| Summary: | 碩士 === 朝陽科技大學 === 工業工程與管理系碩士班 === 100 === Shoes pattern development has been an important process for bridging design intentions and production steps. Most designers would prefer hand sketching draft than prototyping a 3D shoe model, and thus making pattern development a time consuming bottleneck. Current shoe development process requires three steps, transferring design sketches into a 2D forme, rearranging curve segments on the forme into pieces of shoe patterns, and revising forme segments by test makings. All of the steps are performed by hands with experiences. However on the production side, all processes can be driven and carried out by computer data. It will be profitable to create an automatic procedure that transfers design intentions into usable 2D patterns, for it will save lots of human efforts.
This research will apply reverse engineering to scan a 3D shoe example, and acquire a 3D shoe model. An artificial neural network is then created to learn the relationships between the 3D shoe segments and the 2D corresponding pattern segments. A well trained neural network should be able to map the 3D and 2D based on one shoe last size, and will deliver more mappings for the designs of the same shoe last.
The results show promising mapping ability on two pairs of shoe examples.
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