Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures.

Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel × 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

The Mechanical Properties of 3D-Printed Polylactic Acid/Polyethylene Terephthalate Glycol Multi-Material Structures Manufactured by Material Extrusion.

The mechanical properties of polylactic acid (PLA), polyethylene terephthalate glycol (PETG), and PLA/PETG structures manufactured using the multi-material additive manufacturing (MMAM) method were studied in this work. Material extrusion additive manufacturing was used to print PLA/PETG samples with various PLA and PETG layer numbers. By varying the top and bottom layer numbers of two thermoplastics, the effect of layer number on the mechanical properties of 3D-printed structures was investigated. The chemical and thermal characteristics of PLA and PETG were investigated using Fourier transform infrared spectroscopy and differential scanning calorimetry. Tensile and flexural strength of 3D-printed PLA, PETG, and PLA/PETG samples were determined using tensile and three-point bending tests. The fracture surfaces of the samples were evaluated using optical microscopy. The results indicated that multi-material part containing 13 layers of PLA and 3 layers of PETG exhibited the highest ultimate tensile strength (65.4 MPa) and a good flexural strength (91.4 MPa). MMAM was discovered to be a viable way for producing PLA/PETG materials with great mechanical performance.