【摘要】：隨著生活水平的提高,人們對于服裝內(nèi)在及外在質(zhì)量提出了更高的要求�？椢镏瞥煞b后,在穿著過程中難免會起皺,影響美觀�，F(xiàn)有的織物抗皺性測試方法與實際著裝時織物折皺情況差異甚大,不能真實評價織物在實際穿著過程中的抗皺能力。因此對織物抗皺性進行客觀、準(zhǔn)確地測試和評價就顯得很重要。針對這一問題,本文提出一種模擬實際穿著起皺的織物抗皺性測試新方法,構(gòu)建了模擬人體膝部和肘部起皺的裝置。并利用三維激光掃描技術(shù)和圖像處理技術(shù),對模擬裝置產(chǎn)生的折皺提取了三維及二維特征參數(shù),對比分析了兩種方法的優(yōu)劣。選取與主觀評價相關(guān)性較好的特征參數(shù),利用神經(jīng)網(wǎng)絡(luò)技術(shù)對折皺等級進行了預(yù)測,研究內(nèi)容及研究結(jié)果如下:(1)搭建了能夠模擬人體膝部及肘部起皺的裝置,該裝置能夠模擬靜態(tài)及動態(tài)起皺,機械化控制起皺時間、次數(shù)、角度,模擬起皺的折皺形態(tài)與實際穿著起皺折皺形態(tài)非常接近。(2)實驗設(shè)置了 4個影響織物折皺程度的變量,起皺方式、時間、次數(shù),松量。結(jié)果表明:在實驗參數(shù)下,對于抗皺性差織物來說,松量對織物起皺的形態(tài)影響比較大,起皺方式、時間、次數(shù)對織物起皺的形態(tài)影響較小。對于抗皺性較好的織物來說,4個因素對織物起皺的形態(tài)幾乎沒有影響。(3)基于三維激光掃描提取了反映織物折皺程度的特征參數(shù)。結(jié)果表明:粗糙度、平均偏移量與折皺等級顯著負(fù)相關(guān),spearman相關(guān)系數(shù)在都在0.8以上,單位法向量Z方向絕對值的粗糙度、平均偏移量與折皺等級的相關(guān)系數(shù)要高于高度方向粗糙度、平均偏移量與折皺等級的相關(guān)系數(shù)。單位法向量Z方向絕對值的的均值與折皺等級的spearman相關(guān)系數(shù)為0.679。(4)提取了灰度共生矩陣特征參數(shù)能量、熵、對比度、相關(guān)性的均值和標(biāo)準(zhǔn)差。結(jié)果表明:圖像像素為100 x 150時,熵均值及標(biāo)準(zhǔn)差與織物折皺等級的spearman相關(guān)系數(shù)在0.8以上,熵均值及標(biāo)準(zhǔn)差隨著折皺等級的增大而減小。(5)將以上三維指標(biāo)與二維特征相結(jié)合,可以提高折皺等級預(yù)測回歸模型預(yù)測精度。三維參數(shù)單位法向量Z方向絕對值平均偏移量和二維參數(shù)熵均值與折皺等級的多元線性回歸方程的精度為87.5%,三維參數(shù)單位法向量Z方向絕對值平均偏移量和折皺等級的線性回歸方程的精度為81%,精度提高了6.5%。(6)利用RBF神經(jīng)網(wǎng)絡(luò)對折皺等級進行訓(xùn)練及預(yù)測,折皺等級預(yù)測正確率為83.3%,該模型對素色織物折皺等級預(yù)測好于印花織物。部分印花織物折皺等級預(yù)測與主觀評價偏差較大,一些印花圖案對折皺有掩蓋效果,可能會導(dǎo)致主觀評價不一致,在評價部分印花織物時,需要結(jié)合主觀評價綜合評定其折皺等級。
[Abstract]:With the improvement of living standards, people put forward higher requirements for the internal and external quality of clothing. Fabric made of clothing, wearing process will inevitably wrinkle, affect beauty. There is a great difference between the existing testing methods of crease resistance of fabrics and that of the actual clothes, so it is not possible to evaluate the wrinkle resistance of fabrics in the actual wearing process. Therefore, it is very important to test and evaluate fabric wrinkle resistance objectively and accurately. In order to solve this problem, this paper presents a new method to test the wrinkle resistance of fabric, which simulates the actual wrinkling of fabrics, and constructs a device to simulate wrinkling in the knees and elbows of the human body. Using 3D laser scanning technology and image processing technology, the feature parameters of 3D and 2D are extracted from the wrinkle produced by analog device, and the advantages and disadvantages of the two methods are compared and analyzed. The wrinkle grade is predicted by using neural network technology. The research contents and results are as follows: (1) A device which can simulate wrinkling of human knees and elbows is built. This device can simulate static and dynamic wrinkling, mechanization controls wrinkling time, times, angle, and the wrinkle shape of simulation wrinkle is very close to that of actual dress wrinkle. (2) four variables affecting fabric wrinkle degree are set up in the experiment. Wrinkle mode, time, number of times, loose amount. The results show that under the experimental parameters, for the fabric with poor crease resistance, the amount of looseness has a great influence on the wrinkling morphology of the fabric, and the wrinkling mode, time and times have little effect on the wrinkling morphology of the fabric. For the fabric with good crease resistance, four factors have little effect on the wrinkle shape. (3) based on 3D laser scanning, the characteristic parameters reflecting the crease degree of the fabric are extracted. The results show that the roughness, average deviation and crease grade are negatively correlated, the spearman correlation coefficient is above 0. 8, and the roughness of the absolute value in Z direction of unit normal vector. The correlation coefficient between average migration and wrinkle grade is higher than that of height direction roughness, and the correlation coefficient between average migration and crease grade is higher than that of height direction roughness. The average value of the absolute value of the unit normal vector Z and the spearman correlation coefficient of the wrinkle grade are 0.679. (4) the energy, entropy, contrast, correlation mean and standard deviation of the characteristic parameters of the gray level co-occurrence matrix are extracted. The results show that when the image pixel is 100x150, the spearman correlation coefficient between entropy mean and standard deviation and fabric wrinkle grade is more than 0. 8, and the entropy mean and standard deviation decrease with the increase of wrinkle grade. (5) the above three dimensional indexes are combined with two dimensional features. The prediction accuracy of regression model can be improved. The accuracy of the multivariate linear regression equation of the mean deviation of absolute value in Z direction and the mean value of entropy of two-dimensional parameter and wrinkle grade of three-dimensional parameter unit normal vector is 87.5, and the mean deviation and wrinkle of Z-direction absolute value of three-dimensional parameter unit normal vector are 87.5. The accuracy of the linear regression equation of grade is 81 and the accuracy is improved by 6.5. (6) the crease grade is trained and predicted by RBF neural network. The prediction accuracy of crease grade is 83.3%, and the model is better than that of printed fabric in predicting the wrinkle grade of plain color fabric. The prediction of wrinkle grade of some printed fabrics deviates greatly from subjective evaluation, and some printing patterns have the effect of covering up creases, which may lead to inconsistent subjective evaluation. It is necessary to evaluate its wrinkle grade in combination with subjective evaluation.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS101.923

【參考文獻】

相關(guān)期刊論文 前10條

1 張曉婷;洪劍寒;g癜，

本文編號：2271699