Asset Details
Do you wish to reserve the book?
User Preference-Based Method for Characterizing Automotive Wheel Hub Styles
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
User Preference-Based Method for Characterizing Automotive Wheel Hub Styles
Do you wish to request the book?
User Preference-Based Method for Characterizing Automotive Wheel Hub Styles
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
User Preference-Based Method for Characterizing Automotive Wheel Hub Styles
2025
Overview
User preferences serve as a crucial dimension in describing the product style and its psychological experience. To assist designers in rapidly producing wheel designs that align with user preferences and enhance design efficiency by improving design transparency, this research establishes a user preference-based Automotive Wheel Hub Style Characterization Model (AWSCM). The model primarily consists of a style space, a design element space, and a matching relationship space. The style space is used to capture user preferences and product styles, the design element space for sample clustering and design element extraction, and the matching relationship space for calculating and analyzing the mathematical relationships between product styles and design elements. The model explores and reveals the logical and mathematical quantitative relationships among user preferences, product styles, and design elements. The results of controlled experiments indicate that the AWSCM has significant positive impacts on the design time and design quality. Particularly for non-professional designers, the AWSCM can help reduce the design time and enhance design comprehension, thereby improving design efficiency. This suggests that the AWSCM is beneficial for aiding designers in quickly generating reasonable designs in their design practice.
We currently cannot retrieve any items related to this title. Kindly check back at a later time.