本文選題：輪轂造型 + 意象偏好　； 參考：《燕山大學(xué)》2016年碩士論文

【摘要】：目前,傳統(tǒng)的輪轂造型設(shè)計(jì)開發(fā)方法凸顯出一種弊端,造型方案的最終確定基本憑借設(shè)計(jì)師和決策者的直覺判斷,這就導(dǎo)致了設(shè)計(jì)的輪轂產(chǎn)品不能很好的迎合市場(chǎng)中廣大用戶的審美需求和意象偏好。為了使輪轂造型能夠更好的滿足用戶的個(gè)性化需求,需要一種能夠?qū)⒂脩粢庀笃靡氲捷嗇炘煨驮O(shè)計(jì)過程中的設(shè)計(jì)方法,通過運(yùn)用這種方法達(dá)到破譯用戶的感覺編碼,并將其轉(zhuǎn)化為輪轂造型設(shè)計(jì)變量的目的。本文提出了如何將用戶的意象需求引入輪轂造型設(shè)計(jì),并將其轉(zhuǎn)化為造型形態(tài),使最終設(shè)計(jì)出的輪轂符合用戶意象需求的問題。以用戶意象偏好的輪轂款型推薦方法作為研究目標(biāo),結(jié)合感性工學(xué)、計(jì)算機(jī)輔助等技術(shù),將用戶對(duì)輪轂的意象偏好或感覺準(zhǔn)確地轉(zhuǎn)變成輪轂的設(shè)計(jì)變量要素,進(jìn)而能夠更加有效的對(duì)用戶的意象偏好與輪轂造型設(shè)計(jì)變量之間的映射關(guān)系進(jìn)行探索和研究,以這種映射關(guān)系為依據(jù),最終設(shè)計(jì)出的輪轂造型就更能符合用戶的意象偏好。本文采用感性工學(xué)、統(tǒng)計(jì)學(xué)、機(jī)器學(xué)習(xí)等領(lǐng)域的相關(guān)知識(shí)構(gòu)建了一個(gè)行之有效的推薦系統(tǒng),來最大化的滿足用戶的意象需求。首先,對(duì)輪轂的關(guān)鍵造型特征以設(shè)計(jì)變量的形式表現(xiàn)。其次,以感性工學(xué)(Kansei Engineering)理論作為基礎(chǔ),利用調(diào)查問卷的數(shù)據(jù)建立輪轂的評(píng)價(jià)量表。再次,利用支持向量機(jī)(Support Vector Machine)方法構(gòu)建用戶意象語匯與偏好之間關(guān)系模型和輪轂設(shè)計(jì)變量與偏好之間的關(guān)系模型。最后,通過遺傳算法(Genetic Algorithm)求出滿足用戶意象需求的最優(yōu)的輪轂造型。本研究結(jié)果對(duì)輪轂設(shè)計(jì)過程中有效把握用戶意象偏好,設(shè)計(jì)符合用戶意象需求的輪轂造型具有一定的參考價(jià)值及借鑒意義。
[Abstract]:At present, the traditional wheel hub modeling design development method highlights a kind of drawback. The final determination of the modeling scheme basically depends on the intuitive judgment of the designer and the decision maker.This leads to the design of hub products can not meet the needs of the majority of users in the market aesthetic needs and image preferences.In order to make hub modeling better meet the individual needs of users, it is necessary to introduce user image preference into the design process of hub modeling, which can be used to decode the user's sensory coding.And the purpose of transforming it into wheel hub modeling design variable.In this paper, the problem of how to introduce the user's image requirement into the hub modeling design and transform it into the shape form is put forward, so that the final design hub meets the user's image requirement.In this paper, the user's image preference of hub model recommendation method is taken as the research goal, and combining with perceptual engineering and computer aided technology, the user's image preference or feeling towards the hub is accurately transformed into the design variable element of the hub.Furthermore, the mapping relationship between user's image preference and hub design variables can be explored and studied more effectively. Based on this mapping relationship, the final design of hub modeling can be more in line with the user's image preference.In this paper, an effective recommendation system is constructed with the knowledge of perceptual engineering, statistics, machine learning and so on, to maximize the image needs of users.First, the key modeling features of the hub are expressed in the form of design variables.Secondly, based on Kansei engineering theory, the evaluation scale of hub is established by using the data of questionnaire.Thirdly, the relationship model between user image vocabulary and preference and the relationship model between hub design variables and preferences are constructed by using support vector machine support Vector machine method.Finally, genetic algorithm is used to obtain the optimal hub modeling to meet the needs of user image.The results of this study have a certain reference value and reference significance for the wheel hub design process to effectively grasp the user image preference and design the hub modeling which meets the needs of the user image.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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