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

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Harničárová M., Dütsch L., Valíček J., Kadnár M., TOZAN H., Kušnerová M.

Frontiers in Mechanical Engineering, cilt.12, 2026 (ESCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3389/fmech.2026.1815704
  • Dergi Adı: Frontiers in Mechanical Engineering
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: abrasive water jet machining, beam analogy model, multi-objective optimization, physics-informed neural network, sensitivity analysis, surface roughness prediction, uncertainty quantification, universal material parameter
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İstanbul Medipol Üniversitesi Adresli: Evet

Özet

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 (Formula presented) 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.