AI-enabled manufacturing process discovery

Quispe, Daniel (author), Kozjek, Dominik (author), Mozaffar, Mojtaba (author), Xue, Tianju (author), Cao, Jian (author)

Discovering manufacturing processes has been largely experienced-based. We propose a shift to a systematic approach driven by dependencies between energy inputs and performance outputs. Uncovering these dependencies across diverse process classes requires a universal language that characterizes process inputs and performances. Traditional manufacturing languages, with their individualized syntax and terminology, hinder the characterization across varying length scales and energy inputs. To enable the evaluation of process dependencies, we propose a broad manufacturing language that facilitates the characterization of diverse process classes, which include energy inputs, tool-material interactions, material compatibility, and performance outputs. We analyze the relationships between these characteristics by constructing a dataset of over 50 process classes, which we use to train a variational autoencoder (VAE) model. This generative model encodes our dataset into a 2D latent space, where we can explore, select, and generate processes based on desired performances and retrieve the corresponding process characteristics. After verifying the dependencies derived from the VAE model match with existing knowledge on manufacturing processes, we demonstrate the usefulness of using the model to discover new potential manufacturing processes through three illustrative cases.

• Language: English
• Keywords: deep learning, manufacturing, data-driven modeling, variational autoencoder
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Publication date: 01.02.2025
• Year: 2025
• Number of pages: 13 str.
• Numbering: Vol. 4, no. 2
• PID: 20.500.12556/RUL-167737
• UDC: 621
• ISSN on article: 2752-6542
• DOI: 10.1093/pnasnexus/pgaf054
• COBISS.SI-ID: 228422915
• Publication date in RUL: 10.03.2025

Record is a part of a journal

• Title: PNAS nexus
• Publisher: Oxford University Press
• ISSN: 2752-6542
• COBISS.SI-ID: 115680259

Licences

• License: CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
• Link: http://creativecommons.org/licenses/by-nc/4.0/
• Description: A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.

Secondary language

• Language: Slovenian
• Keywords: globoko učenje, proizvodni procesi, podatkovno vodeno modeliranje, variacijski avtoenkoder

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: USA, Department of Defense
• Project number: N00014-19-1-2642
• Name: Vannevar Bush Faculty Fellowship