Publication: Prediction of tensile strength of polymer composites using machine learning algorithms
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Date
2024-07
Authors
Shah Ayu Balqis binti Ismail
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Abstract
The project is to perform the analysis of tensile strength of polymer composites by using linear regression. Although the research focuses on future engineering applications of the work, it is underpinned by the recognition of the growing requirement for high-performance lightweight materials in numerous engineering sectors, together with the difficulty of predicting the tensile strength of these materials. Many times, the conventional techniques are incapable to identify intricate correlation between input variables and developments in the UTS and hence requires more refined procedures. Linear regression serves as another valuable method or approach that may provide a clearer insight into patterns and data correlations. The primary objective of this work is to establish an accurate predictive model, specifically linear regression, based on a large dataset to accurately estimate the tensile strength of polymer composites. The study also aims at identifying the importance of the features as well as finding relationships of the existing ones in the set with an aim of identifying the important parameters affecting the tensile strength. In using and proving the effectiveness of the proposed linear model through training and test datasets and their validations; the study shows linearity’s ability to accelerate material selection, improve process parameters, and design microstructure. This versatility of the model to predict the designs for newly evolved forms of applications can go a long way in helping the engineers design safer and more reliable part of polymer composite structures for these demanding application categories; this would indeed mark shift in polymer composite paradigm. Thus, linear regression analysis is considered, in enhancing the understanding and the computational accuracy of the tensile strength of polymer composites, has laid a wide and profound foundation for subsequent progresses in material science and engineering.