Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

This research looks at how to review and support the use of artificial‑intelligence (AI) methods for predicting the mechanical properties of composite materials. Traditional ways of characterizing composites—finite‑element analysis, physical tensile testing, and other numerical or experimental techniques—take a lot of time and computing power. Machine‑learning and deep‑learning models can provide a data‑driven shortcut that speeds up material characterization. The project sets up a step‑by‑step method for testing several AI architectures, such as: – Artificial neural networks – Support vector machines – Random forests These models will be used to predict key properties like tensile strength, Young’s modulus, and fatigue life. The framework includes: – Cleaning and preparing the data

– Creating features from constituent‑material information (fiber volume fraction, matrix properties, fiber orientation) – Following consistent model‑training procedures After training, the work compares each model’s accuracy, speed, and ability to work with new data across different types of composites. By providing a clear evaluation pipeline, this study gives practical advice for researchers who want to add predictive AI tools to the design and optimization of advanced composite structures. It also points out current hurdles, such as limited data sets and the difficulty of interpreting model results.

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