Intelligent machining of aerospace components has created an urgent demand for online monitoring and fault prediction

particularly in five-axis milling of thin-walled parts and complex curved surfaces. Accurate milling force prediction is critical for process optimization. As the commonly used crucial technique in components milling

five-axis milling is of the advantage of excellent adaptability

enabling its application in gas turbine blade

aero-engine blade and turbine components. However

the complex process conditions and cutting geometry in five-axis milling present significant challenges for precise identification of milling force coefficients. This paper proposes an online identification method for milling force coefficients that considers cutter orientation. By analyzing the milling force signals within a single cutter rotation cycle

precise online identification of milling force coefficients is achieved. Based on a mechanical model for five-axis milling force prediction

the influence of cutter inclination is systematically considered. Analytical expressions are developed for the characteristic lines

intersection lines

and projection lines defining the cutter–workpiece contact area boundaries. Additionally

an instantaneous undeformed chip thickness model incorporating cutter inclination is introduced

leading to the construction of a five-axis milling force coefficient identification model that accounts for cutter orientation effects. Five-axis milling experiments and online monitoring of cutting forces were conducted to compare the milling force coefficient identification and prediction accuracy with and without considering cutter orientation. Error analyses were performed for different models in five-axis milling force prediction. The experimental results show that the proposed method significantly improves the accuracy of milling force prediction. This study provides an essential methodological foundation and theoretical support for online monitoring

fault prediction

and process optimization in the intelligent machining of aerospace components.