Bio-based composites have been a viable material choice in aerospace, automobile and construction industries over the past few decades. From day-to-day products like spoons and chairs to the construction of rocket parts, bio-based composites find their applications due to their mechanically enhanced, cost-effective and lightweight structures, with a reduced carbon footprint. This study collaborates with NPSP, one of the companies leading the way towards a circular economy.
However, the manufacturing and testing of bio-based composites is time and resource expensive. Moreover, exploring all natural fillers and fibres ratios is not feasible experimentally. Optimization and analytical models are two potential approaches that accelerate the search towards an optimal bio-based composite recipe when combined with bio-based materials research. A data-scarce Bayesian optimization model was already developed to research the composition of bio-based composites. The proof-of-concept program adjusts the natural materials’ weight ratios to optimize toward user-defined mechanical properties. The objective of this study is to experimentally investigate if the Bayesian Optimization model works by varying the objective functions and adapting the model according to the clients’ needs. By exploiting the machine learning model at an initial stage, the purpose is to test how the model reacts to different objective functions defined at high weight values and observe if the model can generate optimized recipes with better mechanical properties than the defined training set. For this study, four different fillers (calcite, lignocellulosic filler 1, lignocellulosic filler 2, waste-based filler) and two different fibres (flax, bamboo) are used, with calcite as the reference filler. A primary goal to be achieved by NPSP is to reduce or eliminate calcite as the primary filler due to its high density and brittle nature. Promising results have been obtained within two iterations of using the model for the different filler/fibre systems used.
Additionally, the thesis also investigates the rules of mixing for multi-component systems to develop analytical models that predict the mechanical properties of the composite. The Lewis-Nielsen model and the Cox -Krenchel theory have been used to compare theoretical values with experimental data points. The initial binary phase curve fitting followed by applying cascading to obtain ternary and quaternary phase mechanical properties provides approximate results for most recipes made. Finally, recommendations to improve both models have been covered, and a potential to combine both approaches has been shown.