高能喷丸处理后TC17合金表层显微硬度预测模型（英文）

发布时间：2018-03-04 02:34

本文选题：TC合金　切入点：高能喷丸　出处：《Transactions of Nonferrous Metals Society of China》2017年09期 　论文类型：期刊论文

【摘要】：通过高能喷丸对TC17合金进行表面处理,喷丸空气压力为0.35～0.55 MPa,喷丸时间为15～60 min。测量了TC17合金高能喷丸处理后最表层至基体的显微硬度。测量结果表明,显微硬度随深度的增大而逐渐减小,且不同深度处显微硬度随空气压力与喷丸时间的变化各不相同。建立了TC17合金高能喷丸处理后表层显微硬度的模糊神经网络模型。借助该模型,显微硬度的预测值与测量值的最大相对误差为8.5%,平均误差为3.2%。基于模糊神经网络模型,研究了空气压力与喷丸时间对TC17合金高能喷丸处理后不同深度处显微硬度的影响。结果表明,细化层的脱落与连续的晶粒细化作用之间有显著的交互作用。
[Abstract]:The surface of TC17 alloy was treated by high energy shot peening. The air pressure of shot peening was 0.35 ~ 0.55 MPa and shot peening time was 1560 min. The microhardness from the topmost layer to matrix of TC17 alloy was measured after high energy shot peening treatment. The microhardness decreases with the increase of depth. The variation of microhardness at different depth with air pressure and shot peening time is different. A fuzzy neural network model of surface microhardness of TC17 alloy after high energy shot peening is established. The maximum relative error between the predicted value and the measured value of microhardness is 8.5, and the average error is 3.2. Based on the fuzzy neural network model, The effects of air pressure and shot peening time on microhardness at different depths after high energy shot peening of TC17 alloy were studied. The results show that there is a significant interaction between the shedding of the fine layer and the continuous grain refinement.
【作者单位】：西北工业大学材料学院;
【基金】：Project (51475375) supported by the National Natural Science Foundation of China
【分类号】：TG146.23;TG668

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