Predictive & Prescriptive Analytics for Laser Seam Welding

This repository presents the implementation of a predictive model for product quality based on machine parameters in a laser seam welding process. Additionally, a prescriptive model was developed using meta-heuristic algorithms to find the best machine parameter once a new product quality is required.

For all the code snippets and plots please open the Jupyter Notebook named "predictive_prescriptive_analytics.ipynb". The folder dataset contains all the relevant CSVs, including the original dataset and post-processed ones. For the presented scenario, the dataset label as "no_keyhole" was used.

Create an Anaconda environment

conda create --name machine_intelligence

Activate the environment (if not activated)

conda activate machine_intelligence

Install ipykernel to allow the new environment as a Kernel

conda install -c anaconda ipykernel

Associate the environment to the Jupyter Notebook

python -m ipykernel install --user --name=firstEnv

Installing all dependencies

pip install –r requirements.txt

List all environments

conda env list

deactivate current environment

conda deactivate

Remove an environment

conda remove –name myenv --all

List all packages in an environment

conda list -n myenv