Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning

Abstract

The quality of multiple-machining-feature parts in batch computer numerical control machining systems is affected by complex spatial-temporal relationships, making accurate prediction difficult. Traditional quality prediction methods are difficult to cope with the challenges posed by the time impact effects generated by batch production sequences and the spatial impact effects generated by the adjacency of machining features of thin-walled parts. Hence, the time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning is proposed to analyze the spatial-temporal impact relationship of machining quality and achieve accurate quality prediction. Specifically, a graph learning-based framework to capture the coupled spatial-temporal effects is designed to represent the spatial topological and temporal relationship of the machining features of batch parts. A novel global-local multi-task spatial-temporal graph learning framework is constructed to simultaneously predict the time series quality of each machining feature at different spatial positions. The global multi-task model aims to extract spatial-temporal coupling data features (i.e., input parameters for machining features at different positions of batch parts) for machining feature clusters based on gated networks and spatial-temporal graph convolution with multi-head attention. Then, the local multi-task model for each feature cluster (round holes, rectangular grooves, etc.) performs individual time series quality prediction regarding multiple quality indicators. Experimental results on a production dataset of a computer numerical control machining system for thin-walled parts show that the proposed method is superior to the conventional models such as a simple spatial-temporal graph convolution method (3.0%, 18.3% and 11.0% improvement in R2, MAE and RMSE) or the model without graph approach (6.5%, 29.8% and 19.8% improvement in R2, MAE and RMSE).