Additive manufacturing demonstrates tremendous progress and is expected to play an important role in the creation of construction materials and final products. Contactless (remote) mechanical testing of the materials and 3D printed parts is a critical limitation since the amount of collected data and corresponding structure/strength correlations need to be acquired. In this work, an efficient approach for coupling mechanical tests with thermographic analysis is described. Experiments were performed to find relationships between mechanical and thermographic data. Mechanical tests of 3D-printed samples were carried out on a universal testing machine, and the fixation of thermal changes during testing was performed with a thermal imaging camera. As a proof of concept for the use of machine learning as a method for data analysis, a neural network for fracture prediction was constructed. Analysis of the measured data led to the development of thermographic markers to enhance the thermal properties of the materials. A combination of artificial intelligence with contactless nondestructive thermal analysis opens new opportunities for the remote supervision of materials and constructions.
A simple methodology for testing FFF parts was developed to examine key factors for chemical applications. Carbon-filled polyamide (PA6-CF) was found superior for digital design of chemical reactors due to advantageous combination of properties.
Visible light irradiation of the aqueous solution of sodium alginate and organometallic complex [(C5H5)Fe(toluene)]BF4 transforms it into a rigid hydrogel due to crosslinking of carboxylate groups by the iron ions....
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