Popular GitHub Repositories for Manufacturing

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

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

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"

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.

Code for Neural Inverse Knitting: From Images to Manufacturing Instructions

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

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.

MLOps samples and docs from real world projects in manufacturing industry

This Guidance demonstrates how to create an intelligent manufacturing digital thread through a combination of knowledge graph and generative artificial intelligence (AI) technologies. A digital thread offers an integrated approach to combine disparate data sources across enterprise systems, increasing traceability, accessibility, collaboration.

Worked on a dataset of high entropy alloys which is used to design materials for additive manufacturing. Being responsible for Performing Data Analysis and constructing Machine learning algorithms, including neural networks, Gradient boosting for carrying predictions useful for advanced material invention.

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

This repo contains implementation of deep learning-based steel surface defect segmentation models. Extensive experiments on several deep learning frameworks have been presented with various performance analysis and comparison.