Automating FEA Reports with Large Language Models

Finite Element Analysis (FEA) is the backbone of mechanical validation. We spend days setting up meshes, applying loads, and running simulations. But what happens when the solver finishes? We spend another day copying plots, writing descriptions, and formatting a 50-page Word document that nobody actually wants to read.

This is where Large Language Models (LLMs) and a little bit of Python can save you countless hours of tedious work.

The Problem with Manual Reporting

In a corporate environment, documentation is critical. A design isn't finished until the analysis report is signed off. However, the process of creating these reports is highly repetitive:

1. Extract maximum stress and displacement values.
2. Capture screenshots of the von Mises stress plots.
3. Write a paragraph explaining that the maximum stress is below the yield strength.
4. Repeat for every load case.

This is a perfect candidate for automation.

Building the Automation Pipeline

The goal is to create a script that takes the raw output data from your FEA solver (like Ansys or Abaqus) and generates a formatted report with intelligent commentary.

Step 1: Data Extraction

Most modern FEA tools have Python APIs or can export results to CSV files. The first step is to write a script that extracts the key metrics: maximum stress, maximum displacement, and the locations of these maximums.

import pandas as pd

# Load the results data
results = pd.read_csv('fea_results.csv')

# Extract key metrics
max_stress = results['von_mises'].max()
max_disp = results['displacement'].max()

Step 2: Generating Commentary with an LLM

This is where the magic happens. Instead of writing boilerplate text, we can pass the extracted data to an LLM via an API (like OpenAI's GPT-4) and ask it to generate the commentary.

We construct a prompt that includes the data and the context:

"You are a senior mechanical engineer writing an FEA report. The maximum von Mises stress for Load Case 1 is 250 MPa, and the material yield strength is 300 MPa. The maximum displacement is 1.2 mm. Write a professional, concise paragraph summarizing these results and concluding whether the design is safe."

The LLM will return a perfectly formatted paragraph that you can insert directly into your report.

Step 3: Document Assembly

Finally, we use a library like python-docx or a Markdown generator to assemble the final document, combining the LLM-generated text with the plots exported from the solver.

The Result

By implementing this pipeline, we reduced the time required to generate a standard FEA report from 6 hours to 15 minutes. The engineer simply runs the script, reviews the generated document for accuracy, and signs off.

This isn't about replacing the engineer's judgment; it's about freeing up their time to actually analyze the results rather than just formatting them.