# User Preference-Based Method for Characterizing Automotive Wheel Hub Styles
## 4 Experimental Analysis
### Experimental Results and Analysis  
The results of this experiment aim to evaluate the impact of AWSCM on design efficiency and design quality in order to determine whether the model can deepen designers' understanding of style and improve design efficiency.  
1. Design time. In terms of design time, the average time for experimental group A\({}^{\prime}\) was 57 min, the average time for experimental group B\({}^{\prime}\) was 76 min, and the overall average time for the experimental groups was 66.5 min. The average time for control group A\({}^{\prime\prime}\) was 83 min, the average time for control group B\({}^{\prime\prime}\) was 123 min, and the overall average time for the control groups was 103 min. There was a 26 min difference between experimental group A\({}^{\prime}\) and control group A\({}^{\prime\prime}\) and a 47 min difference between experimental group B\({}^{\prime}\) and control group B\({}^{\prime\prime}\); both differences were significant (\(p<0.01\)). Overall, there was a 36.5 min difference between the experimental groups and the control groups, which was statistically significant (\(p<0.05\)). Additionally, through cross-comparison, it can be found that there was a 6 min difference between experimental group B\({}^{\prime}\) and control group A\({}^{\prime\prime}\), which was statistically significant (\(p<0.05\)). Based on the above data, it can be considered that AWSCM has a significant positive impact on reducing the design time and improving design efficiency.
2. Design quality. In terms of design quality, the average score for experimental group A\({}^{\prime}\) was 85, the average score for experimental group B\({}^{\prime}\) was 78, and the overall average score for the experimental groups was 81.5. The average score for control group A\({}^{\prime\prime}\) was 82, the average score for control group B\({}^{\prime\prime}\) was 65, and the overall average score for the control groups was 73.5. There was a 3-point difference between experimental group A\({}^{\prime}\) and control group A\({}^{\prime\prime}\) and a 13-point difference between experimental group B\({}^{\prime}\) and control group B\({}^{\prime\prime}\); both differences were significant (\(p<0.05\)). Overall, there was an 8-point difference between the experimental groups and the control groups, which was statistically significant (\(p<0.05\)). Additionally, through cross-comparison, it can be found that there was a 4-point difference between experimental group B\({}^{\prime}\) and control group A\({}^{\prime\prime}\), which was statistically significant (\(p<0.05\)). Based on the above data, it can be considered that AWSCM has a significant positive impact on improving the design quality of non-professional participants.
3. Design efficiency. In terms of design efficiency, by comparing the statistical results of design time and design quality, for professional participants, there was a 26 min time difference and a 3-point score difference between experimental group A\({}^{\prime}\) and control group A\({}^{\prime\prime}\), suggesting that AWSCM can improve design efficiency by reducing the design time. For non-professional participants, there was a 47 min time difference and a 13-point score difference between experimental group B\({}^{\prime}\) and control group B\({}^{\prime\prime}\), suggesting that AWSCM can improve design efficiency by reducing the design time and enhancing design understanding. Through cross-comparison, it can be further suggested that AWSCM can help non-professional participants improve design efficiency by reducing the design time and enhancing design understanding, as there was a 6 min time difference and a 4-point score difference between experimental group B\({}^{\prime}\) and control group A\({}^{\prime}\).