基于特征參數(shù)的珩磨油石壽命預(yù)測(cè)研究
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
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 黎洋;花向紅;姚周祥;何屹雄;;傅里葉級(jí)數(shù)修正的動(dòng)態(tài)GM(1,1)模型在沉降預(yù)測(cè)中的應(yīng)用[J];測(cè)繪地理信息;2017年01期
2 楊柳;寧會(huì)峰;龔俊;;灰色加權(quán)馬爾可夫組合模型的車削加工尺寸預(yù)報(bào)研究[J];機(jī)械設(shè)計(jì)與制造;2016年08期
3 孫玉晶;孫杰;李劍峰;;鈦合金銑削加工刀具磨損有限元預(yù)測(cè)分析[J];機(jī)械工程學(xué)報(bào);2016年05期
4 何翔;任小洪;;基于數(shù)字圖像的刀具磨損狀態(tài)檢測(cè)技術(shù)[J];機(jī)床與液壓;2016年03期
5 劉海波;王和平;沈立頂;;基于SAPSO優(yōu)化灰色神經(jīng)網(wǎng)絡(luò)的空中目標(biāo)威脅估計(jì)[J];西北工業(yè)大學(xué)學(xué)報(bào);2016年01期
6 秦國(guó)華;謝文斌;王華敏;;基于神經(jīng)網(wǎng)絡(luò)與遺傳算法的刀具磨損檢測(cè)與控制[J];光學(xué)精密工程;2015年05期
7 張海艷;張臣;張吉林;;Kalman濾波在刀具磨損預(yù)測(cè)模型中的應(yīng)用[J];武漢科技大學(xué)學(xué)報(bào);2013年06期
8 寧會(huì)峰;施建軍;龔俊;;一種珩磨加工尺寸預(yù)報(bào)的組合模型[J];新技術(shù)新工藝;2013年08期
9 劉思峰;蔡華;楊英杰;曹穎;;灰色關(guān)聯(lián)分析模型研究進(jìn)展[J];系統(tǒng)工程理論與實(shí)踐;2013年08期
10 陳燕;楊樹寶;傅玉燦;徐九華;蘇宏華;;鈦合金TC4高速切削刀具磨損的有限元仿真[J];航空學(xué)報(bào);2013年09期
相關(guān)博士學(xué)位論文 前2條
1 劉逸;粒子群優(yōu)化算法的改進(jìn)及應(yīng)用研究[D];西安電子科技大學(xué);2013年
2 李彩虹;兩類組合預(yù)測(cè)方法的研究及應(yīng)用[D];蘭州大學(xué);2012年
相關(guān)碩士學(xué)位論文 前4條
1 劉然;刀具磨損狀態(tài)識(shí)別及預(yù)測(cè)研究[D];西南交通大學(xué);2014年
2 程歡歡;不銹鋼銑削加工刀具磨損有限元分析與刀具壽命預(yù)測(cè)[D];大連理工大學(xué);2012年
3 趙勇;面接觸磨削材料去除機(jī)理研究[D];天津大學(xué);2010年
4 陳海玲;強(qiáng)力珩磨工藝的試驗(yàn)研究[D];山東大學(xué);2006年
,本文編號(hào):1861701
本文鏈接:http://sikaile.net/kejilunwen/jiagonggongyi/1861701.html