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][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][5,6].

Assembly knowledge is constantly evolving in practice, providing a key reference for the design and manufacturing of complex products ^[7][8][9][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][14]. However, multimodal knowledge graphs present a big step forward in graph construction ^[13][15]. The multimodal data are not only limited to text and images, but 3D part models ^[14][13] 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][16] 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][17] 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][18] 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][19] 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][20]. 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][21], Anthony ^[21][22] and Kopena et al. ^[22][23] 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][24], Chen et al. ^[24][25] 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][26]. In terms of product process assembly information ^[26][27], Duan et al. ^[27][28] 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][12] 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.