隨著社會的發展，伴隨產品設計方法與工業製造技術的不斷創新與消費者需求的不斷轉變，產品開發逐漸以生產導向轉為消費者導向。產品設計的趨勢已經由單純以機能考量的觀點轉變為融入產品語意，以期能創造出更符合消費者感性的產品。正因如此，在現今產品供大于求的時代，作為企業需要真正了解消費者的感性需求，才能勾起消費者內心深層次的購買欲望，從而使自身產品在消費者的眾多選擇中脫穎而出。對於設計師來說，在設計的早期階段能夠精準的了解消費者的感性需求特徵是至關重要的，這樣才能設計除出與目標消費者的產品形象期望更加接近的產品設計。
為此，感性工學理論（Kansei Engineering）逐漸發展為最受廣泛使用的設計理論方法，它明確了產品的物理形式與感性認知之間的相關性的同時重視消費者情感上的需求，並將其轉化爲量化數據，映射為設計元素。感性工學雖然可以一定程度反應消費者的感受，但如何將感性資料加以運用從而使設計師在設計過程中更準確的設計出符合消費者感受的產品造型仍然是目前須探討研究的重要議題。
因此，本研究提出一種具有系統化思維的造型影像生成系統，該方法藉由感性工學為基礎結合深度學習中的生成對抗網絡(Generative Adversarial Network)並以汽車側視造型作為說明性案例。首先本研究經過兩次問卷調查，應用因素分析、模糊C平均兩種分析技術定義了一千筆汽車側視資料與消費者感性之間的關係並進行量化排序處理。我們將消費者感性數據進行聚類，聚類結果顯示聚類數目為五類時最適合作為生成對抗網絡的訓練資料。經過對深度卷積對抗生成網絡大量的訓練與調整，本研究共生成了五類汽車側面造型圖像，實驗的結果展示了本研究想法的可行性，也證明經本研究生成的圖像能夠更有效率、準確地輔助設計師進行造型調整，從而設計出更符合消費者感受的產品。最後，雖然本研究僅以汽車側面造型作為案例說明，但此系統化的方法亦可被類推與應用至其他消費性產品的造形設計流程中。

With the rapid development of society and the gradual improvement of human living standards, products need to meet the functional needs of consumers and emotional needs. With the continuous innovation of product design methods and industrial manufacturing technology and the continuous change of consumer demand, product development has gradually changed from production-oriented to consumer-oriented. The trend of product design has changed from a purely functional perspective to one that incorporates product semantics to create products that are more in line with consumer sensibilities. Therefore, when the supply of products exceeds the demand in today's era, companies need to truly understand consumers' emotional needs to evoke their deep-seated desire to buy, thus making their products stand out among the many consumers' choices. For designers, the most important thing is to accurately understand the perceptual needs of consumers in the early stages of design features. Only in this way can we design a product design closer to the expectations of the target consumer's product image.
Therefore, Kansei Engineering (KE) has gradually developed into the most widely used design theory approach, clarifying the correlation between the physical form and perceptual cognition. KE focuses on consumers' emotional needs, translating them into quantitative data and mapping them into design elements. Although KE can reflect consumers' feelings to a certain extent, using perceptual data to more accurately design products that match consumers' feelings in the design process is still an important issue to be explored and studied.
This study proposes a systematic thinking system for modeling image generation that combines Kansei Engineering and Generate Adversarial Networks (GAN). We use the side view of a Generation illustrative case. Firstly, we use the questionnaire to select the Kansei words that consumers think are suitable for describing cars. Then, use a semantic differential scale to quantify the consumers' perceptual cognition. Fuzzy C-Means (FCM) is used to cluster the quantitative data. The clustered data are used as a training set to build a GAN, which the GAN continuously trains to assist designers when they need to design products. In this study, the feasibility of this systematic design approach has been confirmed by experimentation and validation of the car side view. Although this is only an example here, the constructive concept of this systematic methodology can be applied by analogy to the design of other consumer products.

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