{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:04:12Z","timestamp":1789981452234,"version":"4.0.1"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>Cavitation bubble collapse generates impact loads that can initiate surface damage and accelerate material degradation in hydraulic and fluid-handling systems. Predicting these loads remains challenging because the collapse response depends on nonlinear interactions among bubble dynamics, stand-off distance, and polymer mechanical, acoustic, and viscoelastic properties. This study developed machine-learning regression models to predict impact loads from laser-induced single-bubble collapse on polyethylene, polytetrafluoroethylene (PTFE), polyamide, and antistatic polyethylene terephthalate (antistatic PET). Experiments were performed using a pulsed Nd:YAG laser at energies of 12.5, 25, and 50 mJ and normalized stand-off distances of \u03b3 = 1\u20135, producing 180 measurements. Impact loads were measured using a force-calibrated PVDF sensor with material-specific calibration equations. Bubble radius was obtained from high-speed imaging, whereas collapse time was determined from the PVDF voltage-time response. Thirteen input variables were used to train linear, ridge, LASSO, multilayer perceptron, and random forest regressors with Bayesian optimization and grouped five-fold cross-validation. Random forest regression achieved the best performance, with RMSE=0.6820N, MAE=0.5339N, R2=0.9168, and MAPE=12.44%. SHAP analysis identified collapse time, acoustic impedance, and loss modulus as dominant predictors. The model is therefore suitable for trend prediction within the tested experimental domain.<\/jats:p>","DOI":"10.3390\/mi17091083","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:23:53Z","timestamp":1789496633000},"page":"1083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Impact Loads on Polymer Materials from Laser-Induced Cavitation Bubble Collapse Using Random Forest Regression"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1744-154X","authenticated-orcid":false,"given":"Muhammad Farkhan","family":"Abdillah","sequence":"first","affiliation":[{"name":"Department of Transdisciplinary Science and Engineering, Institute of Science Tokyo, Tokyo 152-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Kazuaki","family":"Inaba","sequence":"additional","affiliation":[{"name":"Department of Transdisciplinary Science and Engineering, Institute of Science Tokyo, Tokyo 152-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S1001-6058(16)60638-8","article-title":"A Review of Cavitation in Hydraulic Machinery","volume":"28","author":"Luo","year":"2016","journal-title":"J. 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