First-ply failure prediction of glass/epoxy composite pipes using an artificial neural network model

Ang, Junwei; Majid, M.S. Abdul; Nor, Azuwir Mohd; Yaacob, Sazali; Ridzuan, M.J.M.

doi:10.1016/j.compstruct.2018.05.139

Cited by 34 publications

(7 citation statements)

References 27 publications

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“…Note that accelerating modeling of composite materials and structures are not restricted to multiscale constitutive modeling. The ANN models have been applied to many other problems in the modeling of composite materials and structures such as predicting ballistic limit [147], first-ply failure [148], and the effects of embedded defects and features [149]. The major challenge is to reduce the cost of data generation from physics-based models and add interpretability to the ANN model.…”

Section: Discussionmentioning

confidence: 99%

How machine learning can help the design and analysis of composite materials and structures?

Liu¹,

Tian²,

Tao³

et al. 2020

Preprint

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Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. Artificial neural networks (ANN) is the most popular machine learning model in recent years. Although many ANN models have been used in the design and analysis of composite materials and structures, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posting new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the nonlinear constitutive modeling, multiscale surrogate modeling, and design optimization of composite materials and structures. This review has been designed to focus on the discussion of the general frameworks and benefits of ANN models to the above problems. Moreover, challenges and opportunities in each key problem are identified and discussed. This paper is expected to open the discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.

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Section: Discussionmentioning

confidence: 99%

How machine learning can help the design and analysis of composite materials and structures?

Liu¹,

Tian²,

Tao³

et al. 2020

Preprint

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“…Generally, FRP composite pipes have been produced using two distinct production methods namely filament winding method (FWM) and rotary centrifugal winding (RCW). To best of our knowledge, any long fibres in form of roving, chopped strand mats and any thermoset polymers can be used as source materials in FWM [14,15]. Furthermore, the advantages such as production of larger pipes, hollow pipes, fibre arrangement, high mechanical performance, minimum production time attract the manufacturers towards this process [16][17][18].…”

Section: Discussionmentioning

confidence: 99%

An overview of burst, buckling, durability and corrosion analysis of lightweight FRP composite pipes and their applicability

Prabhakar

Rajini

Ayrılmış

et al. 2019

Composite Structures

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“…Data preparation is important for ensuring the built model's accuracy and robustness. The accuracy of the collected data influences the precision, and the amount of data used to train the model affects robustness [19]. As mentioned above, the data were collected from the extensive database of Sandia National Laboratories to compare four types of thermosetting resins (cited in Table 1) in terms of fatigue lives and hygrothermal effect.…”

Section: Data Preparationmentioning

confidence: 99%

Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions

Ziane

Ilinca

Karganroudi

et al. 2021

Eng

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Moisture and temperature are the most important environmental factors that affect the degradation of wind turbine blades, and their influence must be considered in the design process. They will first affect the resin matrix and then, possibly, the interface with the fibers. This work is the first to use a series of metaheuristic approaches to analyze the most recent experimental results database and to identify which resins are the most robust to moisture/temperature in terms of fatigue life. Four types of resin are compared, representing the most common types used for wind turbine blades manufacturing. Thermoset polymer resins, including polyesters and vinyl esters, were machined as coupons and tested for the fatigue in air temperatures of 20 °C and 50 °C under “dry” and “wet” conditions. The experimental fatigue data available from Sandia National Laboratories (SNL) for wind turbine-related materials have been used to build, train, and validate an artificial neural network (ANN) to predict fatigue life under different environmental conditions. The performances of three algorithms (Backpropagation BP, Particle Swarm Optimization PSO, and Cuckoo Search CS) are compared for adjusting the synaptic weights of the ANN and evaluating the efficiency in predicting the fatigue life of the materials studied, under the conditions mentioned above. For accuracy evaluation, the mean square error (MSE) is used as an objective function to be optimized by the three algorithms.

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First-ply failure prediction of glass/epoxy composite pipes using an artificial neural network model

Cited by 34 publications

References 27 publications

How machine learning can help the design and analysis of composite materials and structures?

How machine learning can help the design and analysis of composite materials and structures?

An overview of burst, buckling, durability and corrosion analysis of lightweight FRP composite pipes and their applicability

Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions

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