Abstract
Surface roughness is critical for bonding applications, as it directly influences the mechanisms occurring at the adhesive interface. Abrasive Waterjet texturing has emerged as a promising technique for functionalizing surfaces, but predicting the surface characteristics from stochastic impact-based processes remains a challenge. This study aimed to develop a numerical model capable of forecasting key morphological parameters for AWJ-textured surfaces with pilotable treatment coverage. The proposed model was optimized through theoretical analysis and confronted to topographical data from polymer samples treated with low-density AWJ using standard parameters. Profilometry measurements were supported by a custom post-treatment algorithm to remove artefacts and assess the characteristics of individual particle impacts (number, repartition, dimensions). The predicted roughness showed a 94 % concordance to the measured values.
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