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研究生: 戴均儒
Tai, Chun-Ju
論文名稱: 應用參數紋理於滑鼠表面設計之視覺意象研究
A Study of Visual Perception on Mouse Surface Design by Using Parametric Pattern
指導教授: 謝孟達
Shieh, Meng-Dar
學位類別: 碩士
Master
系所名稱: 規劃與設計學院 - 工業設計學系
Department of Industrial Design
論文出版年: 2019
畢業學年度: 108
語文別: 英文
論文頁數: 97
中文關鍵詞: 參數紋理視覺意象滑鼠表面紋理設計參數化設計
外文關鍵詞: Parametric Pattern, Parametric Design, Visual Perception, Mouse Surface Design
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  • 現今參數化設計越來越盛行,在許多產品設計中可發現參數化設計的應用,這些產品時常伴隨著造型複雜的參數紋理。紋理在產品設計中所扮演的角色十分重要,紋理的表現形式會直接影響產品的質感。故本研究旨在探討參數紋理應用於滑鼠表面設計對消費者視覺意象感受之影響,並進一步探討提升同款參數紋理疏密比例造成的視覺意象變化。
    本研究彙整目前常見的參數紋理與分類方式,最終依Jabi所提出的分類方式並透過Grasshopper製作出53款參數紋理。接著利用焦點團體法分類與討論,選出14組代表紋理並製作出可調紋理疏密之實驗介面。邀請11位專家實測選出各自認為合適的3種紋理疏密之參數,再將參數套用回滑鼠電腦立體模型中。經焦點團體法選出每款參數紋理之滑鼠代表圖2張,最後進行意象評分並透過相依樣本t檢定來找出紋理疏密的變化對視覺意象感受的影響,本研究結果顯示:
    1. 專家認為以平面型態表現的參數紋理疏密程度,於疏值、適中值與密值對於具有美感的認知差異不大。當紋理套用於滑鼠模型上則大多會選用疏值或適中值。
    2. 參數紋理在視覺意象感受上大多具有現代、裝飾、前衛、變化、科技的感受。隨紋理密度的增加裝飾感會明有顯著的上升,其中較為特別的視覺意象感受為變化與穩定。此感受隨紋理密度的增加並不會使意象偏向於其中一側,其中若紋理構成的單一元素具有鏡像對稱的特性,或紋理具有匯集點,紋理密度增加時會加強變化的感受。若紋理基底為簡單的幾何排列,或紋理線條有交錯,則會加強其穩定的感受。其餘感受則通常會隨密度的增加造成傳統、保守、復古的感受提升。本研究的結果可提供設計師在未來設計參數紋理於產品外觀設計時,擬定紋理疏密之參考依據。

    Currently, parametric design is increasingly prevalent. The application of parametric design can be found in many product designs. These products are often accompanied with parametric patterns of complicated modeling. Patterns play a very important role in product design. The expressive forms of patterns directly affect the texture of products. Therefore, this study aimed to examine the effects of the application of parametric patterns in mouse surface design on consumers' perception of visual images, and further investigate the visual image changes caused by the increasing ratio of sparseness to denseness in the same parametric pattern.
    This study compiled common parametric patterns and classification methods, and finally worked out 53 parametric patterns through Grasshopper3D according to the classification method proposed by Jabi. Then, by means of the classification and discussion of the focus group, 14 groups of representative patterns were selected, and the experimental interface of adjustable pattern sparseness and denseness was worked out. Eleven experts were invited to select three parameters of pattern sparseness and denseness; they considered appropriate patterns based on actual measurement, and then the parameters were applied back to the computer stereoscopic model of the mouse. The focus group decided on two representative pictures of the mouse for each parametric pattern. Finally, image scoring was conducted, and paired samples t-test was used to decide the effects of the changes in pattern sparseness and denseness on the perception of visual images. The results of this study are as follows:
    1. Experts thought that when expressing the sparseness and denseness of parametric patterns by planar pattern, the sparse, medium and dense values led to little difference in aesthetic perception. When the pattern was applied to the mouse model, most people would choose the sparse or medium value.
    2. In terms of the perception of visual images, parametric patterns mostly involve modern, decorative, avant-garde, changing and technological feelings. With increasing pattern density, the decorative feeling will increase significantly; the more peculiar feelings of the visual images were changing and stable. With increasing pattern density, this feeling would not make the image lean to either alternative. If a single element of the pattern composition had the characteristic of mirror symmetry, or the pattern had a convergence point, the changing feeling would strengthen when the pattern density increased. If the pattern base was a simple geometric arrangement or the pattern lines were interlaced, the stable feeling would be stronger. As for the rest of the feelings, with increasing density, traditional, conservative and retro feelings usually increased. The results of this study can provide designers with a reference to determine the pattern sparseness and denseness when designing parametric patterns in product design appearance in the future.

    摘要 ii SUMMARY iii ACKNOWLEDGEMENTS v TABLE OF CONTENTS vi LIST OF TABLES viii LIST OF FIGURES ix CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Motivation 2 1.3 Research Purposes 3 1.4 Research Scope and Limitations 4 1.5 Research Framework 5 CHAPTER 2 LITERATURE REVIEW 6 2.1 Parametric Design 6 2.2 Parametric Pattern 8 2.2.1 Classification of parametric patterns 9 2.2.2 Application of parametric patterns 10 2.3 Kansei Engineering 12 2.4 Student's t-test 13 CHAPTER 3 RESEARCH METHOD 15 3.1 Focus Group 15 3.2 KJ Method 16 3.3 Semantic Differential Scale 17 3.4 Questionnaire Survey 17 3.5 Reliability Analysis 18 3.6 Paired Sample T-Test 19 CHAPTER 4 RESEARCH PROCESS 21 4.1 Collect appropriate parameter values of pattern sparseness and denseness 23 4.1.1 Preparation of parametric patterns 23 4.1.2 Classification of parametric patterns 30 4.1.3 Preparation of the experiment interface 34 4.1.4 Collect appropriate values of sparseness and denseness parameters 35 4.2 Select appropriate representative pictures 37 4.2.1 Preparation of mouse public version 37 4.2.2 Application of parametric patterns to the mouse model 37 4.2.3 Select representative pictures 39 4.3 Selection of adjective vocabulary words 44 4.4 Preparation of the image feeling questionnaire 44 4.5 Data Collection and Analysis 45 4.5.1 Mean value of visual image of parametric patterns on the mouse surface 46 4.5.2 Visual image picture of parametric pattern sparseness and denseness on the mouse surface 48 4.5.3 Effect of the visual image of sparse and dense parametric patterns on the mouse surface 48 4.6 Summary 60 CHAPTER 5 CONCLUSION AND RECOMMENDATION 67 5.1 Research Conclusion 67 5.2 Recommendation 68 REFERENCES 69 Appendix A 53 parametric patterns 72 Appendix B The Grasshopper construction scripts of the 7 classified parametric patterns 77 Appendix C The Grasshopper construction scripts of the 14 representative parametric patterns 79 Appendix D The values from the experts’ assessment 84 Appendix E 40 wireless mouses available on the market 85 Appendix F The median, mode and mean of sparse, medium and dense values apply back to the mouse model 86 Appendix G 140 adjectives words 94 Appendix H Image feeling questionnaire 95 Appendix I Visual image picture 96

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