Abstract
To rapidly predict the thrust force with a tapered drill-reamer, this study develops an integrated methodology coupling a scale-span model and revised artificial neural networks (ANN) in the drilling of carbon fiber–reinforced polymers (CFRPs). First, the optimum mesh size of the scale-span finite element (FE) model of CFRPs was obtained to enhance simulation efficiency on the premise of ensuring accuracy in drilling. Then, an order-driven FE computation approach was first proposed to improve computing efficiency for batch samples and maximize utilization of the available computing resources. Modeling and solving of the weight indices of material property parameters (MPPs) and machining parameters for the thrust force were first carried out entirely based on a feature selection model. A multi-layer revised ANN architecture model, which considers the material properties of CFRPs and the corresponding initial weight indices, was first designed for the thrust force prediction in Python software. Finally, drilling experiments involving T700S-12K/YP-H26 CFRPs specimens with different machining parameters were carried out. The prediction results showed that the established ANN prediction model with a 16-18-18-18-16-1 architecture has excellent prediction precision, and the maximum absolute deviation is only 4.56% with the comparisons of experiments.
Highlights
Carbon fiber reinforced polymers (CFRPs) possess attractive characteristics such as their high strength-to-weight ratios and high specific stiffness-to-weight ratios compared with metallic materials [1,2]
This study develops an integrated methodology to rapidly predict the thrust force with a tapered drill-reamer (TDR) by coupling a scale-span model and revised artificial neural networks (ANN) in drilling carbon fiber reinforced polymers (CFRPs)
These excellent properties account for manufacturing of advanced structures with CFRPs in aviation, aerospace and national defense industries, where the drilling of the structural parts is frequently encountered for either manufacturing riveted assemblies or structural repairs [3]
Summary
Carbon fiber reinforced polymers (CFRPs) possess attractive characteristics such as their high strength-to-weight ratios and high specific stiffness-to-weight ratios compared with metallic materials [1,2]. Based on the aforementioned studies, an effective methodology based on coupling of a previously established scale-span drilling FE model [24] and a revised ANN model which considers the material properties of CFRPs and the corresponding initial weight indices is developed to rapidly predict the thrust force with a tapered drill-reamer (TDR) under different machining parameters in this study. The optimum global mesh size of the scale-span FE model of CFRPs is optimized to obtain the minimum calculation time on the premise of ensuring accuracy of the predicted thrust force, while an order-driven FE computation approach are developed for the batch solution of samples. (II) Implement of thrust force prediction Based on the established multi-layer perceptron ANN model, neurons in the input layer correspond to the elastic and strength parameters of CFRPs and machining parameters which are reported in Tab.. Based on the well-trained ANN model, a series of samples based on the machining parameter variation of the machine are regarded as the fresh samples to verify the correctness of the prediction results
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