## 3 Results and Discussion
### Composite Performance Tuning
For layer 1 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\) with a maximum row sum of 0 and column sum of 0. For layer 2 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\) with a maximum row sum of 0 and column sum of 0. For slices parallel to the Y-axis, we have a maximum layer sum of 0. Thus, the sum of row/column/layer sum maxima is \(0+0+0+0+0=0\). Arrangement P2:  
Two matrices orthogonal to the Z-axis could be formed. For layer 1 along the Z-axis, we have the feature matrix \(\left[ \begin{array}{cc}1&1\\ 0&0\end{array} \right]\) with a maximum row sum of 2 and column sum of 1. For layer 2 along the Z-axis, we have the feature matrix \(\left[ \begin{array}{cc}1&1\\ 0&0\end{array} \right]\) with a maximum row sum of 2 and column sum of 1. For slices parallel to the Z-axis, we have a maximum layer sum of 2. Thus, the sum of row/column/layer sum maxima is \(2+1+2+1+2=8\). Two matrices orthogonal to the Y-axis could be formed. For layer 1 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}1&1\\ 1&1\end{array} \right]\) with a maximum row sum of 2 and column sum of 2. For layer 2 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\) with a maximum row sum of 0 and column sum of 0. For slices parallel to the Y-axis, we have a maximum layer sum of 1. Thus, the sum of row/column/layer sum maxima is \(2+2+0+0+1=5\). Arrangement P3:  
Two matrices orthogonal to the Z-axis could be formed. For layer 1 along the Z-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&1\\ 1&0\end{array} \right]\) with a maximum row sum of 1 and column sum of 1. For layer 2 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&1\\ 0&0\end{array} \right]\) with a maximum row sum of 0 and column sum of 0. For slices parallel to the Y-axis, we have a maximum layer sum of 1. Thus, the sum of row/column/layer sum maxima is \(2+2+0+0+1=5\). Arrangement P3:  
Two matrices orthogonal to the Z-axis could be formed. For layer 1 along the Z-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&1\\ 1&0\end{array} \right]\) with a maximum row sum of 1 and column sum of 1. For layer 2 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&1\\ 0&0\end{array} \right]\) with a maximum row sum of 1 and column sum of 1.  
the Z-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&1\\ 1&0\end{array} \right]\) with a maximum row sum of 1 and column sum of 1. For slices parallel to the Z-axis, we have a maximum layer sum of 2. Thus, the sum of row/column/ layer sum maxima is \(1+1+1+1+2=6\). Two matrices orthogonal to the Y-axis could be formed. For layer 1 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\) with a maximum row sum of 0 and column sum of 0. For layer 2 along the Y-axis, we have the feature matrix \(\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\) with a maximum row sum of 0 and column sum of 0. For slices parallel to the Y-axis, we have a maximum layer sum of 0. Thus, the sum of row/column/layer sum maxima is \(0+0+0+0+0=0\). The sums of maxima are plotted in Figure 6a,b in the red line. Comparing these sums with surface displacement under compression, the sums of maxima were inversely proportional to the displacement and are thus proportional to compressive stiffness. In other words, composites with larger sums of maxima should be more resistant to compression. Therefore, these results have shown that the mechanical properties, specifically compressive stiffness, of wood-inspired composites could be designed by arranging and combining the elementary models in particular orders.