應用感性工學於自行車造型快速建構

在全球資訊爆炸的環境下，各項產品的生命週期皆大幅縮短，企業為了生存與發展必須不斷推陳出新，開發新產品吸引消費者。然而新產品的開發雖為企業帶來營收與獲利，同時也存在著風險。新產品如未能受到消費者喜愛時，企業將會蒙受龐大的虧損。探討新產品的失敗原因，有些是未能正確掌握消費者需求；有些則是設計師與客戶溝通不良使得開發時間過長，喪失了新產品的上市時機。所以本研究以感性工學的程序為基礎藉由複迴歸理論與灰權重理論，探討出不同設計元素或構件對於使用者的感受的影響程度。本研究選定「自行車」為實驗產品，經由階層式分析、MDS分析及K-means分群法來挑選市面上最具代表性的商品後，在利用造型解構，找出產品的基本造型元素。定義每個造型元素的特徵變化形式，在透過型態學圖表，經排列組合後已衍生出多種造型，再以複迴歸與灰權重技術建立出造型參數與感性語彙之關係模型。比較複迴歸分析與灰權重分析後，發現複迴歸分析在Y1預測較為準確，但其餘Y2、Y3則為灰權重分析較為準確，在探討其總誤差比時，即可得知灰權重平均誤差14.12%較複迴歸分析21.67%來的優異，因灰權重分析在Y1的預測僅比複迴歸分析多2.27%，因此我們可以判定灰權重分析在預測造型元素對感性語彙的影響程度的正確度較為優異。

關鍵字

感性工學；灰色理論；複迴歸；自行車

並列摘要

In the age of information explosion over the world, more and more life cycle of products have been significantly shorter than usual. For survive and development in industry, the company needs out with the old and in with the new continuity to attract the consumers. Although the new product will bring gross earnings and profits, but it exists the risks at the same time. On the other side, if the new product can not be accepted by consumers, company will turn to red. For cause failure, some of taking uncertainly control on customer demands, some of taking too long on step of product development by poor communication between designer and customer and then loss the release timing of new product. The study of the process on Kansei Engineering is based on Regression analysis and Gray System Theory by Weighting analysis, and the research elicited the different design elements or design components that will influence on user’s sensation. This project studied on bicycle. Hierarchical analysis, MDS analysis and K-means analysis were developed and employed to identify the most representative model in the market. Following that using deconstruct of model to figure out the style basis with elements. To well defined the variation characteristics on each style elements by using the design database diagram and through the permutation and combination to create many models. And employed multiple regression analysis and gray system to built the related model that conducted by parametric of style and Kansei semantic analysis. By comparison between multiple regression analysis and gray system, the result of predictive value Y1 is more accurate under multiple regression analysis; Y2 and Y3 are more accurate under gray system. By means of diversity, the average of gray system is resulted 14.12 % and it better than resulted 21.67% of multiple regression analysis. The deviation ration of Y1 is 2.27%, it concluded the gray system by weighting analysis is more effectiveness on predict the style element with influenced on Kansei semantic.