本文選題：珩磨機(jī) + 油石磨損量��； 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：珩磨油石的磨損狀態(tài)對(duì)產(chǎn)品的最終質(zhì)量有著較大的影響。為了預(yù)測(cè)油石的切削壽命,便于合理的更換油石,通過對(duì)比油石磨損量與磨鈍標(biāo)準(zhǔn)來判斷油石是否需要更換。所以本文引入灰色神經(jīng)網(wǎng)絡(luò),通過將珩磨工藝加工特征參數(shù)作為模型輸入來預(yù)測(cè)油石的磨損量,最終建立了珩磨油石磨損量預(yù)報(bào)模型來預(yù)測(cè)油石的壽命。從而為提前更換油石提供了理論依據(jù),在保證機(jī)床穩(wěn)定運(yùn)行,提高加工產(chǎn)品質(zhì)量,節(jié)約制造執(zhí)行系統(tǒng)中生產(chǎn)成本等方面具有重大意義。本論文主要研究?jī)?nèi)容包括:(1)研究了神經(jīng)網(wǎng)絡(luò)和灰色神經(jīng)網(wǎng)絡(luò)預(yù)報(bào)模型算法。由于神經(jīng)網(wǎng)絡(luò)具有高度非線性擬合能力以及珩磨加工本身可看做一個(gè)灰色系統(tǒng),通過分析比對(duì)各種預(yù)報(bào)模型,最終選用以上兩種模型。并對(duì)模型結(jié)構(gòu)的選擇,關(guān)鍵參數(shù)的設(shè)置進(jìn)行了詳細(xì)的闡述。給出了評(píng)價(jià)模型擬合精度和穩(wěn)定性的依據(jù)。(2)研究了智能算法在預(yù)報(bào)模型優(yōu)化中的運(yùn)用。對(duì)比了粒子群算法(PSO),遺傳算法(GA)以及蟻群算法(ACO)的優(yōu)缺點(diǎn)。由于PSO算法具有收斂速度快,需要調(diào)整的參數(shù)較少等優(yōu)點(diǎn),采用該算法對(duì)模型進(jìn)行優(yōu)化。并根據(jù)算法存在的不足,提出了利用變異因子來對(duì)標(biāo)準(zhǔn)PSO算法進(jìn)行優(yōu)化改進(jìn),并利用目標(biāo)函數(shù),對(duì)算法的尋優(yōu)能力和收斂性進(jìn)行比較。(3)研究了適合珩磨油石磨損量預(yù)報(bào)的預(yù)報(bào)模型。以強(qiáng)力珩磨的數(shù)據(jù)為基礎(chǔ),建立了基于BPNN的珩磨油石磨損量預(yù)報(bào)模型,并利用MPSO算法和GA算法對(duì)其進(jìn)行優(yōu)化。由于珩磨加工可看為灰色系統(tǒng),首先,利用灰色關(guān)聯(lián)度分析了珩磨加工特征參數(shù)對(duì)珩磨油石磨損量的影響;其次,建立了基于GNN的油石磨損量組合預(yù)報(bào)模型,并利用MPSO算法對(duì)模型中的灰參數(shù)進(jìn)行優(yōu)化。通過仿真實(shí)驗(yàn)對(duì)比建立的各種模型,基于MPSO-GNN模型的MPAE值更小,說明該模型的精度更高,預(yù)測(cè)更穩(wěn)定。因此,該模型在珩磨油石磨損量預(yù)測(cè)中具有一定的優(yōu)勢(shì),可以用于實(shí)際加工中預(yù)測(cè)油石的磨損狀態(tài),進(jìn)而合理更換油石。
[Abstract]:The wear state of honing stone has a great influence on the final quality of the product. In order to predict the cutting life of the oil stone and make it convenient to replace the oil stone, it is necessary to judge whether the oil stone needs to be replaced by contrasting the wear quantity and the bluntness standard of the oil stone. In this paper, grey neural network is introduced to predict the wear rate of honing stone by using honing process characteristic parameters as model input. Finally, the prediction model of honing stone wear quantity is established to predict the life of honing stone. Thus it provides a theoretical basis for the early replacement of oilstones, which is of great significance in ensuring the stable operation of machine tools, improving the quality of processed products, and saving the production cost in the manufacturing execution system. The main contents of this paper include: 1) the neural network and grey neural network prediction model algorithms are studied. Because the neural network has the ability of highly nonlinear fitting and honing itself can be regarded as a grey system, through the analysis and comparison of various prediction models, the two models are finally selected. The selection of model structure and the setting of key parameters are described in detail. The application of intelligent algorithm in prediction model optimization is studied. The advantages and disadvantages of particle swarm optimization (PSO), genetic algorithm (GA) and ant colony algorithm (ACO) are compared. Because the PSO algorithm has the advantages of fast convergence and few parameters to be adjusted, this algorithm is used to optimize the model. According to the shortcomings of the algorithm, this paper proposes to optimize and improve the standard PSO algorithm by using the mutation factor, and uses the objective function. The prediction model suitable for prediction of honing stone wear is studied by comparing the optimization ability and convergence of the algorithm. Based on the data of strong honing, the prediction model of honing stone wear quantity based on BPNN is established, and the MPSO algorithm and GA algorithm are used to optimize the model. Because honing can be seen as a grey system, firstly, the influence of honing characteristic parameters on honing stone wear is analyzed by grey correlation degree, secondly, the combined prediction model of honing stone wear quantity based on GNN is established. The grey parameters in the model are optimized by MPSO algorithm. Through the comparison of various models established by simulation experiments, the MPAE value based on MPSO-GNN model is smaller, which shows that the accuracy of the model is higher and the prediction is more stable. Therefore, the model has some advantages in predicting the wear volume of honing stone, which can be used to predict the wear state of the stone in practical processing and to replace the oil stone reasonably.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG580.67

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