Abstract
Abstract
Many applications are using composites to improve performance and reduce weight, but it is essential to know the different properties of the composite before manufacturing. Properties like natural frequency and elastic modulus are also crucial in many applications. The use of shape memory alloys (SMA) composite has increased in the last few years due to various advantages of the shape memory alloys, like a shift in natural frequency and elastic modulus during phase transformation. Hence it is essential to know the composite’s natural frequency and elastic modulus before constructing it. Although experimental and numerical methods for calculating natural frequency exist, they are time-consuming and infrastructure-dependent. This paper explores relationships between SMA composite construction parameters and natural frequency to predict it better. Nitinol-reinforced silicon rubber composite beams are investigated with various parametric combinations using an orthogonal array. Different machine-learning techniques are applied for natural frequency prediction after training models on numerical results from varied construction combinations. The study identifies the best-performing algorithm and provides tuning recommendations. Linear regression model, Ridge regression model, and Decision Tree regression are the best-performing algorithms for the dataset in this paper. A weighted sum method finds optimal construction parameters for maximum natural frequency. These models can predict natural frequency before construction and the shift during SMA phase transformation. The research aids in designing SMA-reinforced beams by identifying optimal parameters like volume fraction, location, and activation pattern, targeting maximum natural frequency. The composite studied in this research shows a maximum natural frequency of 19.58 Hz for a 3.53% volume fraction of SMA, 3 mm distance of reinforcement, all wires activated, and austenite temperature.