## 2 Related Work
### Rule-Based Approach  
The main idea of the rule-based approach is to search for matched features according to rules that are predefined by experts. Due to its interpretability and reliability, a variety of rule-based approaches have been proposed and widely used. The work in [13] designed a feature recognition based on an expert system, which recognized features using the rule-defined boundary pattern. The authors in [5] proposed a graph-based feature recognition method. This method constructs the attribute adjacency graph (AAG), which represents the relationships and the attributes between faces in a CAD model, and then searches for matching sub-graphs in AAG to recognize features. Naturally, some researchers have proposed improved versions of AAG, such as extended attribute adjacency graph (EAAG) [11, 22], generalized attribute adjacency graph (GAAG) [23], and holistic attribute adjacency graph (HAAG) [7]. The graph-based feature recognition is mainly based on predefined sub-graph matching rules or heuristic sub-graph matching algorithms. Another rule-based approach is volume decomposition, which decomposes the removal volume of a CAD model into a series of intermediate volumes first, and then utilizes heuristic rules to match and reconstruct machining features according to these intermediates [1]. Based on the process of decomposing volumes, volume decomposition approaches can be categorized into convex-hull decomposition [24; 25] and cell-based decomposition [9; 10; 26; 27]. In addition, a hint-based approach [3; 4; 28] is also a promising rule-based feature recognition solution, which is a two-step approach for recognition. First, potential hints are extracted from the CAD model according to heuristic rules. Then, geometric reasoning and matching procedure are used to recognize the machining features from the extracted hints.  
All of the aforementioned approaches have their advantages and shortcomings. As a result, some researchers are attempting to combine different rule-based approaches into a hybrid system for overcoming the limitations of existing rule-based recognition approaches. For example, Ref. [11] proposed an automatic feature recognition approach combing graph-based and hint-based approaches.The work in [29] developed a similar graph-hint hybrid recognition system. The authors of [12] constructed a feature recognition solution based on convex-hull decomposition and graph-based approaches. The work in [30] presented a hybrid machining feature recognizer combining the advantages of graph-based and hint-based approaches.  
In general, despite the fact that rule-based recognition studies have been ongoing for nearly 40 years, there are still many issues to be solved in terms of their application scope and universality: (1) Rule-based approaches require a large number of human-defined rules, which are time-consuming and inflexible [19]. (2) When faced with undefined rules, rule-based approaches will fail to recognize features, indicating a lack of robustness and generalizability [31]. (3) Existing rule-based approaches have difficulty recognizing intersecting and incomplete features, as the geometry and topology of the features are destroyed or changed [32].