Harnessing AI and Machine Learning for Advanced Materials Testing

By Taha KhanJun 14 2024Reviewed by Lexie Corner

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their intended applications.

Image Credit: Gorodenkoff/Shutterstock.com

Researchers and engineers evaluate the properties and behavior of materials used in buildings, bridges, airplanes, and other structures to ensure their safe, reliable, and efficient performance under various conditions.

In this regard, Artificial intelligence (AI) and machine learning (ML) are revolutionizing traditional testing methods, which can be time-consuming, expensive, and often limited in scope.^{1, 2}

AI and Machine Learning in Materials Testing

Enhancing the Accuracy of Materials Testing with AI and ML

AI algorithms improve the accuracy and efficiency of materials testing by processing massive datasets, identifying complex patterns, and making precise predictions.

For instance, AI helps analyze vast amounts of data generated during testing, including sensor measurements, images, and historical records, identifying patterns and correlations within this data.^{1, 2}

AI models can be trained to predict critical material properties, such as mechanical strength, fatigue resistance, and corrosion susceptibility, allowing researchers to optimize material selection for specific applications without extensive physical testing.^{1, 2}

Similarly, repetitive tasks like data analysis and report generation can be automated using AI and ML, freeing researchers to focus on complex problem-solving and material design while significantly reducing time and costs.

Using AI to Predict Material Properties and Behaviors

AI models are being used to predict a material’s yield strength, tensile strength, and ductility based on its composition and processing history.

For instance, a recent study explored AI to enhance non-destructive testing (NDT) methods for assessing the compressive strength of concrete. Traditional NDT methods like the rebound hammer (RH) and ultrasonic pulse velocity (UPV) tests often produce less accurate results than destructive testing.³

The study applied AI models, including adaptive neural fuzzy inference systems (ANFIS), support vector machines (SVM), and artificial neural networks (ANNs), to predict concrete strength more precisely. Analyzing data from 98 in-situ concrete samples, the AI models demonstrated significantly improved accuracy over conventional statistical methods.³

ML techniques can also analyze data from various testing methods, like tensile and fatigue testing, to predict material behavior under different stress conditions, enabling materials with tailored properties for specific applications.

Machine Learning in Developing NDT Methods

NDT is crucial for evaluating materials without causing damage. Machine learning algorithms can analyze complex images and signals generated by NDT methods like X-Ray radiography and ultrasonic testing to detect defects with higher accuracy and sensitivity than traditional methods.^{3, 4}

Researchers are developing AI-powered systems to analyze acoustic emissions during stress testing to identify potential cracks or damage in materials. For instance, a 2019 study developed AI-powered systems to analyze acoustic emissions during stress testing of fiber-reinforced composite structures. They employed artificial neural networks to enhance the prediction of local stress exposure and failure in these materials.

The system predicted failure loads by detecting AE signals, which are ultrasonic stress waves released during internal displacements like crack growth. This approach allowed accurate prediction of structural integrity without the need for full-scale destructive testing, significantly reducing the time and costs associated with large-scale structural assessments.⁵

In another 2019 study, researchers explored the application of machine learning for NDT to detect hidden material damage using low-cost external sensors. The study involved a multi-domain simulation to evaluate various ML models and algorithms, including support vector machines, neural networks, and decision trees.⁶

The researchers demonstrated the effectiveness of these models in predicting internal damages from noisy sensor data by employing a mass-spring network to simulate the device under test.

While deep learning models are popular, they found that simpler ML techniques like decision trees and single-layer perceptrons can also provide robust and accurate predictions, sometimes more efficiently.

The study highlights the potential of integrating ML with inexpensive sensors for real-time structural health monitoring and damage detection.⁶

Companies Integrating AI in Materials Testing Systems

Several companies are integrating AI and ML into their materials testing systems. For example, Baker Hughes, an oilfield services company, utilizes AI to analyze data from downhole sensors, optimize drilling operations, and ensure well integrity.⁷

Similarly, Siemens Simcenter Culgi software uses machine learning to analyze past simulations and real-world data, allowing engineers to predict product performance quickly and accurately.^{8, 9}

Integration Challenges and Solutions

AI and ML hold great potential for materials testing but also present challenges. Integrating AI with existing testing frameworks requires significant technological and methodological adaptations.

Training effective AI models require large amounts of high-quality data, and ensuring data accuracy is imperative since inaccurate data may generate false predictions. Understanding how AI models arrive at their predictions can also be difficult, necessitating interpretability to build trust in AI-driven materials testing.^2,10,11

These challenges can be addressed through continuous research, standardized data format development, and collaboration between different disciplines.^{10, 11}

Future Outlooks

AI is expected to play a significant role in material testing in the future. AI systems for real-time monitoring and predictive maintenance of materials in service can enable proactive interventions before failures occur. Additionally, integrating AI with Internet of Things (IoT) devices offers the potential for continuous, in-situ materials testing and monitoring.¹⁰

AI in materials testing will likely influence industry standards and testing methodologies, evolving them to incorporate AI-driven predictive models, leading to more efficient and accurate evaluation processes. This shift will enhance industries’ ability to develop and deploy advanced materials.

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References and Further Reading

1. Huang, G., Guo, Y., Chen, Y., Nie, Z. (2023). Application of machine learning in material synthesis and property prediction. Materials. doi.org/10.3390/ma16175977
2. Badini, S., Regondi, S., Pugliese, R. (2023). Unleashing the power of artificial intelligence in materials design. Materials. doi.org/10.3390/ma16175927
3. Ngo, TQL., Wang, YR., Chiang, DL. (2021). Applying artificial intelligence to improve on-site non-destructive concrete compressive strength tests. Crystals. doi.org/10.3390/cryst11101157
4. Yella, S., Dougherty, MS., Gupta, NK. (2006). Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing. Insight-Non-Destructive Testing and Condition Monitoring. doi.org/10.1784/insi.2006.48.1.10
5. Sause, M. G., Schmitt, S., Hoeck, B., Monden, A. (2019). Acoustic emission based prediction of local stress exposure. Composites Science and Technology. doi.org/10.1016/j.compscitech.2019.02.004
6. Bosse, S., Lehmhus, D. (2019). Robust detection of hidden material damages using low-cost external sensors and Machine Learning. Proceedings. doi.org/10.3390/ecsa-6-06567
7. Baker Hughes. (n.d). Combining Baker Hughes energy technology expertise with C3 AI technology. [Online] Baker Hughes. Available at: https://www.bakerhughes.com/bhc3#:~:text=BHC3%E2%84%A2 (Accessed on June 6, 2024)
8. Siemens (n.d). AI in Simcenter Simulation. [Online] Siemens. Available at: https://webinars.sw.siemens.com/en-US/ai-in-simcenter-simulation/  (Accessed on June 6, 2024)
9. Siemens (n.d). Simcenter Culgi software. [Online] Siemens. Available at: https://plm.sw.siemens.com/en-US/simcenter/fluids-thermal-simulation/culgi/ (Accessed on June 6, 2024)
10. Yazdani-Asrami, M., Sadeghi, A., Song, W., Madureira, A., Murta-Pina, J., Morandi, A., Parizh, M. (2022). Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring. Superconductor Science and Technology. doi.org/10.1088/1361-6668/ac80d8
11. Himanen, L., Geurts, A., Foster, AS., Rinke, P. (2019). Data‐driven materials science: status, challenges, and perspectives. Advanced Science. doi.org/10.1002/advs.201900808

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