Tool Wear Prediction in Glass Fiber Reinforced Polymer Small-Hole Drilling Based on an Improved Circle Chaotic Mapping Grey Wolf Algorithm for BP Neural Network
نویسندگان
چکیده
Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality directly affected by the processing technology and tool life; burrs, tearing, delamination other defects will reduce service life of GFRP structural parts. Through damage wear experiments GFRP, thrust force, vibration amplitude, number processed holes, feed rate cutting speed were found to be main factors in wear. Using those as input layer, prediction model was established based on an improved circle chaotic mapping (CCM) Grey Wolf algorithm for back propagation (BP) neural network. Compared with original BP network, maximum error network reduced 71.2% root mean square (RMS) 63.82%. The factor at entrance less than 3%, exit 1%. results showed that optimized can better predict wear, had higher accuracy, optimization efficiency robustness ordinary
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13052811