Fracture Behavioural Computer – Aided Engineering Model with Self-Learning Grid System

Abstract

A Fracture Behavioural Computer-Aided Engineering (CAE) Model is a sophisticated computational approach designed to simulate and predict the fracture behavior of materials under various loading conditions. By integrating advanced material models with numerical simulation techniques, this CAE model provides valuable insights into crack initiation, propagation, and material failure. The model leverages finite element analysis (FEA) to simulate stress distributions, fracture toughness, and critical crack growth, considering factors such as material heterogeneity, temperature effects, and loading rates. The Proposed Hashing Self-Organized Map (HSOM) introduces an advanced approach to fracture behavioral modeling in a Computer-Aided Engineering (CAE) framework, integrating a Self-Learning Grid System for improved prediction and analysis of material fracture under various loading conditions.
The HSOM combines the power of self-organizing maps (SOM) with a hashing algorithm to efficiently organize and process large volumes of data related to fracture mechanics. This method enables the model to learn and adapt in real time, improving its ability to predict crack initiation and propagation by processing input data from previous simulations and experimental results. In the context of a fracture behavioral CAE model, the HSOM algorithm uses a self-learning grid to automatically classify material
behavior based on stress distribution, crack location, and environmental factors. Simulation results for the proposed Hashing Self-Organized Map (HSOM) integrated into a Fracture Behavioural Computer-Aided Engineering (CAE) model with a Self-Learning Grid System demonstrate significant improvements in fracture prediction accuracy and computational efficiency. In a simulation of a 500 MPa tensile stress applied to a carbon composite material, the HSOM model accurately predicted fracture initiation at a 0.5 mm defect with a prediction error of only 2% compared to experimental data, while traditional models showed an error of up to 10%. Additionally, the model forecasted crack propagation with a margin of error of just 3% over a 5 mm crack growth distance, compared to the 10% error margin of conventional fracture models. The hashing technique allowed the HSOM to process large datasets with 95% memory optimization, enabling faster simulations without compromising accuracy. In terms of computational efficiency, the HSOM model reduced simulation time by 40%, processing simulations in 30 minutes instead of the usual 50 minutes required by traditional methods.