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Related Experiment Video

Updated: May 24, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

AI-enabled manufacturing process discovery.

D Quispe¹, D Kozjek^1,2, M Mozaffar^1,3

¹Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.

|March 5, 2025

Summary

This summary is machine-generated.

This study introduces a universal manufacturing language to systematically discover new processes based on energy and performance dependencies. A machine learning model (variational autoencoder) encodes diverse manufacturing data, enabling exploration and generation of novel processes.

Keywords:

data-driven modeling deep learning manufacturing variational autoencoder

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Last Updated: May 24, 2025

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Area of Science:

Manufacturing Engineering
Materials Science
Artificial Intelligence

Background:

Manufacturing process discovery traditionally relies on experience, lacking systematic approaches.
Existing manufacturing languages are fragmented, hindering cross-process analysis and scalability.
A universal language is needed to characterize diverse manufacturing inputs and outputs.

Purpose of the Study:

To propose a systematic approach for discovering manufacturing processes.
To develop a universal language for characterizing manufacturing process inputs and outputs.
To leverage machine learning for identifying dependencies and generating novel manufacturing processes.

Main Methods:

Developed a universal manufacturing language to describe process characteristics.
Constructed a dataset encompassing over 50 diverse process classes.
Trained a variational autoencoder (VAE) model on the dataset to encode processes into a 2D latent space.

Main Results:

The VAE model successfully encoded diverse manufacturing processes, revealing underlying dependencies.
The latent space allows for exploration, selection, and generation of processes based on desired performance outputs.
Verified model-derived dependencies align with established manufacturing knowledge.

Conclusions:

The proposed universal language and VAE model offer a systematic framework for manufacturing process discovery.
This approach facilitates the identification of novel manufacturing processes with desired performance characteristics.
The methodology demonstrates potential for accelerating innovation in manufacturing.