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陶瓷材料電加工表面粗糙度的預(yù)測
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    摘要:

    針對電加工工藝參數(shù)與性能指標(biāo)的函數(shù)映射關(guān)系大多具有非線性的特征,提出了將BP神經(jīng)網(wǎng)絡(luò)引入電加工領(lǐng)域中??紤]到BP算法的不足,提出用遺傳算法來優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的連接權(quán)值,設(shè)計(jì)了基于進(jìn)化神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法,建立了陶瓷材料電加工表面粗糙度隨工藝參數(shù)變化的預(yù)測模型。試驗(yàn)結(jié)果表明,該算法可以避免BP神經(jīng)網(wǎng)絡(luò)易陷入局部極小值等問題,預(yù)測精度高,相對誤差在4%之內(nèi),進(jìn)而驗(yàn)證了該模型的可靠性。

    Abstract:

    Owing to that the function mapping relationship between the technological parameters and performance index of wire electrical discharge machining (WEDM) has a non-linear characteristic, the artificial neural networks were incorporated into WEDM calculations. To compensate the disadvantage of the conventional back propagation algorithm (CBPA), an improved learning algorithm, which trained a BP neural network by the genetic algorithm, was developed. A predictive model for surface roughness of ceramic by WEDM was developed based on the evolutionary neural networks (ENN). The results show that the ENN can effectively overcome the problems of easily falling into local minimum point and of weak global search capability. The errors between the prediction values and the practical measured ones are less than 4%.

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徐小青,駱志高,徐大鵬,丁圣銀.陶瓷材料電加工表面粗糙度的預(yù)測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2007,38(3):164-167.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(3):164-167.

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