Abstract Heavy-duty CNC machines are important equipment in manufacturing large-scale and high-end products. During the machining processes, a significant amount of heat is generated to bring working temperatures rising, which leads to deformation of machine elements and further machining inaccuracy. In recent years, data-driven approaches for predicting thermal errors have been actively developed to adaptively compensate the errors on the fly to improve machining accuracy. However, it is challenging to adopting the approaches to support heavy-duty CNC machines due to their low efficiency in processing large-volume thermal data. To tackle the issue, this paper presents a new system for thermal error prediction on heavy-duty CNC machines enabled by a Long Short-Term Memory (LSTM) networks and a fog-cloud architecture. Innovative characteristics of the system include the following aspects: (1) data-based modelling is augmented with physics-based modelling to optimise the number/locations of thermal sensors deployed onto machine elements and minimise excessive data to facilitate computation; (2) a LSTM networks with a data pre-processor is developed for modelling thermal errors more effectively in terms of prediction accuracy and computational efficiency; (3) A fog-cloud architecture is designed to optimise the volume of transferred data and overcome low latency of the system. The system was validated using an industrial heavy-duty CNC machine. Practical case studies show that the system reduced the volume of transmitted data for 52.63 % and improved the machining accuracy for 46.53 %, in comparison with the processes without using the designed system.

中文翻译：

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长短期记忆网络和雾云架构支持重型数控机床的热误差预测

摘要 重型数控机床是制造大型高端产品的重要设备。在加工过程中，会产生大量热量，使工作温度升高，从而导致机器元件变形和进一步加工不准确。近年来，已经积极开发了用于预测热误差的数据驱动方法，以自适应地即时补偿误差以提高加工精度。然而，由于重型数控机床处理大量热数据的效率低下，采用这些方法来支持重型数控机床具有挑战性。为了解决这个问题，本文提出了一种新系统，用于通过长短期记忆 (LSTM) 网络和雾云架构对重型 CNC 机器进行热误差预测。该系统的创新特点包括以下几个方面：（1）基于数据的建模与基于物理的建模相结合，以优化部署在机器元件上的热传感器的数量/位置，并最大限度地减少过多的数据以方便计算；(2) 开发了带有数据预处理器的 LSTM 网络，以便在预测精度和计算效率方面更有效地对热误差进行建模；(3) 雾云架构旨在优化传输的数据量并克服系统的低延迟。该系统使用工业重型数控机床进行了验证。实际案例研究表明，与未使用设计系统的工艺相比，该系统减少了52.63%的传输数据量，提高了46.53%的加工精度。