Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

Abstract

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested under five independent parameters i.e, type of fiber (Tf), moisture content (Mc), weight of sample (Ws), gauge length (Gl) and loading rate (Lr) with response variables, i.e., breaking load and elongation. In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). Among the machine learning models, random forest regressor yielded high R2 value, low mean squared error (MSE), and mean absolute error (MAE). The SHapley Additive exPlanations (SHAP) analysis was performed and found sample weight and gauge length were the most influential features for breaking load and elongation, respectively. The developed GUI, integrated with a random forest regressor, predicted breaking load and elongation with an error range of −2.5% to 2.3% for raw fiber and 1.5% to 6.5% for cleaned fiber. The developed GUI coupled random forest regressor can be used to predict the mechanical properties of fibers with ease.

Keywords

• Breaking load
• elongation
• GUI
• random forest
• 断裂载荷
• 伸长