# A Process Decision-Making Method for Planar Machining of Box-Type Components
## 1 Introduction  
As fundamental components of machines, box-type components have relatively complex structures, primarily due to their thin and unevenly distributed walls, as well as the presence of various geometric planes and numerous holes, all of which must meet high-precision manufacturing requirements. In traditional process design, the selection of process parameters for box-type components surfaces often relies on designers consulting design manuals or drawing upon personal experience. This approach places high demands on skilled process designers and, due to individual differences in understanding, can lead to uncertainty in design results [1, 2], thereby affecting the consistency of process design. This issue has become a key factor limiting the intelligent advancement of process decision-making for box-type parts. To address this, the academic community has proposed numerous methods to investigate the process design of box-type components. [PERSON]. [3] introduced a feature-based reasoning method, which utilizes an extended feature classification approach, considering the working direction and position of machining features relative to reference datums, andwhich generates machining sequences based on part setup and machine tool alternatives for turning and milling components. [PERSON]. [4] proposed a machining process program based on knowledge representation learning, which maps entities and relationships of machining graph models to dense low-dimensional vectors using the TransD algorithm, and clusters vectors to obtain parts with similar operation sequences, identical cutting tools, and machine tool machining. [PERSON] [5; 6] suggested using XML to extract machining process features of product parts, while employing the FBM method for part modeling, and utilizing a knowledge-based system approach to select process parameters for given micro-features, thereby achieving the intelligent selection and optimization of process parameters. [PERSON] [7] proposed a three-dimensional process planning method based on machining elements, which extracts all machining elements for a part, employs a hybrid algorithm combining simulated annealing and ant colony optimization to optimize the process route decision-making method for box-type parts, and provides a feature-based operation model generation method. [PERSON] [8] introduced a three-dimensional digital process design method based on MBD, which integrates MBD with knowledge engineering technology, applies knowledge engineering technology for process decision-making, and introduces ontology technology to describe process knowledge, enabling process information to be displayed in a three-dimensional environment. Although existing research has made progress in the automation of process decision-making for machining box-type parts, the independence and heterogeneity of CAX systems used in different studies hinder the sharing and transmission of process knowledge, significantly limiting the efficiency of process design (as illustrated in Figure 1).To address this issue, this study focuses on the planar machining of box-type parts and introduces ontology technology into process decision-making under the premise that the machining datum coincides with the design datum. As a formalized specification for shared conceptual models, ontology offers significant advantages in enhancing semantic interoperability among heterogeneous systems, strengthening knowledge sharing, and promoting knowledge reuse [9; 10]. Considering the requirements of artificial intelligence and its application in manufacturing, this study constructs an ontology-based knowledge base for box-type parts. This approach reduces uncertainty in machining process decision-making, improves process design efficiency, and establishes an efficient information exchange platform for process design, thereby advancing the intelligization of process decision-making for box-type parts.