## 2 Materials and Methods
### Computational Methods
Furthermore, in the aforementioned data for chainmails bent only unidirectionally, the angle in relevant direction should be set to zero. Figure 1: Traditional ANN structure [13], **(a)** the general structure of the neural network, (**b**) inputs and outputs of the neural network. Where: MAT—type of 3D printing material (filament), SE—presence of stiffening elements in the 3D printed chainmail (yes/no), ANGX—max angle of chainmail bending in x direction, ANGY—max angle of chainmail bending in y direction, MAX—maximum tensile force, X—size of chainmail in x direction, Y—size of chainmail in y direction, PT—chainmail part type, Z—chainmail part height, LH—3D printing layer height. The initial dataset consisted of much more parameters (60 parameters taken from the printer and slicer software--e.g., layer height or seam position, whole list of this parameters is presented in [13]), but it only increased computational effort without significantly affecting the accuracy of the final result. The change in MSE for the two aforementioned neural network structures is shown in Figure 3.  
Figure 3: _Cont_. Figure 2: CNN structure: (**a**) general rule, (**b**) inputs and outputs. With DL, we can improve and optimize the stiffness of the chainmail with a stiffening element (Figure 5). Adjustment is made by setting the height and width of the stiffening element. The higher and wider the element, the more rigid it becomes. Changing the stiffness of the chainmail by changing the size of the stiffening element is shown in Figure 6. The aforementioned adaptation, especially individual adaptation, is only possible through the use of DL, as a great many internal design variables have to be taken into account to achieve such effects in 3D printing. Figure 5: Construction of a single element-stiffness adjustment by means of a stiffening element (orange): (**a**) one element, (**b**) multiple elements. Figure 6: Stiffness adjustment by means setting the height and width of the stiffening element. By using CNN to generate supports (overflows), the same effect can be achieved, but much faster. We then manipulate the stiffness of the material by adjusting the following parameters:  
* The spacing of the pattern (the spacing of the lines between the supports): the smaller spacing means the stiffer material;
* Z spacing (perpendicular to the movement of the table and the force of gravity), i.e., the vertical distance between the individual layers of the support (stiffer) and the actual model: the smaller spacing for the individual layers means the stiffer material;
* the pattern/shape of the support (stiffer). Individual pieces are assembled into chainmail. Detailed chainmail designed by DL is shown in Figure 7. Its exactness is far beyond possibilities of traditional ANN. A raft generation algorithm was used as a stiffening material. It is a material that allows the fabric to \"breathe\" (ideally to move air and water) while at the same time being easily and very well stiffened. We manipulated the stiffness of the fabric through parameters:  
* Number of raft layers: more elements mean stiffer chainmail;
* The spacing of the pattern, i.e., the spacing of the lines between the lines: the smaller the spacing the stiffer material;
* Z-axis distance (perpendicular to table movement and gravity), i.e., the vertical distance between the individual layers of the raft: the smaller spacing between each layer means the stiffer material;
* Spacing of the elements in the raft: closer elements mean the stiffer chainmail (Figure 8). * Such complicated multilayered shapes can be designed only by DL. Figure 7: Chainmail designed by DL.