Accelerating development in wood‐plastic composites: Presenting key material properties and demonstrating statistical methods

Abstract In a time when global initiatives to lower CO ₂ emissions are accelerating the shift towards bio‐based products, various efforts are being made to develop wood plastic composites (WPCs) and similar bio‐composites using a variety of raw materials. This paper first showcases benchmark material properties serving as standards and models the composition, for polypropylene‐based WPCs integrating wood flour and maleic anhydride‐modified polypropylene. Subsequently, by applying statistical modeling techniques, we demonstrate an adaptive experimental design that efficiently and rapidly adjusts formulations. Beginning with an empirical dataset (24 samples), we established high‐performing models that articulate the individual contributions of the constituents on ten vital properties of WPCs. Our adaptive experimental design, leveraging nonlinear partial least squares regression and the Bayesian optimization, successfully refined formulations to enhance bending and impact strengths. Notably, we proposed optimal conditions starting from just eight samples with minimal iterations. This study shows that statistical techniques can quickly optimize WPC formulations, making the overall development process faster. By addressing the multiple conflicting properties, these techniques can greatly reduce the time and effort needed for development. Highlights Applied several regression methods to analyze ten properties of PP‐based WPCs. Showcased distinct impacts of the formulations of WF and MAPP on WPCs. Implemented the adaptive experimental design for formulation optimization. Enhanced bending and impact strengths in the formulations of WPCs. Advanced sustainable material development with data‐driven approach.