Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm

Abstract

Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms.

Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study

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A review on machine learning implementation for predicting and optimizing the mechanical behaviour of laminated fiber-reinforced polymer composites

“…Altabey et al [ 61 ] employed ML in their search for the optimum geometrical parameters that maximize the transmission loss factor in laminated composite mufflers. They developed a model of a dual-chamber laminated composite muffler (DCLCM) in MATLAB to generate their acoustic behavior dataset.…”

Research of pavement crack detection system based on image processing

“…Several researchers in the fields of pavement crack identification algorithm and system integration research and development domain have also carried out exploratory research, by continuously improving the algorithm and system tests such as wavelet transform, block processing, and other methods to solve the problem of obtaining crack data [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] Wang et al [57] proposed an image segmentation method based on shape features, Martin et al [58] consider the crack region combined with boundary information to detect crack edges, this class is based on edge features. The crack identification algorithm is very important for the detection of pavement cracks.…”