應用感性工學與創意方法於產品造型設計─以LED自行車燈為例

The Application of Kansei Engineering and Creative Method to Product Form Design ─A case study on LED Bike Lights

黃俊賀(Jyun-He Huang)

指導教授 ： 王焜潔

嶺東科技大學/設計學院/科技商品設計研究所/碩士(2012年)

摘要

截至目前感性工學的相關研究眾多，但探討感性工學加入創意方法之相關研究卻不多見。所以本研究以感性工學的程序結合創意發展程序，探討何種造型外觀會令人有不一樣的感受。本研究目的有:(1)蒐集產品樣本以及形容詞語彙，建立個別資料庫。(2)使用形態解析法將產品形態的解構與分析，將多個不同設計元素或構件予以排列、替換，組合變化以產生新的造形。(3)建構一適性調整之創意發展程序。(4)感性工學數學建模和適性調整之創意發展程序相互整合產生一「感性工學整合創意流程」。
本研究採用了線性模式複迴歸與非線性模式倒傳遞類神經探討造型型態與感性語彙之間的相關性，研究共分為三個階段：第一階段為廣泛的收集LED自行車燈樣本及語彙。以集群分析法，共歸納出5 個代表性樣本以及15 組代表性語彙，並經由問卷調查結果進行因素分析，得到四組最終感性語彙為「普通的─新穎的」、「一般的─搶眼的」「穩定的─變化的」即「脆弱的─耐用的」因素。
第二階段以線性模式複迴歸與非線性模式倒傳遞類神經，分別將LED自行車燈形態特徵及最終語彙進行分析。其中複迴歸分析與倒傳遞類神經分析所得型態特徵誤差值，倒傳遞類神經分析誤差值皆小於複迴歸分析，因此倒傳遞類神經分析較適合運用在產品的型態特徵分析上。以倒傳遞類神經分析中語彙Y1「新穎的」平方誤差總合值為0.797低於其他三組語彙，表示七個型態特徵權重估計值中以車燈外殼造型之相關程度較大，而出燈罩造型、造型導引、燈源排列形式、開關型式、固定造型以及色彩形式之相關程度較小。最後第三階段為驗證模式，依據上一階段之結果經由感性工學整合創意流程」中以人員決定、決定主題、流程說明、討論、選擇、集合、收斂、檢討、評估標準、決策等十個步驟，產生出概念造型，經問卷調查結果得知，其中鯊魚概念之造型讓消費者對產品的喜好程度提升了22%。

關鍵字

感性工學 ； 類神經 ； 複迴歸 ； 創意方法

並列摘要

The study of Kansei Engineering is widely accepted. However, few of the studies link creative design into the process. Therefore, this study integrates them into a feasible solution to discuss the affection of product forms. The study objects are (1) collected the adjective vocabulary, (2) the analysis of analytical form design to sort, replace and combine a new form, (3)construct a process of creative procedure, (4) Kansei modeling and adjustable procedure to integrated Kansei steps. In detail, this study used multi -regression (MR) and Back Propagation Neural Network(BPN) to study the relations of forms and Kansei vocabulary. Three steps are: (1) Collected the LED lights of bicycles to clustering analysis of five represent type, such as ordinary-novel , ordinary- attracting, stable –change, and fragile-durability factor. (2)The second stage used multi regression and Back Propagation Neural Network(BPN) to feature and last vocabulary analysis. The error of features are computed by BPN and MR. The results display that “new” are 0.797 which are lower than others. This means that the factor of “forms of the weights” among the seven different types is important. The rest of them display little effectiveness. (3)The last stage is the module of verification. The study will determine the steps of personnel decision, theme, process description, discussion, selection, collection, convergence, review, and evaluation criteria. The last outcome shows the shark type offer the preference enhanced 22%.

並列關鍵字

Kansei Engineering ； Back-Propagation Neural Network, Intelligent ； multiple regression analysis ； Creative Method

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