Machining accuracy prediction and adaptive compensation method of CNC machine tool under absence of machining process sensing

Spindle axial error is the main factor restricting machining accuracy improvements of machine tools. Monitoring the machining process of computer numerical control (CNC) machine tools is challenging due to inability to reserve sensor space and interference from high-pressure coolant spray with sensor readings. This paper proposes a method for predicting machining accuracy of CNC machine tools and adaptive compensation for the absence of sensing during machining. The residual and skip connection enabled adaptive cosine annealing learning rate physics informed neural networks model reconstructs the temperature field of spindle warm-up process, with computational speed improving by 82.10% and stability by 40.68%, respectively, versus PINN models. The temperature node most correlated with axial error is identified, and its temperature process is predicted using a long short-term memory model with hyper-parameter optimization. The end condition of the warm-up occurs when the temperature reaches a specific threshold, determined by the preset machining accuracy requirement. Subsequently, the transition characteristics of temperature-error mapping relationship in the warm-up process are identified and an error prediction model is developed according to the sensing information after the turning point. Timely compensation is then performed before the accumulated prediction errors exceed the limit. Prediction and compensation effectiveness are verified on the factory machine tool, with results demonstrating that prediction accuracy improves with extended warm-up time, and machining precision enhances by 96.8% compared to conventional machining.