Enhancing four-axis machining center accuracy through interactive fusion of spatiotemporal graph convolutional networks and an error-controlled digital twin system

Mitigating thermal errors constitutes a crucial method for enhancing the machining accuracy of four-axis machining centers. At the heart of effective thermal error control lie the thermal error control platform and a resilient thermal error prediction model. It is imperative to note that thermal errors exhibit intricate dynamic and nonlinear spatiotemporal dependencies. However, prevailing thermal error prediction models tend to primarily focus on temporal features or employ simplistic spatiotemporal characteristics, resulting in diminished accuracy and robustness. Furthermore, the present thermal error compensation system is plagued by a lack of user-friendliness, stemming from its suboptimal execution efficiency. In response to the aforementioned challenges, an innovative approach: the interactive fusion spatiotemporal graph convolutional network is proposed. This novel model is specifically designed to capture the intricate dynamic spatiotemporal dependencies inherent in thermal errors. The interactive fusion spatiotemporal graph convolutional network model consists of three essential components: a bilinear temporal convolutional network, a multi-layer spatiotemporal module, and a linear module. These components work in harmony to comprehensively extract both global and local spatiotemporal features. Subsequently, a mapping relationship between thermal errors and compensation components is established, laying the foundation for theoretical advancements in thermal error compensation within the realm of four-axis machining centers. A digital twin system framework tailored for error control is devised, which leverages cloud-edge computing to enable dynamic control and real-time monitoring of thermal errors. To assess the effectiveness of this digital twin system framework and the interactive fusion spatiotemporal graph convolutional network model, a series of rigorous experiments were conducted. The oriented to error-controlled digital twin system coupled with the interactive fusion spatiotemporal graph convolutional network model yielded exceptional machining accuracy, resulting in minimal geometric disparities of [−3.0 μm, 3.0 μm] for the central hole diameter D and [−3.5 μm, 4.0 μm] for the hole distance H.