ESTIMATION OF WAVY HONEYCOMBS’ COMPRESSION PERFORMANCE VIA A MACHINE LEARNING ALGORITHM
IN THIS STUDY, TO DETERMINE THE WAVY HONEYCOMB WITH DESIRED MECHANICAL PERFORMANCE, INITIAL PEAK CRUSHING FORCE (IPCF) AND ENERGY ABSORPTION (EA) WERE ESTIMATED USING THE DECISION TREE ALGORITHM. FIRST, USING EXPERIMENTAL RESULTS IN THE LITERATURE, FINITE ELEMENT MODELS OF HEXAGONAL HONEYCOMBS WERE VERIFIED IN LSDYNA. IN THIS WAY, IT WAS FOUND THAT THE STRESS-STRAIN CURVES AND SHAPES OF DEFORMATION WERE COMPATIBLE. SECONDLY, THE EFFECT OF DESIGN PARAMETERS ON MECHANICAL PERFORMANCE WAS EXAMINED. ADDING WAVES TO HEXAGONAL HONEYCOMBS CONTRIBUTES SIGNIFICANTLY TO COMPRESSION PERFORMANCE VALUES. IN PARTICULAR, FOR
HONEYCOMBS WITH THE SAME GEOMETRIC PROPERTIES, WHEN THE WAVE NUMBER IS 3, THE IPCF AND SPECIFIC ENERGY ABSORPTION (SEA) VALUES INCREASE BY 121.59% AND 75.08%, RESPECTIVELY. IN ADDITION, WHEN THE WAVE AMPLITUDE IS 0.15MM, IPCF AND SEA INCREASE BY 60.89% AND 71.3%, RESPECTIVELY. AFTERWARD, USING THE FULL FACTORIAL METHOD, A DATA SET WITH VARIOUS DESIGN PARAMETER VALUES WAS PREPARED. TO TRAIN AND VERIFY THE DECISION TREE ALGORITHM USING PYTHON, THE DESIGN PARAMETERS (INPUTS) AND PERFORMANCE VALUES (OUTPUTS) IN THE DATA SET WERE USED. FINALLY, NEW DATA WAS INTRODUCED INTO THE ALGORITHM, AND COMPRESSION PERFORMANCE VALUES WERE ESTIMATED. ERRORS RANGED FROM 0.17% TO 14.65% BETWEEN LS-DYNA AND THE ALGORITHM RESULTS. THESE FINDINGS DEMONSTRATE THAT MACHINE LEARNING IS SUITABLE FOR ESTIMATING THE COMPRESSION PERFORMANCE OF WAVY HONEYCOMBS.
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