## 6 Discussion
### Availability of the 3D Interpreted Model Network Construction Method  
It is very important for the further application to express 3 DIM into a computer recognizable and processable 3D interpreted model network. One possible application is to solve the problem of propagation reasoning and assistant decision-making of product technical information change. In the relatively simple product model as shown in Figure 11, there are 21 pieces of technical information, and the distribution of technical information in 3D environment is relatively clear. However, when faced with complex product models,with the increase of technical information, the product models presented to designers are complicated. When designers change a certain size or technical information of the product, the product model is too complicated to accurately and intuitively find out the relevant technical information that will be affected. However, when 3 DIM is represented as 3D interpreted model network, the product information that needs to be changed synchronously can be found by studying the corresponding information change propagation mechanism, and the corresponding design knowledge can be provided to support designers in making change decisions. For example, in Figure 14 (part of Figure 13), both Linear Size.1 and Linear Size.5 are associated with the same two surfaces. Therefore, when modifying Linear Size.1, we can find Linear Size.5 through the 3D interpreted model network according to the simple association mechanism, and push the Linear Size.5 and its corresponding design knowledge to the designers. Then the designers can confirm whether and how to change the value of Linear Size.5. On the other hand, in the process of the 3D interpreted model network generation, the key to ensure the accuracy of 3D interpreted model network is how to extract and organize the geometric information, technical information and the relationship between them. This paper proposes an algorithm to extract information from STEP file to solve this problem. Through this algorithm, the geometric information, technical information and the relationship between them are extracted and stored in CSV file, and finally visualized in Neo4. This paper verifies the effectiveness of the algorithm by comparing the type and number of entities used to describe the geometric and technical information of the product before and after the implementation of the algorithm. The entity before extraction refers to the entity in the STEP file, which can be parsed by STEP File Analyzer tool [29]. This tool is a public access tool provided by NIST, which has the functions of STEP file parsing, STEP file analysis, consistency check and other functions. The entity extracted after the extraction refers to the entity recorded in the CSV file. In this example, the STEP file of the product model is parsed in STEP File Analyzer and compared with the data recorded in the CSV file, as shown in Table 3.  
Through STEP File Analyzer, 85 entity types can be identified in STEP file. The number of TESSELLATED_ANNOTATION_OCCURRENCE, EDGE_CURVE, CLOSED_SHELL and ADVANCED_FACE is consistent with the number in CSV file, which indicates that the algorithm can extract such information accurately. At the same time, the STEP file also includes CARTESIAN_POINT, ORIENTED_EDGE, DIRECTIOND and other information with 81 types and 4315 entities. Although these entities reflect the geometric and topological relationship of product at some levels, it does not play a major role in the construction of 3D interpreted model network. Therefore, the proposed algorithm not only accurately extracts the relevant product information, but also effectively filters the redundant information in the STEP file.