热成形-淬火和热处理工艺对 AA6016 铝合金板材机械性能的影响

Metals Pub Date : 2024-05-20 DOI:10.3390/met14050599
Jiahong Lu, Baitong Liu, Shiyao Huang, Zuguo Bao, Yutong Yang, Xilin Li, Zhenfei Zhan, Qing Liu
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引用次数: 0

摘要

本研究探讨了热成型-淬火(HFQ)和热处理工艺对 AA6016 板材机械性能的影响。实验结果表明,在 15%的高温预应变(HT-PS)条件下,随着热处理温度和持续时间的逐渐增加,AA6016 板材的强度性能有所提高。相反,在 3% 和 7% 的 HT-PS 条件下,热处理过程对 AA6016 板材机械性能的影响相对较小。为了了解不同工艺条件对析出相的影响,我们采用了差示扫描量热法(DSC)。通过比较 HT-PS 为 3% 和 15% 时 β″ 相的析出峰,可以发现随着 HT-PS 的增加,β″ 相的析出峰降低。这表明 HT-PS 促进了 β″ 相的析出。为了预测施加各种预拉伸和热处理参数后 AA6016 板材的机械性能,使用了两个模型:反向传播(BP)神经网络和遗传算法(GA)-BP 神经网络。对这些模型的拟合和预测能力进行了评估。研究结果表明,与 BP 神经网络模型相比,GA-BP 神经网络模型具有更高的拟合和预测精度。
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The Effect of Hot Forming–Quenching and Heat Treatment Processes on the Mechanical Properties of AA6016 Aluminum Alloy Sheets
This study explored the impact of Hot Forming–Quenching (HFQ) and heat treatment processes on the mechanical properties of AA6016 sheets. The experimental findings demonstrated that at high-temperature pre-straining (HT-PS) of 15%, the strength performance of the AA6016 sheet exhibited enhancement, with a progressive increase in both the heat treatment temperature and duration. Conversely, under HT-PS conditions of 3% and 7%, the heat treatment process exhibited a relatively modest impact on the mechanical properties of the AA6016 sheet. Differential scanning calorimetry (DSC) was employed to understand the influence of different process conditions on the precipitated phases. By comparing the precipitation peaks of the β″ phase at HT-PS of 3% and 15%, it was observed that the precipitation peak of the β″ phase decreased with an increase in HT-PS. This indicated that HT-PS promoted the precipitation of the β″ phase. In order to forecast the mechanical performance of the AA6016 sheets after applying various pre-straining and heat treatment parameters, two models were used: a backpropagation (BP) neural network and a genetic algorithm (GA)-BP neural network. These models were evaluated for their fitting and predictive capabilities. The research findings demonstrated that the GA-BP neural network model exhibited superior fitting and predictive accuracy compared to the BP neural network model.
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