## 4 Results
The specific dimensional information retrieved from the reference model is summarized in Table 5.  
Based on the dimension attribute information from the reference model, the key components for dimension annotation in the target model are identified. The names of the key components that require dimensioning for the \"Rear Wall Assembly\" model are shown in Table 6.  
In Section 3.5, SolidWorks API was utilized to perform dimension annotation on the engineering drawing views of the \"Rear Wall Assembly\". According to the code in Table 2, the dimensions were mapped and annotated on the engineering drawing, with the annotation results shown in Figure 7. Figure 8 displays the overall dimension annotation results for the Rear Wall Assembly engineering drawing. Table 7 presents a time comparison between manual annotation and algorithm-based annotation on different components, as well as the efficiency improvement multiples. The results indicate a significant reduction in time when using the algorithm for dimensioning compared to manual annotation. Specifically, for simpler components like the left wall foam assembly, the efficiency improvement was as high as 7.6 times. For more complex components, such as the left wall assembly, although the algorithm also reduced annotation time, the efficiency improvement was slightly lower, around 6.7 times. These results demonstrate that automated dimensioning can significantly save time in the design and manufacturing process, particularly for tasks that involve repetitive annotations, thereby improving overall work efficiency. \begin{table}
\begin{tabular}{c c} \hline \hline
**Target Model** & **Key Component Selection Results** \\ \hline Rear Wall Assembly & Inner Skin, Outer Skin, Foam Componen, Wooden Framework \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Target model key component selection results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Extract Name Information** & **Relevant** \\
**Number** & **Extract Name Information** & **Components** \\ \hline No.1 & D2@Sketch 8@Split Non-Independent Rear & Inner Skin \\  & Inner Skin-1@Split Non-Independent Rear & Inner Skin \\ \hline No.2 & D3@Sketch 8@Split Non-Independent Rear & Inner Skin \\  & Inner Skin-1@Split Non-Independent Rear & Inner Skin \\ \hline No.3 & D4@Sketch 8@Split Non-Independent Rear & Inner Skin \\  & Inner Skin-1@Split Non-Independent Rear & Inner Skin \\ \hline No.4 & D5@Sketch 8@Split Non-Independent Rear & Inner Skin \\  & Inner Skin-1@Split Non-Independent Rear & Inner Skin \\ \hline No.5 & D1@Sketch 1@Split Non-Independent Rear & Outer Skin \\  & Outer Skin-1@Split Non-Independent Rear & Outer Skin \\ \hline No.6 & D2@Sketch 1@Split Non-Independent Rear & Outer Skin \\  & Outer Skin-1@Split Non-Independent Rear & Outer Skin \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Extraction of key dimensional parameters from the reference model. Figure 7: “Rear Wall Frame” engineering drawing dimensioning results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode Name** & **Manual Annotation/min** & \
\begin{tabular}{c} **Algorithmic** \\ **Annotation/min** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency Improvement** \\ **Factor** \\ \end{tabular} \\ \hline Left Wall Foam Assembly & 6.03 & 0.79 & 7.6 \\ Left Wall Outer Skin & 5.13 & 0.68 & 7.5 \\ Left Wall Inner Skin & 4.87 & 0.62 & 7.8 \\ Left Wall Framework & 5.85 & 0.81 & 7.2 \\ Left Wall Assembly & 7.23 & 1.07 & 6.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of manual annotation and algorithmic annotation in terms of time and efficiency improvement. Figure 9 presents a comparative evaluation of the proposed MKD-ICP algorithm against traditional rule-based methods and SVM-based methods. Across all complexity levels, the MKD-ICP method consistently achieves the lowest dimensioning error compared to rule-based and SVM-based methods. Especially in complex geometries, MKD-ICP maintains an error rate below 5%, while rule-based methods exceed 10%, and SVM-based methods approach 8%.