Reuse towards Complex Product Assembly Process. Sustainability 2022, 14, 15541.
  12. Wang, M.; Wang, H.; Li, B.; Zhao, X.; Wang, X. Survey on Key Technologies of New Generation Knowledge Graph. J. Comput. Res. Dev. 2022, 59, 1947–1965.
  13. Cheng, B.; Zhu, J.; Guo, M. MultiJAF: Multi-Modal Joint Entity Alignment Framework for Multi-Modal Knowledge Graph. Neurocomputing 2022, 500, 581–591.
  14. Li, X.; Zhang, S.; Huang, R.; Huang, B.; Xu, C.; Kuang, B. Structured Modeling of Heterogeneous CAM Model Based on Process Knowledge Graph. Int. J. Adv. Manuf. Technol. 2018, 96, 4173–4193.
  15. Ding, Y.; Yu, J.; Liu, B.; Hu, Y.; Cui, M.; Wu, Q. MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-Based Visual Question Answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 19–20 June 2022; pp. 5089–5098.
  16. Zhou, B.; Hua, B.; Gu, X.; Lu, Y.; Peng, T.; Zheng, Y.; Shen, X.; Bao, J. An End-to-End Tabular Information-Oriented Causality Event Evolutionary Knowledge Graph for Manufacturing Documents. Adv. Eng. Inform. 2021, 50, 101441.
  17. Liu, P.; Qian, L.; Zhao, X.; Tao, B. The Construction of Knowledge Graphs in the Aviation Assembly Domain Based on a Joint Knowledge Extraction Model. IEEE Access 2023, 11, 26483–26495.
  18. Wen, Y.; Luo, B.; Zhao, Y. IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling. arXiv 2023, 1–12.
  19. Bahubalendruni, M.V.A.R.; Gulivindala, A.K.; Varupala, S.S.V.P.; Palavalasa, D.K. Optimal Assembly Sequence Generation through Computational Approach. Sādhanā 2019, 44, 174.
  20. Bortolini, M.; Ferrari, E.; Gamberi, M.; Pilati, F.; Faccio, M. Assembly System Design in the Industry 4.0 Era: A General Framework. IFAC-PapersOnLine 2017, 50, 5700–5705.
  21. Anthony, L.; Regli, W.C.; John, J.E.; Lombeyda, S.V. An Approach to Capturing Structure, Behavior, and Function of Artifacts in Computer-Aided Design. J. Comput. Inf. Sci. Eng. 2001, 1, 186–192.
  22. Kopena, J.B.; Regli, W. Functional Modeling of Engineering Designs for the Semantic Web. IEEE Data Eng. Bull. 2003, 26, 55–61.
  23. Wang, X.; Ong, S.K.; Nee, A.Y.C. A Comprehensive Survey of Augmented Reality Assembly Research. Adv. Manuf. 2016, 4, 1–22.
  24. Chen, X.; Gao, S.; Yang, Y.; Zhang, S. Multi-Level Assembly Model for Top-down Design of Mechanical Products. Comput.-Aided Des. 2012, 44, 1033–1048.
  25. Zhou, W.; Zheng, J.; Wang, J. Nested Partitions Method for Assembly Sequences Merging. Expert. Syst. Appl. 2011, 38, 9918–9923.
  26. Gao, S.; Jin, R.; Lu, W. Design for Manufacture and Assembly in Construction: A Review. Build. Res. Inf. 2020, 48, 538–550.
  27. Duan, G.; Shen, Z.; Liu, R. An MBD Based Framework for Relative Position Accuracy Measurement in Digital Assembly of Large-Scale Component. Assem. Autom. 2019, 39, 685–695.
  28. Zhou, B.; Bao, J.; Chen, Z.; Liu, Y. KGAssembly: Knowledge Graph-Driven Assembly Process Generation and Evaluation for Complex Components. Int. J. Comput. Integr. Manuf. 2022, 35, 1151–1171.
  29. Xu, Z.; Liu, H.; Li, J.; Zhang, Q.; Tang, Y. CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation. Appl. Sci. 2022, 12, 1669.
  30. Champatiray, C.; Samal, S.; Bahubalendruni, M.V.A.R.; Mahapatra, R.N.; Mishra, D.; Balabantaray, B.K. Modified Cat Swarm Optimization for Optimal Assembly Sequence Planning Problems. Int. J. Perform. Eng. 2022, 18, 289.
  31. Shen, X.; Liu, S.; Zhang, C.; Bao, J. Intelligent Material Distribution and Optimization in the Assembly Process of Large Offshore Crane Lifting Equipment. Comput. Ind. Eng. 2021, 159, 107496.
  32. Han, Z.; Mo, R.; Hao, L. Clustering and Retrieval of Mechanical CAD Assembly Models Based on Multi-Source Attributes Information. Robot. Comput.-Integr. Manuf. 2019, 58, 220–229.
  33. Liu, C.; Zhang, F.; Zhang, H.; Shi, Z.; Zhu, H. Optimization of Assembly Sequence of Building Components Based on Simulated Annealing Genetic Algorithm. Alex. Eng. J. 2023, 62, 257–268.
  34. Xie, Z.; Du, J.; Chen, Q.; Wang, X. Enhancing the Labor Division in the Balancing of Apparel Assembly Lines with Parallel Workstation through an Improved Ant Colony Algorithm. J. Eng. Fibers Fabr. 2021, 16, 15589250211055784.
  35. Li, F.; Yang, C.; Shao, J. Research on Ant Colony Algorithm for Wing Assembly Sequence Planning. In Proceedings of the 2021 2nd International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), Shenyang, China, 17–19 November 2021; pp. 175–178.
  36. Chaudhari, P.; Thakur, A.K.; Kumar, R.; Banerjee, N.; Kumar, A. Comparison of NSGA-III with NSGA-II for Multi Objective Optimization of Adiabatic Styrene Reactor. Mater. Today Proc. 2022, 57, 1509–1514.
  37. Ehsaeyan, E.; Zolghadrasli, A. FOA: Fireworks Optimization Algorithm. Multimed. Tools Appl. 2022, 81, 33151–33170.
  38. Fountas, N.A.; Kechagias, J.D.; Vaxevanidis, N.M. Optimization of Selective Laser Sintering/Melting Operations by Using a Virus-Evolutionary Genetic Algorithm. Machines 2023, 11, 95.
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Subjects: Engineering, Manufacturing
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mingfei Liu
,
Bin Zhou
,
Jie Li
,
Xinyu Li
,
Jinsong Bao
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Revisions: 2 times (View History)
Update Date: 20 Oct 2023
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