感性工學結合迴圈式創意思考聯想法應用於產品設計

Integration on Kansei Engineering Product Design through Association Thinking

林夢葦(Meng-Wei Lin)

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

目前感性工學的相關研究眾多，但探討感性工學加入創意概念之相關研究卻不多見。所以本研究以感性工學的程序結合迴圈式創意思考聯想法，探討何種造型外觀會令人有不一樣的感受。本研究目的在於發掘消費者對產品造型形式與產品形象之間的關係；並將消費者感性導入產品設計之中。 研究結果可作為激發設計創意與構想方法的參考。
本研究採用了線性模式複迴歸分析與非線性模式倒傳遞類神經網路探討造型型態與感性語彙之間的相關性，運用四組語彙，驗證線性分析模式與非線性分析模式之差異。研究結果如下：(1)倒傳遞類神經網路分析誤差值皆小於複迴歸分析，因此倒傳遞類神經網路分析較適合運用在產品的型態特徵分析上。(2)以倒傳遞類神經網路分析中語彙Y4「流線的」平方誤差總合值為0.933低於其他三組語彙，顯示七個型態特徵權重估計值中以外殼造型及按鈕形式之相關程度較大；而出音口造型、出音口位置、音量鈕、開關鈕及色彩之相關程度較小。(3)上述之結果經由「迴圈式創意思考聯想法」分別為產品定位、特徵語彙、特徵聯想、抽取設計元素、圖像、轉化等六大步驟，產生出三種概念造型，經問卷調查結果得知，其中蝸牛概念之造型讓消費者對產品的語彙特徵顯著提升。

關鍵字

感性工學 ； 倒傳遞類神經網路 ； 複迴歸分析 ； 迴圈式創意思考聯想法

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 applies the iterative associate thinking to discuss affection. The goal of this study is to discover the form and image relations to employ into the product design. The outcome can stimulate the design creative and idea as a reference. In detail, this study used multi regression (MR) and Back Propagation Neural Network(BPN) to study the relations of forms and Kansei vocabulary. Four sets of vocabularies are used to MR and BPN to attain there differences. The results are : (1)the error of BPN is less than those of MR. Hence, BPN is available for feature analysis on product. (2)The flow type has 0.933 of error which is lower than the rest of other vocabulary. It displays that the form and button type offer good relations. The rest of the factors have little influences. (3) The above mentioned outcomes are based in iterative creative associated thinking to analyze the Product positioning, feature vocabulary, feature association to extract the design elements, images, transforming to build three new design forms. From the questionnaire, we attain that snail type forms that enhance the customer vocabulary of feature.

並列關鍵字

Kansei Engineering ； Back-Propagation Neural Network, Intelligent ； Multiple Regression Analysis ； Association Thinking

參考文獻

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