(中南大學 材料科學與工程學院，長沙 410083)
摘 要: 在變形溫度為200~400 ℃、應變速率為0.001~1 s−1條件下，對ZK60鎂合金進行熱壓縮實驗，建立一個單隱層前饋誤差反向傳播人工神經(jīng)網(wǎng)絡模型，研究該鎂合金的流變行為。模型的輸入?yún)?shù)分別為變形溫度、應變速率和應變，輸出為流變應力，中間隱含層包含23個神經(jīng)元，并采用Levenberg-Marquardt算法對此網(wǎng)絡模型進行訓練。結果表明：ZK60鎂合金的流變應力隨變形溫度升高和應變速率降低而減小；其高溫壓縮流變應力曲線可描述為加工硬化、過渡、軟化和穩(wěn)態(tài)流變4個階段，但在較高溫度和較低應變速率時，過渡階段不很明顯；所建神經(jīng)網(wǎng)絡模型可以很好地描述ZK60鎂合金的流變應力，其預測值與實驗值吻合很好；利用該模型預測的變形溫度和應變速率對流變應力的影響結果與一般熱加工理論所得結果一致。
關鍵字: ZK60鎂合金；人工神經(jīng)網(wǎng)絡；流變應力；熱壓縮變形
（School of Materials Science and Engineering, Central South University, Changsha 410083, China）
Abstract:Compression tests for ZK60 magnesium alloy were carried out in the temperature range of 200−400 ℃ and strain rate range of 0.001−1 s−1. A feed-forward back-propagation artificial neural network with single hidden layer was established to investigate the flow behavior of this magnesium alloy. The input parameters of the model were temperature, strain rate and strain while flow stress was the output. A network contains 23 neurons in the hidden layer, and Levenberg-Marquardt training algorithm was employed. The results show that the flow stress of the ZK60 magnesium alloy decreases with increasing deformation temperature and decreasing strain rate. The flow stress curves obtained from the experiments are composed of four different stages, such as work hardening stage, transition stage, softening stage and steady stage. While for the relatively high temperature and low strain rate, the transition stage is not very obvious. The proposed model can describe the flow behavior of the ZK60 magnesium alloy precisely, the predicted results agree with the experimental values. The predicted results of the effect of deformation temperature and strain rate on the flow behavior of the ZK60 alloy are consistent with what is expected from he fundamental theory of hot compression deformation.
Key words: ZK60 magnesium alloy; artificial neural network; flow stress; hot compression deformation
