Popular GitHub Repositories for Steel Manufacturing

? Open Source Browser API for AI Agents & Apps. Steel Browser is a batteries-included browser instance that lets you automate the web without worrying about infrastructure.

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

Production-Grade ML System for Automated Unit of Measure Error Detection | 88-92% Accuracy | 94% Autonomy | KNIME Workflow

Additive manufacturing simulation with JAX.

Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

Differentiable Computational Lithogrpahy Framework

A curated list of datasets, publically available for machine learning research in the area of manufacturing

Image anomaly detection benchmark in industrial manufacturing

Deep Learning Based Steel Pipe Weld Defect Detection

I have built the computer vision models in 3 different ways addressing different personas, because not all companies will have a resolute data science team. quality-control manufacturing big-data-analytics jupyter-notebook cognitive services industry solutions

Bucketize an image based on exhaust data and AI generated data. industry-solutions azure azure machine learning services computer-vision big data big data analytics machine learning image recognition manufacturing quality control cognitive services

Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"

Code for Neural Inverse Knitting: From Images to Manufacturing Instructions

This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.

Sintef Digital Twin

Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems

A Multi-Agent Manufacturing Testbed

​This repository provides code and resources for automating manufacturing feature recognition in CAD designs using vision-language models.

Predict performance issues with manufacturing equipment motors. Perform local or cloud analytics of the issues found, and then display the data on a user interface to determine when failures might arise.

Predicting Indian stock prices using Stacked LSTM model. Analysing Reliance, Tata Steel, HDFC Bank, Infosys data. Data prep, EDA, hyperparameter tuning.