Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture

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.

Automated summary

This paper presents a new system for thermal error prediction on heavy-duty CNC machines using LSTM networks and a fog-cloud architecture. By combining data-driven modeling with physics-based modeling, optimizing the number and location of thermal sensors, and reducing excessive data, the system improves prediction accuracy and computational efficiency. Practical case studies show that the system successfully reduces data transmission volume and improves machining accuracy.