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題名：參數化感性設計程序之發展
作者：陳鴻源 引用關係
作者(外文)：Hung-Yuan Chen
校院名稱：國立成功大學
系所名稱：工業設計學系碩博士班
指導教授：張育銘
學位類別：博士
出版日期：2008
主題關鍵詞：感性工學；產品造形；NDSA；造形特徵；意象感受；Product Form；NDSA；Image Perception；Kansei Engineering；Form Features
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消費者在日常生活中與產品的互動，潛意識地發展出對產品的整體評價與意象區別的本能。產品造形的美感不但直接影響消費者心理感受，同時也扮演著決定產品在市場上成功的角色。直覺上，令消費者滿意的產品造形意象，其中必存某些明確的產品特徵。因此在設計初步階段而言，如何掌握此特定的特徵進行設計，往往是設計師關注的重點與興趣，以便於其設計成果能更貼近消費者對於產品意象的期望。
然而，在產品的發展過程中，產品造形的部份往往由設計師藉由個人主觀的偏好與創作認知所決定。因此，這種藉由設計師主觀認定所設計出來的產品造形，往往存在無法滿足消費者期望的可能性。為了要改善此現象，以設計出更客觀地符合消費者意象的產品造形，近年來許多研究提出系統化的產品造形設計方法，以降低設計師主觀認定的成份，並且朝向客觀的方式進行產品造形設計。感性工學即是一門轉換消費者意象於產品造形的系統化方法之一。傳統的感性工學經常以「類目」與「項目」的質化方式定義產品整體造形，此質化的定義方式，在建立產品造形特徵與消費者意象的關聯性後，雖可提供一定程度的特徵描述參考，但細節造形的設計資訊仍需藉由設計師個人的主觀經驗臆測與決定。基於此，發展更精密且詳細的產品造形定義方式，以更瞭解產品造形細節變化對消費者意象的對應關係，即成為當前設計研究的議題之一。然而，精密詳細的造形描述，往往需要更多的變數去定義產品造形。因此，如何挑選出有效的變數，以輔助設計師更有效率地設計出令人滿意的產品造形，即是發展系統化方法的另一個重要議題。
因此，本研究目的在於發展出一個參數化感性設計程序，藉由數值化定義為基礎之系統化方法，以汽車與餐刀兩個範例產品，進行產品造形的詳盡定義，並經由一系列的評估實驗所獲得的結果，分別應用線性複迴歸分析(MRA)、倒傳遞神經網路(BPN)與迴歸倒傳遞神經網路(MRBPN)等三種分析技術，建構出三種以數值化定義為基礎之產品造形特徵與消費者意象感受的對應關係模式，並進行產品造形的可能性意象評價的預測績效比較。結果顯示：以迴歸倒傳遞神經網路(MRBPN)技術構成之預測模式，不僅在消費者對產品造形意象評價的預測方面具有相當的精確性，而且篩選具影響力之設計變數的機制，能夠提供設計師調整產品造形，更有效率地設計出與消費者期望意象相符的產品造形。最後，在本研究中，此數值化定義為基礎的系統化方法，已藉由汽車輪廓與餐刀造形兩個範例的實驗與驗證確認其可行性，雖然在此僅以此兩範例作為說明，但此系統化方法論的建構概念，亦可被類推與應用至其他消費性產品的造形設計。
Consumers interact with a vast number of diverse products during the course of their daily lives and therefore subconsciously develop powerful product evaluation and discrimination skills. A consumer’s psychological perception of a product is significantly influenced by its aesthetics, and thus product form plays an essential role in determining the commercial success of a product. Intuitively, it seems reasonable to suppose that a consumer’s psychological satisfaction with a product is governed by a particular sub-set of the product’s form features. Consequently, it is of fundamental interest to designers to identify these critical form features during the early stages of the design process such that a product design can be produced which more closely matches the product image expectations of the target consumers.
he evolution of a product’s form during the design process is typically governed by the designer’s individual preferences and creative instincts. As a consequence, there is a risk that the product form may fail to satisfy the consumers’ expectations or may induce an unanticipated consumer response. In an attempt to address this problem, many systematic product form design methodologies have been proposed in recent years to minimize the requirement for subjective judgments on the part of the designer and to objectively relate the form features of a product to the emotional response induced by the product in the consumer. Kansei Engineering (KE) has emerged as one of the most powerful techniques for taking explicit account of the correlation between the physical form of a product and its projected image. Traditional KE approaches are commonly based on the concept of “items” and “categories”, defined in pictorial terms and used to generate high-level qualitative descriptions of the overall product form. However, since such approaches can not reliably predict the effects on the consumers’ emotional response of introducing subtle changes in the product form, the input of an experienced designer is still required to subjectively conjecture the consumers’ likely perception of the product’s projected image. Consequently, a requirement exists for sophisticated mechanisms capable of describing the form of a product in an explicit manner such that the correlation between the individual product form features and the consumers’ perception of the product image can be more reliably modeled. However, such systems typically require the use of a large number of variables to accurately describe the product form, and thus the problem of identifying the particular sub-set of design variables which govern the consumer’s psychological response to the product is inevitably complex.
ccordingly, this study commences by developing a parametric Kansei design procedure in accordance with the numerical definition-based systematic approach (NDSA) for generating an explicit numerical definition of a product’s geometrical form. A series of evaluation trials are then performed to establish the correlation between the product form features and the consumers’ perceptions of the product image. The results of the evaluation trials are used to construct three different types of mathematical model, namely a multiple regression analysis (MRA) model, a back-propagation neural network (BPN) model, and a combined multiple regression analysis / back-propagation neural network (MRBPN) model, to predict the likely consumer response to any arbitrary product form designed in accordance with the NDSA of parametric Kansei design procedure. Evaluating the predictive performance of the three models, it is found that the MRBPN model not only yields an acceptable level of prediction accuracy, but also enables the influential design parameters to be sieved from the general design variables when constructing the predictive model such that the designer can more readily produce desirable product forms in an efficient and cost effective manner. The feasibility of the proposed NDSA of parametric Kansei design procedure is demonstrated using two illustrative product form examples, namely a 2D automobile profile and a 3D knife form, respectively. Although this study takes just two examples for illustration and verification purposes, the methodology proposed in this thesis is equally applicable to any form of consumer product.
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