## 1 Introduction  
Engineering drawings are essential technical documents in the design and manufacturing processes of mechanical products. Dimensions and technical requirements play a vital role in standardizing manufacturing operations and ensuring consistent product quality. Although modern 3D CAD software (SolidWorks2020) can automatically generate 2D projection views with dimensions after model creation, the accuracy of these dimensions is often insufficient [1]. As a result, designers must manually verify and adjust the dimensions to achieve completeness. Furthermore, for the same 3D model, different designers may adopt varying dimensioning approaches, leading to significant inconsistencies in the standardization and quality of engineering drawings, largely depending on their expertise and technical proficiency [2]. Research on automatic dimensioning for engineering drawings not only supports intelligent design processes and improves design efficiency but also helps standardize dimensioning workflows, thereby enhancing the quality of finalized drawings. As a critical medium for conveying information, dimensions have long been a central focus and a major challenge in this field. To advance the automation and intelligence of dimensioning, researchers worldwide have conducted extensive studies, making notable progress in improving the efficiency and accuracy of dimension completeness verification [3]. For instance, [PERSON] and [PERSON] [4; 5] provided a comprehensive overview of the current state of dimensioning research, emphasizing its significance in engineering applications. [PERSON] et al. [6] proposed a rapid dimensioning method for intelligent 3D process design based on a \"feature-dimension-removal body\" constraint mechanism. In the area of feature modeling, [PERSON] et al. [7] developed an automated technology for efficient and standardized dimension annotation generation based on predefined rules. By organizing installation dimension annotation rules for various iron fittings, they designed a solution capable of automatically recognizing annotation objects and generating dimension annotations using a backend configuration table. [PERSON] et al. [8], adhering to the GB/T 24734.1-11-2009 standards (GB/T 24734; General Rules for Defining Data of Digital Products. Publisher: Beijing, China, 2009) explored rapid dimensioning techniques for 3D designs. [PERSON] et al. [9] proposed an automatic dimensioning algorithm for shaft components based on functional characteristics, employing graph theory to eliminate redundant dimensions. In the field of 2D dimensioning, [PERSON] et al. [10] leveraged the SolidWorks API (SolidWorks2020) to investigate batch generation and automatic dimensioning of engineering drawings. [PERSON] et al. [11] further redefined graphic output primitive features, built a case library, and studied automatic dimensioning techniques for 2D designs. Similarly, [PERSON] et al. [12] proposed a line-chain feature-based automatic dimensioning algorithm for 2D engineering drawings, further exploring the relationship between 3D model features and 2D drawing elements. In recent years, state-of-the-art dimensioning techniques have increasingly leveraged artificial intelligence approaches such as deep learning and support vector machines (SVMs). For instance, CNN-based models have demonstrated promising results in interpreting complex geometric features and automatically generating annotations. SVMs and other classical machine learning techniques have also been applied to identify relevant dimensions based on training data. Additionally, rule-based heuristics remain widely used in industry due to their interpretability and reliance on domain knowledge. However, these methods often face challenges related to generalizability, data dependency, or rule maintenance. In contrast, the proposed MKD-ICP algorithm integrates geometric matching and case-based reasoning, requiring minimal training data while maintaining adaptability to varying model types and annotation requirements.