Prediction of the mixed mode I/II fracture toughness of PMMA by an artificial intelligence approach

Wiangkham, Attasit; Ariyarit, Atthaphon; Aengchuan, Prasert

doi:10.1016/j.tafmec.2021.102910

Cited by 24 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 shows the mechanical properties of the PMMA sheet utilized in this study, for which testing was conducted in accordance with ASTM D638 and D732 standards. Numerous testing methods and specimens, such as incline edge crack asymmetric bending [ 3 ], compact tension shear [ 6 ], asymmetric four-point bending [ 22 ], semi-circular bending [ 23 ], Brazilian disc specimen [ 24 ], etc., are used to study mixed-mode I/II fracture toughness. In this study, the mixed-mode I/II of PMMA was studied using the inclined crack bending specimen based on previous research by Mingdong Wei [ 25 ] and M.R.M.…”

Section: Mixed-mode I/ii Fracture Toughnessmentioning

confidence: 99%

“…In recent years, many researchers have applied artificial intelligence to many complex engineering problems in many disciplines. For fracture mechanics, artificial intelligence has been applied to describe the behavior of cracks, such as in the prediction of the pure mode loading fracture parameters [ 8 , 32 , 33 ] or mixed-mode loading fracture parameters [ 22 , 34 ]. Artificial intelligence methods have significant advantages in terms of making accurate predictions on complex problems, but there is a rather problematic disadvantage about the learning process of artificial intelligence models.…”

Section: Mixed-mode I/ii Fracture Toughness Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Improvement of Mixed-Mode I/II Fracture Toughness Modeling Prediction Performance by Using a Multi-Fidelity Surrogate Model Based on Fracture Criteria

et al. 2022

Self Cite

View full text Add to dashboard Cite

Today, artificial intelligence plays a huge role in the mechanical engineering field for solving many complex problems and the problem with fracture mechanics is one of them. In fracture mechanics, artificial intelligence is used to predict crack behavior under various conditions such as mixed-mode loading. Many parameters are used for explaining the crack behavior under various conditions, but those parameters are obtained from destructive testing, in which usually, only one data point is obtained from each test. An artificial problem method requires a large amount of data to train the model to be able to learn crack behavior, which is a disadvantage of applying this method to fracture mechanics. To eliminate the disadvantage of the large amount of experiment data required for modeling, in this study, the small data obtained from the experiment along with data obtained from fracture criteria that were used for elementary prediction of mixed mode fracture toughness were used to create an artificial intelligence model. Data from the experiment was combined with fracture criteria data using the multi-fidelity surrogate model that is described in this study. The mixed mode I/II fracture toughness of the PMMA material was tested in order to primarily propose the data combination technique. After the modeling process, the prediction results indicated that the performance of a model in which the actual test data was combined with the data from the fracture criteria (multi-fidelity surrogate model) was more predictively effective compared to only actual data-based modeling.

show abstract

Section: Mixed-mode I/ii Fracture Toughnessmentioning

confidence: 99%

Section: Mixed-mode I/ii Fracture Toughness Predictionmentioning

confidence: 99%

Improvement of Mixed-Mode I/II Fracture Toughness Modeling Prediction Performance by Using a Multi-Fidelity Surrogate Model Based on Fracture Criteria

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…With regrading to the first kind of application strategies, material properties including compressive/tensile strength, fracture toughness indexes, such as stress intensity factor, crack tip opening distance (CTOD), as well as elastic modulus, are predicted by utilizing various artificial intelligence approaches [38][39][40][41], such as artificial neural network (ANN), deep neural network (DNN), particle swarm optimization (PSO) and ant colony optimization (ACO). With regrading to the second kind of application strategies, some artificial intelligence approaches are utilized to predict the local damage and crack path distribution based on the global indexes such as the structural nature frequency, displacement, and stress intensity factor obtained from monitoring or theoretical solution [42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Physical cellular automata and artificial fish swarm fusion catastrophic failure prediction of brittle-like materials

Sun,

Guo

2024

Preprint

View full text Add to dashboard Cite

The investigation aims to establish a physics-informed data-driven method for catastrophic failure analysis of brittle-like materials based on the strategy of the physical cellular automata and artificial fish swarm fusion prediction. Based on the method, local mesoscopic elasticity modulus and damage distribution of brittle-like materials can be predicted based on the monitored data of the global macroscopic mechanical response. In the data-driven prediction process based on the simplification of the artificial fish swarm algorithm, physical catastrophic failure mechanisms can be considered by utilizing a modified cellular automata technique for updating and iterating damage distribution based on the principle of energy conservation and energy dissipation. Two numerical cases are implemented to support the developed method. The results support that the method possesses a good convergence and a high prediction accuracy. The maximum prediction error of the macroscopic stress-strain relationship of the two numerical cases is 9.6% based on the optimal mesoscopic elasticity modulus prediction. Meanwhile, the predicted local mesoscopic catastrophic failure paths of the two numerical cases both match well with the corresponding experimental results. The developed physics-informed data-driven method can provide an efficient tool to predict both macroscopic and mesoscopic failure particularities of brittle-like materials for better investigating their catastrophic failure mechanisms.

show abstract

“…To know how the above factors affect fracture toughness most of the general methods have to be tested on real materials which will cause quite a lot of expenses. For this reason, this study aims to create an equation that can be used to predict the effect of such factors on the fracture toughness of materials using artificial intelligence methods (AI) that are popular in the materials field today [2]. The widely used AI algorithm such as generalized regression neural network [3] and Gaussian processes regression [4] were selected to a created a prediction model based on actual fracture toughness obtained from experiments.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

Wiangkham

Aengchuan

Ariyarit

2023

Self Cite

View full text Add to dashboard Cite

Present, artificial intelligence methods play a huge role in solving complex engineering problems such as the fracture toughness of materials, which is one of the parameters to be considered for engineering design. Fracture toughness tests can be prepared materials and test configured in a variety of ways, resulting in different fracture toughness depending on the preparation method. In this study, fracture toughness of PMMA under the effect of loading rate is one of the testing configs that can be adjusted according to the actual load characteristics of the material and the crack geometry (crack width and crack length ratio) according to crack preparation to test specimens and the effect of these factors was predicted with generalized regression neural network (GRNN) and Gaussian processes regression (GPR) models which are one of the artificial intelligence models, compared to traditional fracture toughness predictions. The results showed that artificial intelligence prediction was able to more accurately predict the effect of the factors studied on the fracture toughness of PMMA compared to the traditional fracture toughness prediction.

show abstract

Prediction of the mixed mode I/II fracture toughness of PMMA by an artificial intelligence approach

Cited by 24 publications

References 24 publications

Improvement of Mixed-Mode I/II Fracture Toughness Modeling Prediction Performance by Using a Multi-Fidelity Surrogate Model Based on Fracture Criteria

Improvement of Mixed-Mode I/II Fracture Toughness Modeling Prediction Performance by Using a Multi-Fidelity Surrogate Model Based on Fracture Criteria

Physical cellular automata and artificial fish swarm fusion catastrophic failure prediction of brittle-like materials

Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

Contact Info

Product

Resources

About