本文選題：加工參數(shù)優(yōu)化　切入點：響應(yīng)面法　出處：《西南交通大學(xué)》2015年博士論文　論文類型：學(xué)位論文

【摘要】：在金屬切削過程中,加工參數(shù)不僅影響了切削過程的生產(chǎn)效率,而且還影響了切削力和切削零件的表面質(zhì)量。實際上,切削力和零件的表面質(zhì)量是密切相關(guān)的,切削力也是影響表面質(zhì)量的因素。所以加工參數(shù)的優(yōu)化研究對改善切削過程中的切削力和加工表面質(zhì)量有非常重要的意義。本文以切削力和表面粗糙度為直接對象,應(yīng)用基于響應(yīng)面法的代理模型和人工神經(jīng)網(wǎng)絡(luò)模型技術(shù),對加工參數(shù)進行優(yōu)化。本論文主要進行了以下幾個方面的工作：(1)在建立切削力模型的基礎(chǔ)上,分析研究了影響切削力的主要因素。在本論文的研究工作中,除了把切削速度、切削深度、進給量作為關(guān)鍵的加工參數(shù)外,還把銑刀的刀齒數(shù)引入作為一個重要加工參數(shù)。在分析基于響應(yīng)面的加工粗糙度代理模型的基礎(chǔ)上,通過實驗設(shè)計確定了四個關(guān)鍵加工參數(shù)的范圍和水平。(2)進行了基于響應(yīng)面的粗糙度代理模型的實驗研究和靈敏度分析。首先根據(jù)四個關(guān)鍵加工參數(shù)的水平,通過切削實驗采集了有關(guān)加工參數(shù)和表面粗糙度的樣本數(shù)據(jù)集,在此基礎(chǔ)上建立了基于二次響應(yīng)面法的加工參數(shù)和表面粗糙度的代理模型。其次,進一步通過代理模型的靈敏度分析,簡化了模型中對粗糙度不明顯的項。最后利用代理模型對加工參數(shù)進行優(yōu)化,切削實驗表明基于響應(yīng)面法的表面粗糙度代理模型的誤差在可接受的范圍之內(nèi)。(3)基于響應(yīng)面法進行了影響切削力和表面粗糙度的加工參數(shù)優(yōu)化研究。根據(jù)四個關(guān)鍵加工參數(shù)的水平,通過實驗采集有關(guān)粗糙度和切削力數(shù)據(jù)的基礎(chǔ)上,建立了表面粗糙度和切削力的線性和二階響應(yīng)面代理模型,并通過切削實驗對代理模型進行了驗證,確保了代理模型的可靠性。應(yīng)用建立的代理模型,對加工參數(shù)進行了單目標(biāo)和多目標(biāo)加工參數(shù)優(yōu)化。同樣,切削實驗表明基于代理模型,針對表面粗糙度和切削力的單、多目標(biāo)優(yōu)化是可靠的。(4)進行了基于人工神經(jīng)網(wǎng)絡(luò)模型的加工參數(shù)單目標(biāo)和多目標(biāo)優(yōu)化的研究。在研究人工神經(jīng)網(wǎng)絡(luò)模型結(jié)構(gòu)和原理的基礎(chǔ)上,應(yīng)用人工神經(jīng)網(wǎng)絡(luò)建立了基于切削力和表面粗糙度的加工參數(shù)單、多目標(biāo)優(yōu)化模型,切削實驗表明基于神經(jīng)網(wǎng)絡(luò)模型的切削力和粗糙度模型能夠很好地逼近真實的切削模型。論文工作在試驗設(shè)計的基礎(chǔ)上,通過多種代理模型研究加工參數(shù)對切削力和表面粗糙度的影響規(guī)律,以及應(yīng)用代理模型對加工參數(shù)單、多目標(biāo)優(yōu)化的方法,切削試驗證結(jié)果表明所建立的各種代理模型和優(yōu)化方法是有效和可行的。這項研究有三種技術(shù)。這些技術(shù)的響應(yīng)面法(RSM),田口和人工神經(jīng)網(wǎng)絡(luò)(ANN)。在響應(yīng)面方法中,敏感性分析被用于以評估每個切削參數(shù)的重要程度。單響應(yīng)表明,在這項研究中與所使用的其他加工參數(shù)之間比較更為重要。在多響應(yīng)(雙響應(yīng))表面粗糙度為獨立響應(yīng),而切削力被稱為伴隨響應(yīng)。田口技術(shù)應(yīng)用研究中具有相同的加工參數(shù)和相同的水平,取樣結(jié)果與全因子設(shè)計相比結(jié)果非常好。使用田口方法的取樣的數(shù)量等于三分之一全因子法中使用的數(shù)據(jù)。采用神經(jīng)網(wǎng)絡(luò)方法對加工參數(shù)進行優(yōu)化,得到滿意的結(jié)果。
[Abstract]:In the process of metal cutting, machining parameters of cutting process not only affects the production efficiency, but also affect the surface quality of the cutting force and cutting parts. In fact, the cutting force and surface quality of parts is closely related to the factors of cutting force also affects the surface quality. The optimization of processing parameters to improve the cutting force in the process of cutting and machining surface quality is very important. In this paper, the cutting force and surface roughness as direct object, application agent model of response surface method and artificial neural network model based on the technology of processing parameters are optimized. This paper mainly discussed the following aspects: (1) based on the cutting force model, mainly analyzes the factors that influence the cutting force. In this study, in addition to the cutting speed, cutting depth, feed rate as the key processing The parameters, the cutter tooth number of milling cutter is introduced as an important processing parameters. Based on the analysis of response surface roughness agent model based on four key processing parameters and the level determined by the experimental design. (2) were studied and the sensitivity analysis of response surface roughness model. Based on four key process parameters, by cutting experiment collect the machining parameters and the surface roughness of the sample data set is established based on the agent model of machining parameters of two order response surface method and surface roughness based on. Secondly, through further sensitivity agent model analysis, simplified the roughness is not obvious. In the final model using proxy model to optimize machining parameters, cutting experiments show that the surface response surface method of rough degree of error in model based on agent Within the acceptable range. (3) response surface method is the effect of cutting force and surface roughness of machining parameter optimization based on degree. According to the four key processing parameters collected by the experiments based on the level of roughness and the cutting force data, a linear surface roughness and the cutting force and the two order the response surface model, and through the cutting experiment of agent model is validated to ensure the reliability of the model. The agent model based on the single objective and multi-objective optimization of machining parameters on machining parameters. Also, the cutting experiments show that based on the agent model, the surface roughness and the cutting force of single and multiple targets the optimization is reliable. (4) the processing parameters of artificial neural network model of single objective and multi-objective optimization based on artificial neural network. Based on the model structure and the principle of the application. The artificial neural network to establish the cutting force and surface roughness on machining parameters based on single and multi objective optimization model, cutting experiments show that the cutting force based on neural network model and roughness model can well approximate the real cutting model. This paper based on experimental design, through a variety of proxy processing model parameters the law of cutting force and surface roughness, and the application of processing parameters of single agent model, the multi-objective optimization method, cutting test results of various agent model and optimization method proposed is effective and feasible. This study has three kinds of technology. The response surface method of these technologies (RSM), Tian and artificial neural network (ANN). The response surface method, sensitivity analysis is used to assess the importance of each cutting parameter. The single response shows that in this study with other use and Work is more important. The comparison between parameters in multi response (dual response surface roughness) as an independent response, and the cutting force is called with the response. With processing parameters and the same level of application of Taguchi, sampling results compared with full factorial design results very well. The number of sampling using Taguchi method the use of 1/3 factor method is equal to the data. By using neural network method to optimize machining parameters and obtain satisfactory results.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：TH16
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本文編號：1597593