發(fā)布時(shí)間：2018-08-07 21:49

【摘要】：在實(shí)際生產(chǎn)中,產(chǎn)品經(jīng)常具有多個(gè)質(zhì)量特性且特性之間互有影響,本文主要對(duì)關(guān)聯(lián)多響應(yīng)問(wèn)題的穩(wěn)健性優(yōu)化進(jìn)行研究,目的在于在響應(yīng)間具有相關(guān)性的情況下考慮到因子容差的波動(dòng)對(duì)于響應(yīng)的影響,最終找到問(wèn)題的穩(wěn)健最優(yōu)解。以此為目標(biāo),本文通過(guò)響應(yīng)曲面法建立影響因子與響應(yīng)之間的關(guān)系模型,使用馬氏距離函數(shù)法對(duì)響應(yīng)之間的相關(guān)性進(jìn)行了考慮,進(jìn)一步對(duì)存在因子容差的條件下如何進(jìn)行穩(wěn)健性優(yōu)化并獲得穩(wěn)健最優(yōu)解進(jìn)行了研究。具體研究?jī)?nèi)容如下:首先,本文闡述了試驗(yàn)設(shè)計(jì)與響應(yīng)曲面法的基本理論與方法,并進(jìn)一步對(duì)多響應(yīng)穩(wěn)健性優(yōu)化的概念與幾種常見(jiàn)的優(yōu)化方法進(jìn)行了介紹與總結(jié),選取考慮了方差-協(xié)方差矩陣的馬氏距離函數(shù),將關(guān)聯(lián)多響應(yīng)優(yōu)化問(wèn)題轉(zhuǎn)化為使總體馬氏距離函數(shù)最小化的問(wèn)題。其次,引入結(jié)合了遺傳算法與模式搜索的混合智能算法對(duì)總體馬氏距離函數(shù)進(jìn)行極小化尋優(yōu),先使用遺傳算法在可行域內(nèi)進(jìn)行全局性尋優(yōu),再采用模式搜索算法對(duì)返回的解進(jìn)行局部精確尋優(yōu)。與單一的模式搜索算法相比,混合智能算法可以更好的處理具有高度復(fù)雜性的函數(shù)的優(yōu)化問(wèn)題,并且比單一的智能算法更能提高最優(yōu)解的精度。最后,分析因子容差對(duì)馬氏距離函數(shù)的影響,針對(duì)因子容差波動(dòng)的影響對(duì)馬氏距離函數(shù)進(jìn)行優(yōu)化,并使用遺傳算法與模式搜索的混合智能算法尋找穩(wěn)健最優(yōu)解,該方法可以得到落在穩(wěn)健可行域中的穩(wěn)健最優(yōu)解,這樣的解對(duì)因子容差的波動(dòng)是不敏感的。
[Abstract]:In actual production, the product often has multiple quality characteristics and the characteristics affect each other. In this paper, the robust optimization of correlated multi-response problems is studied. The aim of this paper is to find the robust optimal solution of the problem by considering the effect of the fluctuation of factor tolerance on the response when the response is correlated. In this paper, the response surface method is used to establish the relationship model between the factors and the response, and the correlation between the responses is considered by using the Markov distance function method. Furthermore, how to optimize the robustness and obtain the robust optimal solution under the condition of factor tolerance is studied. The specific research contents are as follows: firstly, the basic theory and method of experimental design and response surface method are expounded, and the concept of multi-response robust optimization and several common optimization methods are introduced and summarized. The Markov distance function which considers the variance-covariance matrix is selected to transform the associated multi-response optimization problem into the problem of minimizing the total Markov distance function. Secondly, a hybrid intelligent algorithm combining genetic algorithm and pattern search is introduced to minimize the global Mahalanobis distance function. Then the pattern search algorithm is used to optimize the returned solution with local precision. Compared with the single pattern search algorithm, the hybrid intelligent algorithm can better deal with the optimization problem with high complexity, and can improve the accuracy of the optimal solution better than the single intelligent algorithm. Finally, the influence of factor tolerance on Markov distance function is analyzed. The Mahalanobis distance function is optimized according to the influence of factor tolerance fluctuation, and the robust optimal solution is found by using the hybrid intelligent algorithm of genetic algorithm and pattern search. This method can obtain the robust optimal solution in the robust feasible domain, which is insensitive to the fluctuation of factor tolerance.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TB114;TP18

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本文編號(hào)：2171448