Novel beam analogy model for force distribution analysis in abrasive water jet machining

Background Abrasive water jet (AWJ) machining is a common technology in modern manufacturing due to its ability to cut any material without causing heat damage. However, many existing models require material-dependent calibration constants, making them impractical. Objective The current study proposes a beam analogy model that treats microscopic surface details as cantilever beams subjected to a constant load of the abrasive jet. Methods Based on Bernoulli-Navier beam theory, we propose a general material parameter K a w j = 10 12 / E m a t 2 to link material cuttability with Young’s modulus. The experiment conducted 750 trials over 10 different materials ( E mat = 45–210 GPa) and then combined the results with Physics-Informed Neural Network (PINN), along with sensitivity and dimensional analyses. The outcome revealed the model to be in strong agreement with the experimental results, with R 2 values of 0.94 for surface roughness and 0.91 for jet lag. Adding the PINN component improved the model’s predictive ability to R 2 = 0.97, yet it remained physically valid when extrapolating. Sensitivity analysis revealed a material-independent relative sensitivity of −2, meaning that a 1% uncertainty in Young’s modulus corresponds to a 2% uncertainty in K awj . The universal scaling law unified the results for various materials, while the depth-dependent K awj model explained the evolution of roughness with cutting depth. We further introduced dimensionless parameters Π AWJ , Γ stab , and η AWJ to describe process similarity, stability, and energy efficiency, respectively. Conclusion The method provides a physics-informed framework for AWJ process modeling, which can be used for accurate prediction and optimization of the process.