Data-driven analysis of bonding strength in laser-structured metal-GFRP hybrid joints via groove morphology

Abstract This study aims to enhance predictions of the mechanical properties of mechanically interlocked hybrid joints by employing machine learning techniques coupled with feature engineering of cross-sectional groove morphology. Unlike mechanical fastening, which promotes localized stress, and adhesive bonding, which requires prolonged contaminant removal, mechanically interlocked joints offer a distinct advantage by eliminating the need for either. The mechanically interlocked joints in this study combine glass fiber reinforced composite fabricated via injection molding, with cold rolled steel structured by a nanosecond laser. Through optical microscopy, crucial groove dimensions such as depth and width are identified for feature extraction. Domain-specific feature engineering is employed to improve predictive accuracy, integrated with existing regression models. The concept of “structure density,” initially defined as groove width over hatch distance, is expanded during feature engineering to include additional relevant features over hatch distance. Experimental investigations identified optimal laser parameters for shear strength, yielding a maximum single lap shear strength of 33.3 MPa under specific conditions. The third polynomial regression model incorporating structure density features emerged as the most effective in predicting shear strength, demonstrating high accuracy in both interpolation and extrapolation scenarios. The study suggests potential cost savings by utilizing surface topography for shear strength prediction, with implications for industries amidst the increasing prevalence of composite materials.