孔德瑜,晏洋,张浩,等.基于GA-BP神经网络晶粒尺寸预测模型的轮端轮毂锻造工艺优化[J].精密成形工程,2024,16(3):44-51.
KONG Deyu,YAN Yang,ZHANG Hao,et al.Optimization of Wheel End Hub Forging Process Based on GA-BP Neural Network Grain Size Prediction Model[J].Journal of Netshape Forming Engineering,2024,16(3):44-51. |
| 基于GA-BP神经网络晶粒尺寸预测模型的轮端轮毂锻造工艺优化 |
| Optimization of Wheel End Hub Forging Process Based on GA-BP Neural Network Grain Size Prediction Model |
| 投稿时间：2024-01-15 |
| DOI：10.3969/j.issn.1674-6457.2024.03.004 |
| 中文关键词: 轮端轮毂 晶粒尺寸预测 遗传算法 神经网络 数值模拟 |
| 英文关键词: wheel end hub grain size prediction genetic algorithm neural network numerical simulation |
| 基金项目:国家重点研发计划(2022YFB3706903);国家自然科学基金(52090043) |
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| 中文摘要: |
| 目的 针对6082铝合金轮端轮毂在热处理过程中出现的粗晶问题，利用基于遗传算法优化的BP神经网络晶粒尺寸预测模型模拟优化锻造工艺方案，避免产生粗晶。方法 以遗传算法替代梯度下降法优化神经网络各节点的权值和阈值，建立高精度的GA-BP神经网络晶粒尺寸预测模型，再以轮端轮毂为对象，设计锻造工艺方案并利用Deform进行微观组织仿真，研究压下速率、坯料初始温度对晶粒尺寸的影响，获得最优方案。结果 优化模型预测的晶粒尺寸平均值和最大值的平均绝对百分比误差分别为2.55%、0.43%，与常规的BP神经网络相比，准确性有了较大提高。对比不同锻造方案的结果，得到轮毂较优的初始坯料温度为500 ℃，压下速率为200 mm/s，经试验验证，锻件特征位置的晶粒尺寸预测值与实际值之间的误差均在10%以下，表明该预测模型具有良好的工程应用价值。结论 遗传算法的引入大大增强了BP神经网络的全局寻优能力，提高了模型的准确性。在Deform中复现的预测模型对锻件的晶粒尺寸分布有较好的预测效果，并基于此成功模拟、优化了轮端轮毂的锻造方案。 |
| 英文摘要: |
| Aiming at the coarse grains of the 6082 aluminum alloy wheel end hub occurring during heat treatment, the work aims to simulate and optimize the forging process with the BP neural network grain size prediction model based on genetic algorithm optimization so as to avoid coarse grains. The weights and thresholds of each node in the neural network were optimized by using the genetic algorithm instead of the gradient descent method. A GA-BP neural network grain size prediction model with high precision was established. Subsequently, taking the wheel end hub as the object, different forging process schemes were designed and microstructure simulation was conducted using Deform to investigate the impact of compression rate and initial billet temperature on grain size, and obtain the optimal scheme. The mean absolute percentage error of the average and maximum grain size predicted by the optimized model were 2.55% and 0.43%, respectively, which was a significant improvement in accuracy compared with the conventional BP neural network. The optimal initial billet temperature of the wheel end hub was determined to be 500 ℃, with a compression rate of 200 mm/s, based on comparative analysis of different forging schemes. The experimental results demonstrated that the error between the predicted and the actual grain size of the characteristic position was less than 10%, which indicated that the prediction model had good engineering application value. The introduction of genetic algorithm greatly enhances the global optimization ability of the BP neural network and improves the accuracy of the model. The prediction model reproduced in Deform has a good prediction effect on the grain size distribution of forgings, and based on this, the forging scheme of wheel end hub is successfully simulated and optimized. |
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