【摘要】：在汽車(chē)發(fā)動(dòng)機(jī)活塞銷(xiāo)表面制備N(xiāo)i-ZrO_2納米復(fù)合鍍層,以期提高活塞銷(xiāo)表面的耐磨性。通過(guò)正交試驗(yàn),得到影響納米復(fù)合鍍層磨損量的因素主次順序,并在正交試驗(yàn)基礎(chǔ)上建立BP神經(jīng)網(wǎng)絡(luò)模型,對(duì)納米復(fù)合鍍層的磨損量進(jìn)行預(yù)測(cè),進(jìn)而評(píng)價(jià)其耐磨性。研究表明:BP神經(jīng)網(wǎng)絡(luò)模型能用于預(yù)測(cè)納米復(fù)合鍍層的磨損量,磨損量的預(yù)測(cè)值與實(shí)驗(yàn)值具有較高的擬合度。通過(guò)正交試驗(yàn)與運(yùn)用BP神經(jīng)網(wǎng)絡(luò)模型優(yōu)化的最佳參數(shù)組合相同,采用最佳參數(shù)組合制備的納米復(fù)合鍍層的磨損量較低(僅為4.26mg),其耐磨性良好。
[Abstract]:Ni-ZrO_2 nanocomposite coating was prepared on the surface of motor piston pin in order to improve the wear resistance of piston pin surface. The primary and secondary order of the factors influencing the wear amount of nano-composite coating was obtained by orthogonal test. Based on the orthogonal test, the BP neural network model was established to predict the wear rate of nano-composite coating and then to evaluate its wear resistance. The results show that the ratio BP neural network model can be used to predict the wear of nano-composite coatings, and the predicted value of wear quantity has a high fitting with the experimental value. The orthogonal test is the same as the optimal parameter combination optimized by using BP neural network model. The wear rate of nano-composite coating prepared by the best parameter combination is lower (only 4.26mg), and its wear resistance is good.
【作者單位】： 四川航天職業(yè)技術(shù)學(xué)院;
【分類(lèi)號(hào)】：TG174.4;U464.133.1

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