Abstract:
The underlying study propounds novel hyperbolic fuzzy entropy (HFE) and single valued neutrosophic entropy (NFE) based methodology for classifying the processing parameters employed for studying the wear-resistance of friction-stir-processing (FSP) of AA7075 aluminum, allow incorporated with B4C particles under different reinforcement conditions. Fast Fourier transform (FFT) was applied for the acquisition of vibration data. An alloy sheet with a thickness of 5 mm and dimensions was machined on the aluminium plates for the purpose of accommodating B4C particles. The experiments were performed at varying tool rotational speeds (1400 rpm, 1500 rpm and 1600 rpm), feed rate (30 mm/min, 40 mm/min and 50 mm/min) with plunge depth and constant tilt angles of After acquitting vibration data through FFT, the lower and upper bounds from energy eigenvalues of each processing parameters were extracted and thereafter rehabilitated into the forms of non-probabilistic sets, also called fuzzy sets (FSs) and single-valued neutrosophic sets (SVNSs) consecutively. The tool rotational speed of 1600 rpm with feed rate 30 mm/min was found to be the most superlative processing parameter owing to its maximum HFE and NFE values respectively. The wear-properties of the fabricated-samples were investigated employing pin-on tribometer. The investigations made in this study reveal that the fabricated specimen with tool rotational-speed 1600 rpm and feed-rate 30 mm/min was having higher wear resistance and coefficient-of-friction (COF). The proposed entropy-based method of classification of processing parameters can help the readers to improve surface integrity and enhancement of mechanical & chemical properties of the selected aluminium alloy as well as other related metal composites.
