This study aims to establish an accurate prediction model for nonuniform tool wear in GH4169 milling by integrating process optimization and intelligent learning techniques.
A two-stage approach was used: response surface methodology (RSM) optimized cutting parameters, and a Whale Optimization Algorithm-backpropagation (WOA-BP) neural network model was built using machining angle and time to predict localized tool wear.
The proposed RSM–WOA-BP model achieved high prediction accuracy, reducing root mean square error to 1.69µm and mean absolute percentage error to 1.14%, significantly outperforming conventional BP networks in robustness and generalization.
Because the model parameters are closely related to workpiece machinability, coating wear resistance and tool–workpiece contact geometry, significant changes in workpiece material, coating system, tool diameter, cutting-edge geometry or cooling/lubrication strategy may alter the wear mechanism and the angle-dependent load distribution, leading to systematic bias if the model is directly applied. In such cases, recalibration is required. The proposed workflow is transferable to other materials and tool/coating systems, provided that necessary recalibration and validation are conducted under the new conditions.
In batch manufacturing, the machining parameters and tool type for a given operation are typically kept stable, so the calibration effort can be amortized over the production batch; the model can thus serve as a practical tool for process planning and wear monitoring.
This work integrates the strengths of RSM and WOA-BP to develop a high-accuracy model for predicting nonuniform tool wear in ball-end milling, ensuring both modeling precision and experimental efficiency. The model supports precise tool wear prediction in machining nickel-based superalloys with ball-end mills, enabling better control of tool life, cost reduction and improved reliability in complex aerospace and high-temperature applications.
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2025-0308/
