本文選題：超音速等離子噴涂　切入點(diǎn)：人工神經(jīng)網(wǎng)絡(luò)　出處：《江西理工大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：摩擦磨損是造成零件失效的一個(gè)很重要的原因，所以零件耐磨性能的好壞在一定程度上決定了零件的使用壽命。研究證明，在零件表面制備一層具有潤(rùn)滑耐磨性能的涂層，可以顯著地改善表面的磨損狀況。在材料的摩擦學(xué)方面，存在著許多無(wú)法用明確的函數(shù)表達(dá)式來(lái)描述的非線(xiàn)性問(wèn)題，而這些問(wèn)題對(duì)了解材料的摩擦磨損性能有著很重要的作用，而人工神經(jīng)網(wǎng)絡(luò)很適合來(lái)處理這個(gè)問(wèn)題，它可以完成n維空間矢量到m維空間矢量的映射。 本文采用超音速等離子噴涂技術(shù)在基體GCr15表面制備了KF-301/WS2復(fù)合涂層，利用MM-U5G屏顯示材料端面高溫摩擦磨損試驗(yàn)機(jī)對(duì)涂層進(jìn)行摩擦磨損試驗(yàn)，然后分析其摩擦磨損性能。采用正交試驗(yàn)方法對(duì)影響涂層摩擦學(xué)性能的因素進(jìn)行分析，確定較優(yōu)的配方組合。以實(shí)驗(yàn)數(shù)據(jù)為基礎(chǔ)，以溫度、摩擦?xí)r間、潤(rùn)滑劑含量和表面微造型為輸入量，，摩擦系數(shù)和磨損量為輸出量，建立了一個(gè)4×7×2的三層BP神經(jīng)網(wǎng)絡(luò)，通過(guò)網(wǎng)絡(luò)模型對(duì)樣本數(shù)據(jù)進(jìn)行訓(xùn)練學(xué)習(xí)，然后用訓(xùn)練好的網(wǎng)絡(luò)對(duì)涂層進(jìn)行摩擦磨損性能的預(yù)測(cè)分析。 研究結(jié)果表明：當(dāng)溫度在300℃~600℃時(shí)，磨損量和摩擦系數(shù)隨著溫度的不斷升高而增大，但增長(zhǎng)較緩慢，而當(dāng)溫度在600℃~750℃時(shí)，摩擦量和摩擦系數(shù)隨著溫度升高增長(zhǎng)較快。在同一溫度和同一WS2含量的情況下，不同微造型面的摩擦磨損性能從高到低依次是凹坑、菱形、平行、斷紋。溫度和表面微造型相同時(shí)，WS2含量為30%時(shí)的磨損性能要比WS2含量為20%時(shí)稍好一些。WS2含量為40%時(shí)，摩擦性能最差。當(dāng)配方組合為潤(rùn)滑劑含量30%，表面微造型為凹坑時(shí)，涂層的摩擦磨損性能較好一點(diǎn)。通過(guò)三層BP神經(jīng)網(wǎng)絡(luò)的分析，預(yù)測(cè)結(jié)果和試驗(yàn)結(jié)果總體上擬合的比較好，預(yù)測(cè)結(jié)果所反映的規(guī)律和試驗(yàn)結(jié)果所反映的規(guī)律吻合，預(yù)測(cè)精度較高，因此所建神經(jīng)網(wǎng)絡(luò)模型可以對(duì)涂層摩擦性能進(jìn)行預(yù)測(cè)分析。
[Abstract]:Friction and wear is a very important reason for the failure of the parts, so the wear resistance of the parts determines the service life of the parts to a certain extent. It is proved that a coating with lubricating and wear resistance is prepared on the surface of the parts. In tribology of materials, there are many nonlinear problems that cannot be described by explicit functional expressions, and these problems play an important role in understanding the friction and wear properties of materials. The artificial neural network is suitable to deal with this problem. It can map n-dimensional space vector to m-dimensional space vector. In this paper, KF-301/WS2 composite coatings were prepared on the surface of GCr15 substrate by supersonic plasma spraying technology. Friction and wear tests were carried out on the coatings by MM-U5G display material end surface high temperature friction and wear tester. Then the friction and wear properties of the coating were analyzed. The factors affecting the tribological properties of the coating were analyzed by orthogonal test method, and the optimum formula was determined. Based on the experimental data, temperature and friction time, A three-layer BP neural network of 4 脳 7 脳 2 is established, in which the lubricant content and the surface microform are input and the friction coefficient and wear quantity are output. The training and learning of the sample data are carried out by the network model. Then the friction and wear properties of the coatings are predicted and analyzed by the trained network. The results show that when the temperature is 300 鈩

本文編號(hào)：1569037