failure-analysis-toolkit 1.0.0

A toolkit for probabilistic failure analysis of mechanical components

Probabilistic Failure Analysis Toolkit

A Python toolkit for performing probabilistic failure analysis on mechanical components. This tool allows engineers to simulate component behavior under various uncertain conditions (forces, dimensions, material properties) using Monte Carlo simulations.

Features

• Monte Carlo Simulation: Run thousands of simulations to understand the probability of failure.
• Sensitivity Analysis: Identify which input variables have the biggest impact on your results using Spearman rank correlation.
• Engineering Formulas: Built-in formulas for common mechanical elements:
  • Shafts: Torsional shear stress, Von Mises stress.
  • Beams: Bending stress, deflection.
  • Gears: Lewis bending stress, contact stress.
  • Sheet Metal: Shearing force, bending force.
• Visualization: Generate plots for stress distributions and sensitivity analysis.

Installation

1. Clone the repository.
2. Install the required dependencies:

pip install numpy matplotlib scipy

Usage

The demo.py file contains a complete example of how to use the toolkit to analyze a cantilever beam.

To run the demo:

python demo.py

Example Code Snippet

from prob_failure_kit.statistical_methods.monte_carlo import MonteCarloSimulation
import numpy as np


# 1. Define your model
def my_stress_model(force, area):
    return force / area


# 2. Setup Simulation
mc = MonteCarloSimulation(my_stress_model)
mc.add_input('force', 'normal', loc=1000, scale=100)
mc.add_input('area', 'normal', loc=0.01, scale=0.001)

# 3. Run
results = mc.run()
print(f"Mean Stress: {np.mean(results)} Pa")

Project Structure

• prob_failure/: Main package directory.
  • formulas/: Engineering formulas for different components.
  • statistical_methods/: Monte Carlo and sensitivity analysis logic.
  • plotting/: Visualization utilities.
• demo.py: Example usage script.