A novel integrated BPNN/SNN artificial neural network for predicting the mechanical performance of green fibers for better composite manufacturing

Al-Jarrah, Rami; AL‐Oqla, Faris M.

doi:10.1016/j.compstruct.2022.115475

Cited by 52 publications

(17 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess the performance of the BPNN, the GA-BPNN, and the PSO-LSVM model, three evaluation indicators, i.e., the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE), are adopted [ 20 ]. The MSE, RMSE, and MAE are calculated, as shown in Equations (9)–(11).…”

Section: Suggestions and Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Xiao

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.

show abstract

Section: Suggestions and Methodologymentioning

confidence: 99%

“…Due to the rapid development of machine learning, many intelligent algorithms have been presented to fuse several fire feature parameters [ 19 , 20 , 21 , 22 ]. This method overcomes the singularity and instability of the traditional threshold judgment method, which can significantly improve the accuracy of fire detection.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Xiao

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Biocomposite materials are aimed to be utilized in various industrials applications where mechanical characteristics including tensile strength, tensile modulus, impact strength and elongation at break are vital in achieving sustainable, functional, and dimensional stability successful green products [13][14][15][16][17][18][19]. Such mechanical features of the materials are responsible for composite behavior regarding deformations and load resistance, and thus their geometrical stability.…”

Section: Introductionmentioning

confidence: 99%

Investigating and Predicting the Effects of Fiber Chemical Composition and Treatment on the Mechanical Properties of Natural Fiber Composites by Response Surface Method

Gharaibeh¹,

AL‐Oqla²

2023

MME

View full text Add to dashboard Cite

This paper aims to integrate the response surface methodologies to investigate the effect of the cellulose and hemicellulose contents on the mechanical properties of the polypropylene-based composites with green fiber reinforcement conditions. In this study, the tested data are collected from various literature resources demonstrating the green fiber type, the chemical treatment condition, and the resultant mechanical properties, i.e., the tensile modulus and tensile strength. Accordingly, the response surface analysis is utilized to obtain a high-accuracy first order regression model to formulate the influence of the cellulose, hemicellulose, and the treatment condition on the tensile characteristics of the green composite. The results showed that the biocomposites samples with higher cellulose and lower hemicellulose percentages have significantly better tensile modulus properties. However, such samples would have lower tensile strength qualities. Additionally, the presence of chemical treatment can significantly improve the tensile properties of the polypropylene-based composites.

show abstract

“…Artificial neural network (ANN) has received growing attention in broad ranges of thermal-fluid sciences due to its ability to learn and adapt to changing conditions [1]. An ANN model consists of multiple interconnected nodes (called neurons) within multiple layers [2].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Models to Predict Heat Transfer Coefficients and Pressure Drops in Cold Plates with Surface Roughness

Randriambololona¹,

Shaeri²,

Sarabi³

2022

Proceedings of the 8th World Congress on Mechanical, Chemical, and Material Engineering

View full text Add to dashboard Cite

In the present study, artificial neural network (ANN) models are developed to predict heat transfer coefficient (ℎ) and pressure drop (∆𝑃𝑃) in cold plates (CPs) with surface roughness operating in turbulent flow. Roughness sizes range from zero (smooth surface) to 0.5 mm, and Reynolds numbers vary from 3,170 to 10,560. The RNG 𝑘𝑘 − 𝜀𝜀 model is used to simulate turbulent flow. Input data for the ANN models are prepared by simulating three-dimensional steady state turbulent flow and heat transfer inside the CPs. Separate multilayer neural networks are selected to predict ℎ and ∆𝑃𝑃. Both ANN architectures include two hidden layers with 1,024 neurons in each layer. The accuracy of the training process and the neural network is assessed by the mean absolute error. Both ANN models show excellent predictions as the predicted ℎ and ∆𝑃𝑃 are within ±1.2% and ±2.6% of the simulated values, respectively. Since roughness is an inevitable consequence of additive manufacturing, the present study suggests that accurate ANN-based models can be used as promising design tools for optimizing additively manufactured CPs. While roughness improves heat transfer, it leads to a higher pressure drop. As a result, accurate ANN models can be used to design additively manufactured cooling systems with an optimized range of roughness to improve heat transfer while operating within the allowed pressure drop and pumping power.

show abstract

A novel integrated BPNN/SNN artificial neural network for predicting the mechanical performance of green fibers for better composite manufacturing

Cited by 52 publications

References 45 publications

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Investigating and Predicting the Effects of Fiber Chemical Composition and Treatment on the Mechanical Properties of Natural Fiber Composites by Response Surface Method

Artificial Neural Network Models to Predict Heat Transfer Coefficients and Pressure Drops in Cold Plates with Surface Roughness

Contact Info

Product

Resources

About