Machine learning guided optimization of engineered wood dust reinforced hybrid polymer composites

Within this study, the modification of engineered wood dust-epoxy (EWD-EP) composite with the addition of Linz-Donawitz (LD) sludge as a filler to improve physical, mechanical properties, and abrasion resistance of composites was investigated. The neat epoxy has a density of 1100 kg/m3 and it decreased with the addition of EWD and LD sludge addition. A clear improvement in tensile, flexural, and impact strengths, along with micro-hardness, is observed as the LD sludge content increases. The tensile strength rises to a high from 48.0 MPa for neat epoxy (B0) to 62.3. MPa for 10 wt. % EWD and 10 wt. % LD sludge addition (B3). Similarly, the flexural strength rose from 22.5 MPa for B0 to 27.9 MPa for 10 wt. % EWD and 15 wt. % LD sludge addition (B4). The impact strength and micro-hardness also improved significantly, increasing from 12.2 kJ/m² and 13.3 Hv for the neat epoxy to 22.7 kJ/m² and 23.9 Hv for B3 composite. Taguchi design showed a significant improvement in abrasion resistance corresponding to the increase in LD sludge content. The results obtained from machine learning demonstrate a clear alignment between the model and the experimental findings.