Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Kibrete, Fasikaw; Trzepieciński, Tomasz; Gebremedhen, Hailu Shimels; Woldemichael, Dereje Engida

doi:10.3390/jcs7090364

Cited by 26 publications

(5 citation statements)

References 208 publications

(244 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence is experiencing growing applications in engineering and even in the fields of experimental mechanics and materials modelling [6,7]. In order to reduce the error from noninstrumented tests, artificial intelligence can be used to build a virtual strain measurement instrument (AI-virtual extensometer), returning a stress-strain curve starting from the simple load-displacement curve of the non-instrumented test.…”

Section: Non-physical Correction Of Non-instrumented Testsmentioning

confidence: 99%

Material Characterization Augmented with Artificial Intelligence.

Vettori,

Marchi,

Bellocchio

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

The present paper investigates the application of artificial intelligence to improve the results from simple, non-instrumented, tensile tests, performed with a desktop-size MaCh3D smart universal testing machine. Non-instrumented tensile tests, performed on any testing machine, are affected by both deterministic and random factors that introduce errors in the test results. Specific features of the MaChh3D tester minimize random factors effects on test results while introducing a larger effect of deterministic factors. Artificial intelligence is identified as a novel approach to correct errors in non-instrumented tensile test, capable of simulating a direct strain measure onto the test, replacing traditional contact or non-contact instrumentations (like strain-gages, extensometer and optical measures) that introduce complexity into test procedure and require time for setup. The resulting AI model implementation is described and its performance evaluated in comparison with instrumented tests, also comparing different training strategies. The developed AI-extensometer (artificial intelligence virtual extensometer), is capable of a precise mechanical properties evaluation, with errors from 0 to 10% depending on the specific parameter.

show abstract

Section: Non-physical Correction Of Non-instrumented Testsmentioning

confidence: 99%

Material Characterization Augmented with Artificial Intelligence.

Vettori,

Marchi,

Bellocchio

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The SVM regression method was used in this study because of its ability to find complicated or non-linear relationships between the variables. Choosing an appropriate kernel, training the model by maximizing an objective function, and adjusting hyperparameters to obtain the best possible performance in continuous result prediction are the steps involved in SVM regression [40]. To predict the thermal data, this method created an optimized hyperplane.…”

Section: Support Vector Machine (Svm) Regression Modelingmentioning

confidence: 99%

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Deb,

Gou,

Song

et al. 2024

J. Compos. Sci.

View full text Add to dashboard Cite

Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.

show abstract

“…These approaches have been effectively used to predict critical mechanical properties accurately. However, their success largely hinges on data availability and the efficiency of the learning models [35]. The performance of composite materials can be greatly affected by their microstructure and composition.…”

Section: Ai In Properties Of Composite Materialsmentioning

confidence: 99%

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites

Serrano,

Ulloa,

Flores

et al. 2024

MMEP

View full text Add to dashboard Cite

The novel class of composite materials known as polyethylene-carbon nanotube composites (PECNTs) has attracted significant interest from scientists. In this study, authors investigated how artificial intelligence (AI) is employed to calculate the elastic modulus of PECNTs. For the first time, an AI-based modeling methodology replaces nanoindentation techniques like depth sensing indentation (DSI). This study highlights the complexities inherent in traditional methods, where the proposed methodology utilizes a gene expression programming (GEP) model, addressing challenges associated with accuracy in PECNT simulation. The proposed AI model test uses 135 input/output data pairs taken from the literature and randomly split into 82 training and 53 testing sets. The elastic modulus (EM) whichever dynamic E′ or quasi-static E) employs as an output factor in the models created, with the method of analysis, matrix type, processing technique, nanofiller type, and its content serving as inputs. Though the modeling progression is complete with results from the training and testing sets, the nanometer sensitivity of the prominent designs of the AI model displayed significant promise for the effective application of artificial intelligence methods in measuring the elastic modulus of PECNTs through non-destructive testing.

show abstract

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Cited by 26 publications

References 208 publications

Material Characterization Augmented with Artificial Intelligence.

Material Characterization Augmented with Artificial Intelligence.

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Artificial Intelligence for Mechanical Properties Prediction of Polyethylene-Carbon Nanotube Composites

Contact Info

Product

Resources

About