Ma Fengyuan, Wang Haoyu, E Mingfeng, Sha Zhongjin, Wang Xingshu, Cui Yunxian, Yin Junwei
School of Mechanical Engineering, Dalian Jiaotong University, Dalian, China.
Angang Heavy Machinery Co., Ltd, Anshan, China.
Front Neurorobot. 2024 Sep 27;18:1448482. doi: 10.3389/fnbot.2024.1448482. eCollection 2024.
With the development of intelligent manufacturing technology, robots have become more widespread in the field of milling processing. When milling difficult-to-machine alloy materials, the localized high temperature and large temperature gradient at the front face of the tool lead to shortened tool life and poor machining quality. The existing temperature field reconstruction methods have many assumptions, large arithmetic volume and long solution time. In this paper, an inverse heat conduction problem solution model based on Gated Convolutional Recurrent Neural Network (CNN-GRU) is proposed for reconstructing the temperature field of the tool during milling. In order to ensure the speed and accuracy of the reconstruction, we propose to utilize the inverse heat conduction problem solution model constructed by knowledge distillation (KD) and compression acceleration, which achieves a significant reduction of the training time with a small loss of optimality and ensures the accuracy and efficiency of the prediction model. With different levels of random noise added to the model input data, CNN-GRU + KD is noise-resistant and still shows good robustness and stability under noisy data. The temperature field reconstruction of the milling tool is carried out for three different working conditions, and the curve fitting excellence under the three conditions is 0.97 at the highest, and the root mean square error is 1.43°C at the minimum, respectively, and the experimental results show that the model is feasible and effective in carrying out the temperature field reconstruction of the milling tool and is of great significance in improving the accuracy of the milling machining robot.
随着智能制造技术的发展，机器人在铣削加工领域的应用越来越广泛。在铣削难加工合金材料时，刀具前刀面的局部高温和大温度梯度会导致刀具寿命缩短和加工质量变差。现有的温度场重建方法存在诸多假设、计算量大且求解时间长。本文提出一种基于门控卷积循环神经网络（CNN-GRU）的逆热传导问题求解模型，用于重建铣削过程中刀具的温度场。为确保重建的速度和准确性，我们提出利用通过知识蒸馏（KD）和压缩加速构建的逆热传导问题求解模型，该模型在最优性损失较小的情况下显著减少了训练时间，并确保了预测模型的准确性和效率。在模型输入数据中添加不同程度的随机噪声后，CNN-GRU+KD具有抗噪声能力，在噪声数据下仍表现出良好的鲁棒性和稳定性。针对三种不同工况进行了铣削刀具的温度场重建，三种工况下的曲线拟合优度最高为0.97，均方根误差最小为1.43°C，实验结果表明该模型在进行铣削刀具温度场重建方面是可行且有效的，对提高铣削加工机器人的精度具有重要意义。
