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Machine Learning for Sheet Metal Bending Process

LightGBM Physics-Informed ML Feature Engineering Manufacturing Optimization
Machine Learning for Sheet Metal Bending Process

Overview

Traditional sheet metal bending relies on iterative trial-and-error, increasing production time and waste. This project introduced a physics-informed LGBM model to predict bending sequences with high precision.

Development Process

Final Result

By incorporating material properties and geometric parameters, prediction time was reduced from over an hour to under five minutes, improving accuracy from 84.7% to 93.0%.

Final result

Collaborators

This project was developed in collaboration with the manufacturing optimization team.