IMPACT LOADING ANALYSIS OF PARTICULATE POLYMER COMPOSITES WITH AN EFFICIENT HYBRID MACHINE LEARNING APPROACH
Journal: Proceedings on Engineering Sciences (Vol.5, No. 3)Publication Date: 2023-08-31
Authors : Soumya A. K. Ritu Shree Anu Sharma;
Page : 97-102
Keywords : Particulate polymer composites; machine learning; hybrid artificial neural network; and random forest (HANN-RF);
Abstract
The fracture behaviour of particle composites made of polymers under impact loading is predicted in this research using a hybrid machine learning approach dubbed Hybrid Artificial Neural Networks and Random Forest (HANN-RF), with a focus on mode-I fracture. The goal of the study is to create a model for prediction that accurately links input variables to histories of crack initiation, fracture toughness, and the intensity of the stress factor (SIF). A full dataset is created, with inputs for the composites' compositional properties and impact loading scenarios. The HANN-RF model combines a Random Forest (RF) method and an ANN (Artificial Neural Network) in order to improve robustness and accuracy in forecasting. Metrics like MAE, MAPE for short, and accuracy are used in model evaluation. The outcomes show that the HANN-RF technique successfully predicts and forecasts mode-I fracture behaviour, offering insightful information for evaluating the effect on resilience and longevity of particle polymer composites in a variety of applications.
Other Latest Articles
- MACHINE LEARNING BASED GRID SAFETY ASSESSMENT THROUGH SIMULATION OF UNEXPECTED CONTINGENCIES DURING MAINTENANCE
- OPTIMIZING PRODUCTION SCHEDULING THROUGH HYBRID DYNAMIC GENETIC-ADAPTIVE IMPROVED GRAVITATIONAL OPTIMIZATION ALGORITHM
- DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS
- RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS
- INTEGRATING SENSOR DATA AND MACHINE LEARNING FOR PREDICTIVE MAINTENANCE IN INDUSTRY 4.0
Last modified: 2023-09-07 01:26:04