【摘要】：通用橋式起重機(jī)是國(guó)民經(jīng)濟(jì)建設(shè)的重要起重設(shè)備，對(duì)我國(guó)的現(xiàn)代化生產(chǎn)和基礎(chǔ)設(shè)施的建設(shè)起著至關(guān)重要的作用。橋式起重機(jī)的金屬結(jié)構(gòu)占整機(jī)重量的60%以上，沿用至今的起重機(jī)金屬結(jié)構(gòu)設(shè)計(jì)理論偏于保守，主要表現(xiàn)在安全系數(shù)過(guò)高，結(jié)構(gòu)自重偏大，這樣就人為造成起重機(jī)的整體尺寸和重量過(guò)于龐大。若能保證在強(qiáng)度、剛度、穩(wěn)定性的前提下，將其重量盡可能的減小，不僅有利于運(yùn)輸安裝，同時(shí)也會(huì)使制造成本大大降低。因此，采用合理的優(yōu)化設(shè)計(jì)方法對(duì)橋式起重機(jī)的金屬結(jié)構(gòu)進(jìn)行優(yōu)化，是起重機(jī)設(shè)計(jì)中必不可少的一個(gè)環(huán)節(jié)。 到目前為止，國(guó)內(nèi)外不少學(xué)者對(duì)起重機(jī)金屬結(jié)構(gòu)的優(yōu)化問(wèn)題進(jìn)行了研究，并取得一定的成果，概括起來(lái)所使用的方法主要有：懲罰函數(shù)法、隨機(jī)方向法、復(fù)合形法、遺傳算法等。然而這些方法主要在優(yōu)化目標(biāo)的提高上做了大量工作，未對(duì)其優(yōu)化性能(優(yōu)化速度和全局收斂性)進(jìn)行任何探討，而一個(gè)真正實(shí)用有效的優(yōu)化方法不僅要具有好的優(yōu)化結(jié)果，更應(yīng)具備優(yōu)良的優(yōu)化性能。目前用于起重機(jī)金屬結(jié)構(gòu)優(yōu)化設(shè)計(jì)的遺傳算法，基本將問(wèn)題歸類(lèi)為混合變量的優(yōu)化問(wèn)題，造成必須對(duì)優(yōu)化后的設(shè)計(jì)變量進(jìn)行圓整；另外，基本遺傳算法均存在著早熟、收斂速度慢及全局收斂率低的缺陷。鑒于此，考慮在生產(chǎn)實(shí)踐中，人眼所能辨識(shí)的精度及單軋鋼板的公稱(chēng)厚度“1.”對(duì)鋼板所規(guī)定的厚度尺寸，，重新定位起重機(jī)金屬結(jié)構(gòu)的優(yōu)化設(shè)計(jì)屬于約束、非線性、離散變量的優(yōu)化設(shè)計(jì)問(wèn)題，避免了對(duì)優(yōu)化后的設(shè)計(jì)變量必須進(jìn)行圓整的現(xiàn)象；并針對(duì)遺傳算法的固有缺陷將具有較強(qiáng)局部搜索能力模擬退火算法融入到改進(jìn)的遺傳算法中，形成一種混合算法——自適應(yīng)模擬退火遺傳算法(Adaptive Simulated Annealing Genetic Algorithm，簡(jiǎn)稱(chēng)ASAGA)，由于新算法強(qiáng)化了各自的優(yōu)點(diǎn)，弱化了各自的缺點(diǎn)，達(dá)到了行為互補(bǔ)，通過(guò)實(shí)例驗(yàn)證了其較好的優(yōu)化性能。 基于Visual C++6.0開(kāi)發(fā)平臺(tái)，以通用橋式起重機(jī)主梁截面積為目標(biāo)函數(shù)，建立通用橋式起重機(jī)箱形主梁優(yōu)化設(shè)計(jì)的數(shù)學(xué)模型，應(yīng)用所形成的混合算法編制“起重機(jī)箱形主梁優(yōu)化”軟件，進(jìn)行通用橋式起重機(jī)主梁截面的優(yōu)化，并將優(yōu)化結(jié)果與工程實(shí)例進(jìn)行對(duì)比，在滿足主梁所要求的各種約束條件下使優(yōu)化后的主梁截面積減少了19.9%，說(shuō)明應(yīng)用該軟件能降低起重機(jī)重量，減少鋼材消耗，達(dá)到了節(jié)約成本和提高設(shè)計(jì)效率的目的。
[Abstract]:Bridge crane is an important lifting equipment in national economy construction, which plays an important role in modern production and infrastructure construction of our country. The metal structure of bridge crane accounts for more than 60% of the weight of the whole crane. The design theory of crane metal structure used up to now is conservative, which mainly shows that the safety factor is too high and the weight of the structure is too large. This artificial cause the overall size and weight of the crane is too large. If the weight can be minimized on the premise of strength, stiffness and stability, it is not only advantageous to transport and installation, but also can greatly reduce the manufacturing cost. Therefore, it is necessary to optimize the metal structure of bridge crane by means of reasonable optimization design method. Up to now, many scholars at home and abroad have studied the optimization of crane metal structure, and achieved certain results. The main methods used in this paper are: penalty function method, random direction method, compound shape method, etc. Genetic algorithm, etc. However, these methods have done a great deal of work on the improvement of optimization objectives, without any discussion of their optimization performance (optimization speed and global convergence), and a real practical and effective optimization method should not only have good optimization results. It should also have excellent optimization performance. At present, the genetic algorithm used in the optimization design of crane metal structure basically classifies the problem as the optimization problem of mixed variables, which makes the optimized design variables must be rounded. In addition, the basic genetic algorithm has the defects of premature convergence, slow convergence rate and low global convergence rate. In view of this, the accuracy of human eye recognition and the nominal thickness of single rolled steel plate are considered in production practice "1." For the thickness dimensions of steel plate, the optimization design of the metal structure of the repositioning crane belongs to the optimization design problem of constraint, nonlinear and discrete variables, which avoids the phenomenon that the optimized design variables must be rounded. Aiming at the inherent defects of genetic algorithm, the simulated annealing algorithm with strong local search ability is integrated into the improved genetic algorithm to form a hybrid algorithm-adaptive simulated annealing genetic algorithm (Adaptive Simulated Annealing Genetic Algorithm,) for short ASAGA),. Because the new algorithm strengthens their advantages and weakens their shortcomings, the new algorithm achieves complementary behavior, and its better optimization performance is verified by an example. Based on the Visual C 6.0 development platform, taking the cross section area of the main girder of the general bridge crane as the objective function, the mathematical model of the optimization design of the box girder of the general bridge crane is established. The software of "crane box girder optimization" is compiled by using the hybrid algorithm, and the optimization of the section of the main girder of the general bridge crane is carried out. The optimization results are compared with the engineering examples. The optimized cross section area of the main beam can be reduced by 19.9g under various constraint conditions which meet the requirements of the main beam. It is shown that the application of the software can reduce the crane weight and steel consumption, and achieve the purpose of saving cost and improving the design efficiency.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH215

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