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AbstractThis study used human DNA structure to inspire an innovative design concept. Mechanical testing assessed how design parameters and thermal shock cycles affected it. As thermal shock cycles increase, material characteristics decrease. After sixty thermal shock cycles, tensile and flexural strengths dropped 43.33% and 35.90%, respectively. DNA structures had a better strength-to-weight ratio than solids. After 60 thermal shock cycles, 2 mm ribs, straight ribs, and hexagonal honeycomb ribs had higher tensile and flexural strengths of 9.42 and 20.76 MPa, respectively. [ABSTRACT FROM AUTHOR]

Demand for the newer manufacturing techniques to produce materials with difference and specific properties are kept on increasing. The needed specific properties are obtained through composite materials by reinforcing components onto a base material to improve some critical properties. In that way, the polylactic acid (PLA) incorporated with aluminium was used to print the samples through fused filament fabrication (FFF) process. During printing, the printing process parameters have considerable effect on the material properties. At first, specimens are printed as per the Taguchi’s experimental design with L9 Orthogonal array design and the outputs are obtained experimentally. As an extension, this study proposed two neural network-based machine learning models to predict the influence of process parameters like printing temperature (PT), layer height (LH), raster angle (RA) and fill pattern (FP) on the surface finish (SF), surface finish near to the drilled holes (SFNDH), and delamination factor (DF). Both the proposed models recorded the overall prediction accuracy about 99.99%. To ensure the results predicted by the NN models, validation experiments are conducted and compared with the model predicted results. The experimental results are in good agreement with the predicted values which are obtained through numerical modelling. [ABSTRACT FROM AUTHOR]