## 1 Introduction  
In the vision of Industry 4.0, the manufacturing industry is undergoing an unprecedented revolution. Traditional patterns are progressively turning to the new paradigm of intelligent automation. As a connection between computer aided design (CAD) and computer aided manufacturing (CAM), computer aided process planning (CAPP) plays a critical role in the future of intelligent manufacturing and is expected to be capable of self-perception, self-decision, and self-execution. Self-perception, which is a prerequisite for subsequent decision-making, has the ability to extract high-level machining semantics (e.g., chamfer, slot, step) from low-level geometry and topology representations (e.g., vertex, edge, face) in CAD models. This is also known as automatic feature recognition.  
Feature recognition techniques have been developed for nearly four decades. During this time, numerous impressive methods with engineering value have been proposed [1]. Currently, there are two types of automatic feature recognition approaches: rule-based and learning-based. In the last 40 years, rule-based approaches [2], such as hint-based [3,4], graph-based [5, 6, 7], volume decomposition [8, 9, 10], hybrid-based [11, 12], expert systems [13], and others have been thoroughly investigated. Although the programming implementation and application of the rule-based approach are well-developed, there are still many issues to be resolved in terms of generalization and automation [1, 14, 15]. One limitation of rule-based feature recognition is that it relies on expert-defined machining feature matching rules, making it ineffective in complex situations and undefined rules. The other is the learning-based approach, which enables the model to learn complex transformations from low-level geometric information to high-level machining features like experts [16, 17]. Since the 1990s, learning-based approaches, such as artificial neural networks (ANNs), have been widely utilized in the field of feature recognition [1]. Theoretically, an ANN-based approach is able to recognize user-defined, incomplete, and intersecting features, but its effectiveness is limited by the scale of dataset, the size of network, and the data structure fed into network. With the rapid development of artificial intelligence (AI), especially deep learning (DL), many recent feature recognition studies are attempting to apply it to achieve more autonomous and flexible recognition [14, 18, 19]. These methods address some shortcomings of early neural network research, but some issues still remain, such as the need for a large amount of labeled data (machining-feature-dataset [14] has 144 K CAD models) or large neural network architecture (MsvNet [20] has over 128.86 M parameters and 7.46 G FLOPs). In real-world applications, it is challenging to collect and annotate large amounts of data, which requires significant time and resources to deploy neural networks in computer-aided process planning (CAPP) systems. In addition, large neural networks consume a lot of memory and processing time, making them unsuitable for ordinary computers and users.  
Motivated by recent advances in self-supervised learning and semi-supervised learning for image data [21], this paper proposes a semi-supervised machining feature recognition framework, aiming at efficiently learning to recognize machining features with small labeled samples. Additionally, inspired by some novel mobile convolution neural networks (CNNs), this paper presents two CPU-optimized lightweight CNNs called FeatureNetLite and MsvNetLite, which are the life versions of well-known FeatureNet [14] and MsvNet [20] for reducing memory usage and inference time while keeping high accuracy.  
The rest of this paper is organized as follows: Section 2 presents a review of recent related work. Section 3 describes the proposed methodology in detail. Section 4 shows the effectiveness and efficiency of the proposed methods. Section 5 presents a conclusion for this paper and discusses some future work.