Geometric and thermal errors critically affect the precision of face gear grinding, yet current modeling approaches are computationally intensive and lack real-time adaptability. This study proposes a real-time spatiotemporal error compensation framework for face gear grinding. A closed-loop feedback mechanism is introduced to adaptively update compensation intensity based on residual error feedback, ensuring robustness and efficiency under fluctuating machining conditions. Moreover, a novel spatial-temporal thermal error model is developed by integrating Taylor-graph convolutional network and modified-long short term memory network to capture both node-level spatial fusion and long-term temporal dependencies. High-order terms in geometric error modeling are eliminated using a vector decomposition and truncation-based approach, significantly reducing computational complexity. Furthermore, a high-efficiency multi-source error-tooth flank mapping model is developed based on vector decomposition and truncation function methods, enabling accurate prediction with reduced computational cost. To identify dominant error contributors, an improved Morris-based sensitivity analysis method is integrated, distinguishing geometric and thermal errors affecting tooth flank deviation. Experimental results demonstrate sub-65 ms real-time response, 24.2 μm maximum error reduction, and robust adaptability under fluctuating machining conditions. Compared with recent gear-flank compensation studies, the proposed closed-loop framework achieves a 63.4 % reduction in maximum normal flank error under real machining and <65 ms response latency. This level is comparable to reported reductions based on grid-aggregated metrics in spiral bevel gears (76.82 % reduction of the sum of absolute grid errors), while additionally ensuring real-time, delay-aware execution. These findings validate the proposed system's potential for precision, real-time compensation in multi-axis manufacturing environments.
A real-time spatiotemporal error compensation framework for face gear grinding
Totis G.;
2025-01-01
Abstract
Geometric and thermal errors critically affect the precision of face gear grinding, yet current modeling approaches are computationally intensive and lack real-time adaptability. This study proposes a real-time spatiotemporal error compensation framework for face gear grinding. A closed-loop feedback mechanism is introduced to adaptively update compensation intensity based on residual error feedback, ensuring robustness and efficiency under fluctuating machining conditions. Moreover, a novel spatial-temporal thermal error model is developed by integrating Taylor-graph convolutional network and modified-long short term memory network to capture both node-level spatial fusion and long-term temporal dependencies. High-order terms in geometric error modeling are eliminated using a vector decomposition and truncation-based approach, significantly reducing computational complexity. Furthermore, a high-efficiency multi-source error-tooth flank mapping model is developed based on vector decomposition and truncation function methods, enabling accurate prediction with reduced computational cost. To identify dominant error contributors, an improved Morris-based sensitivity analysis method is integrated, distinguishing geometric and thermal errors affecting tooth flank deviation. Experimental results demonstrate sub-65 ms real-time response, 24.2 μm maximum error reduction, and robust adaptability under fluctuating machining conditions. Compared with recent gear-flank compensation studies, the proposed closed-loop framework achieves a 63.4 % reduction in maximum normal flank error under real machining and <65 ms response latency. This level is comparable to reported reductions based on grid-aggregated metrics in spiral bevel gears (76.82 % reduction of the sum of absolute grid errors), while additionally ensuring real-time, delay-aware execution. These findings validate the proposed system's potential for precision, real-time compensation in multi-axis manufacturing environments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
