Fast reconstruction of milling temperature field based on CNN-GRU machine learning models.

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-09-27 eCollection Date: 2024-01-01 DOI:10.3389/fnbot.2024.1448482
Fengyuan Ma, Haoyu Wang, Mingfeng E, Zhongjin Sha, Xingshu Wang, Yunxian Cui, Junwei Yin
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Abstract

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.

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基于 CNN-GRU 机器学习模型的铣削温度场快速重建。
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,刀具前端面的局部高温和较大的温度梯度会导致刀具寿命缩短和加工质量下降。现有的温度场重建方法存在假设条件多、算术量大、求解时间长等问题。本文提出了一种基于门控卷积递归神经网络(CNN-GRU)的反热传导问题求解模型,用于重建铣削过程中的刀具温度场。为了保证重构的速度和准确性,我们提出利用知识蒸馏(KD)和压缩加速构建的反热传导问题求解模型,在损失少量最优性的情况下显著缩短了训练时间,保证了预测模型的准确性和效率。在模型输入数据中加入不同程度的随机噪声后,CNN-GRU + KD 的抗噪能力很强,在高噪声数据下仍表现出良好的鲁棒性和稳定性。对三种不同工况下的铣刀温度场进行了重构,三种工况下的曲线拟合优度最高分别为 0.97,均方根误差最小分别为 1.43°C,实验结果表明该模型对铣刀温度场的重构是可行且有效的,对提高铣削加工机器人的精度具有重要意义。
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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