發(fā)布時(shí)間：2018-11-18 14:27

【摘要】：如今消費(fèi)者逐漸重視對(duì)感性的需求，感性意象的定量研究已成為當(dāng)今設(shè)計(jì)學(xué)科熱點(diǎn)研究的問(wèn)題之一。感性工學(xué)為設(shè)計(jì)師提供了了解消費(fèi)者內(nèi)心感受的重要工具。本研究將運(yùn)用感性工學(xué)研究方法，以智能手機(jī)為例對(duì)其外觀形態(tài)進(jìn)行解構(gòu)分析，研究產(chǎn)品造型設(shè)計(jì)的類目與項(xiàng)目，進(jìn)而探究造型設(shè)計(jì)要素的感性特征及其感性意象空間的關(guān)聯(lián)性。 在大量調(diào)研與分析的基礎(chǔ)上，篩選目標(biāo)產(chǎn)品的研究樣本，，運(yùn)用形態(tài)分析法，對(duì)產(chǎn)品外觀形態(tài)進(jìn)行解構(gòu)，并確定產(chǎn)品造型要素及其項(xiàng)目，從而獲得樣本吸引消費(fèi)者的關(guān)鍵影響因素。結(jié)合正交試驗(yàn)的方法，將多個(gè)不同設(shè)計(jì)元素或構(gòu)件進(jìn)行排列、替換，組合變化產(chǎn)生新的造型，以確定典型代表實(shí)驗(yàn)樣本。 搜集感性意象語(yǔ)匯，建立資料庫(kù)，并利用多元尺度、聚類分析等方法，篩選并確定代表性感性意象的語(yǔ)匯；結(jié)合產(chǎn)品造型樣本，進(jìn)行感性評(píng)價(jià)試驗(yàn)，運(yùn)用語(yǔ)義差分法將人們對(duì)于產(chǎn)品外觀形態(tài)的主觀評(píng)價(jià)轉(zhuǎn)化為量化評(píng)價(jià)值，從而確定消費(fèi)者的感性需求。 基于產(chǎn)品形態(tài)要素量化值與產(chǎn)品外觀形態(tài)主觀評(píng)價(jià)量化值，進(jìn)行基于數(shù)量化I類分析方法的產(chǎn)品造型意多元線性回歸模型構(gòu)建。并以此預(yù)測(cè)自變量與因變量之間的影響程度，建立關(guān)于感性意象的產(chǎn)品造型要素設(shè)計(jì)法則；對(duì)所建立的模型進(jìn)行驗(yàn)證，利用統(tǒng)計(jì)學(xué)檢驗(yàn)方法來(lái)分析模型的可靠性。 鑒于線性模型的局限性，基于非線性BP人工神經(jīng)網(wǎng)絡(luò)理論，結(jié)合產(chǎn)品形態(tài)要素與感性意象評(píng)價(jià)的量化值，確定多層神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)，訓(xùn)練網(wǎng)絡(luò)達(dá)到預(yù)定目標(biāo)。構(gòu)建關(guān)于產(chǎn)品形態(tài)要素的感性意象預(yù)測(cè)模型。通過(guò)比較誤差評(píng)價(jià)所建立的產(chǎn)品形態(tài)法則，得到不同建模方法的優(yōu)劣性。并利用造型意象非線性模型預(yù)測(cè)結(jié)果，分析造型要素對(duì)感性意象影響規(guī)律。
[Abstract]:Nowadays, consumers pay more and more attention to the demand of sensibility, and the quantitative study of perceptual image has become one of the hot issues in the field of design. Perceptual engineering provides an important tool for designers to understand consumers' inner feelings. This research will use the perceptual engineering research method, take the smart phone as an example to carry on the deconstruction analysis to its appearance form, studies the product modelling design category and the item, Then explore the emotional characteristics of the design elements and the relevance of perceptual image space. On the basis of a great deal of investigation and analysis, the research samples of the target products are screened, the appearance of the products is deconstructed by morphological analysis, and the elements of product modeling and their items are determined. Thus obtaining samples to attract consumers is a key factor. Combined with the orthogonal test method, several different design elements or components are arranged, replaced and combined to produce a new shape to determine the typical representative experimental samples. Collect the words of perceptual image, set up the database, and use the methods of multivariate scale and cluster analysis to screen and determine the vocabulary of representative perceptual image; Based on the sample of product modeling, the perceptual evaluation test is carried out, and the subjective evaluation of product appearance is transformed into quantitative evaluation value by using semantic difference method, so as to determine the perceptual needs of consumers. Based on the quantitative value of product form elements and subjective evaluation value of product appearance, the multi-linear regression model of product modeling meaning is constructed based on quantitative class I analysis method. The influence degree between independent variable and dependent variable is predicted, the design rule of product modeling elements about perceptual image is established, the established model is verified, and the reliability of the model is analyzed by statistical test method. In view of the limitation of the linear model, based on the theory of nonlinear BP artificial neural network, combined with the quantitative value of product morphological elements and perceptual image evaluation, the multi-layer neural network structure is determined and the training network achieves the predetermined goal. The perceptual image prediction model about product form elements is constructed. The advantages and disadvantages of different modeling methods are obtained by comparing the product morphology rules established by error evaluation. The influence of modeling elements on perceptual image is analyzed by using the prediction results of modeling image nonlinear model.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB472

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