Wheel_Assembly_Detection

PROJECT DESCRIPTION

A project with no prior study that to predict a successful assembly of tires on wheels without the need for operator intervention using data from torque and force sensor.

Motivation

The project is based on real case situated within the Testbed for Industry 4.0 at CTU Prague. The current quality control methodology uses CNNs for the visual inspection of tire assemblies.

Data

Data are meassured and labelled by the lab. The dataset is generated through robotic cell runs, every sample is then labeled as ‘true’ (successful assembly) or ‘false’ (unsuccessful assembly). We are using a “smaller” dataset (size limited due to self-labelling the data).

Project Goal

This project aims to introduce a new method for enhancing the quality control process in car wheel assembly executed by a delta robot.

Approach

Departing from the picture-based assessment using CNNs, our approach aims to evaluate the correctness of the assembly based on the data from a force-torque sensor. This transforms the dataset into a collection of time series, capturing recorded sensor data from individual tire assemblies. Each element from the series is a 6D vector combining a 3 DOF force vector and a 3 DOF torque vector.

Methodology

The chosen methodology is an implementation of Long Short-Term Memory Recurrent Neural Networks (LSTM RNNs) using PyTorch since the data are in timeseries. There is no existing baseline solution for the current problem, so some part of the project time would be spent on experimenting with models.

Framework

As a third-party framework we are going to use PyTorch Lightning and maybe with a Pytorch Forecasting package built on top of the Lightning.

INSTALLATION

Steps to build the repository in conda or docker

CONDA

1. git clone https://github.com/malek-luky/Automatic-Wheel-Assembly-Detection.git
2. cd Automatic-Wheel-Assembly-Detection
3. conda env create -f environment.yml
4. conda activate DTU_ML_Ops
5. dvc pull

DOCKER PIP

1. git clone https://github.com/malek-luky/Automatic-Wheel-Assembly-Detection.git
2. cd Automatic-Wheel-Assembly-Detection
3. docker build -f dockerfiles/Dockerfile . --build-arg DOCKER_BUILDKIT=1 -t mlops:latest
4. docker run --name mlops -it --entrypoint sh mlops:latest

DOCKER CONDA

1. git clone https://github.com/malek-luky/Automatic-Wheel-Assembly-Detection.git
2. cd Automatic-Wheel-Assembly-Detection
3. docker build -f dockerfiles/conda.dockerfile . --build-arg DOCKER_BUILDKIT=1 -t mlops:latest
4. docker run --name mlops -it --entrypoint sh mlops:latest

PROJECT STRUCTURE

The directory structure of the project looks like this:

├── Makefile             <- Makefile with convenience commands like `make data` or `make train`
├── README.md            <- The top-level README for developers using this project.
├── data
│   ├── processed        <- The final, canonical data sets for modeling.
│   └── raw              <- The original, immutable data dump.
│
├── docs                 <- Documentation folder
│   │
│   ├── index.md         <- Homepage for your documentation
│   │
│   ├── mkdocs.yml       <- Configuration file for mkdocs
│   │
│   └── source/          <- Source directory for documentation files
│
├── models               <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks            <- Jupyter notebooks.
│
├── pyproject.toml       <- Project configuration file
│
├── reports              <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures          <- Generated graphics and figures to be used in reporting
│
├── requirements.txt     <- The requirements file for reproducing the analysis environment
|
├── requirements_dev.txt <- The requirements file for reproducing the analysis environment
│
├── tests                <- Test files
│
├── Wheel_Assembly_Detection  <- Source code for use in this project.
│   │
│   ├── __init__.py      <- Makes folder a Python module
│   │
│   ├── data             <- Scripts to download or generate data
│   │   ├── __init__.py
│   │   └── make_dataset.py
│   │
│   ├── models           <- model implementations, training script and prediction script
│   │   ├── __init__.py
│   │   ├── model.py
│   │
│   ├── visualization    <- Scripts to create exploratory and results oriented visualizations
│   │   ├── __init__.py
│   │   └── visualize.py
│   ├── train_model.py   <- script for training the model
│   └── predict_model.py <- script for predicting from a model
│
└── LICENSE              <- Open-source license if one is chosen

Created using mlops_template, a cookiecutter template for getting started with Machine Learning Operations (MLOps).