|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|5127963||1489064||2016||15 صفحه PDF||سفارش دهید||دانلود رایگان|
- This study investigates the characteristics of shape, style, vocabularies to the NBA players' and sneakers' association.
- To obtain the Kansei assessment between shoes' curves and NBA players' style through the image questionnaire.
- To categorize between 50 basketball shoes and the NBA's top 10 point guards by SOM topological feature map.
- BPN was used to confirm that the validation accuracy for testing sneakers was acceptable.
- To apply morphing process in sneaker style, designers can create lots of conceptual forms in a short time.
Due to the improvements associated with our modern lifestyle, both personal preferences and commercial market value should be considered when undertaking the challenge of new product development. Thus, a crucial research topic is how to design a customized product for consumers. In this study, a feature recognition and shape-design process for basketball sneakers was established that integrated Kansei engineering and artificial neural networks (ANNs), in combination with famous point guards in the National Basketball Association (NBA), as the basis for constructing a topological feature map for sneakers. We used questionnaires to get 20 fans in NBA games to assign a rating value in Kansei adjectives to evaluate 50 basketball shoes and to identify the top 10 NBA point guards. The fans' perspectives combined the psychological and physical scales for the top 10 point guards to establish a gross relationship between sneakers and the point guards. Based on competitive learning in the self-organizing map (SOM), the similarities of the inputs from the Kansei perspective concerning NBA point guards and sneakers can be reduced in their dimensions and grouped into six clusters. A back propagation network (BPN) is proposed to verify the mapping of sneakers onto point guards. The results were validated by comparing them with the target neurons based on SOM feature map. A MATLAB program was developed using SOM and BPN algorithms to verify the groups of sneakers accompanying the point guards. Then, the system can categorize the sneakers that match the players. A feature-based, shape-morphing process for the design of a new style of sneaker was implemented. A model to blend the features was constructed in SolidWorks CAD by choosing any two different shapes from the SOM map. This research was used to add the vast variety of shapes and design of sneakers from the selected SOM feature map to help designers create many different styles in a short period of time.
Journal: Computers & Industrial Engineering - Volume 102, December 2016, Pages 408-422