AI-enabled manufacturing process discovery

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

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.

• Jezik: Angleški jezik
• Ključne besede: deep learning, manufacturing, data-driven modeling, variational autoencoder
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Datum objave: 01.02.2025
• Leto izida: 2025
• Št. strani: 13 str.
• Številčenje: Vol. 4, no. 2
• PID: 20.500.12556/RUL-167737
• UDK: 621
• ISSN pri članku: 2752-6542
• DOI: 10.1093/pnasnexus/pgaf054
• COBISS.SI-ID: 228422915
• Datum objave v RUL: 10.03.2025

Gradivo je del revije

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

Licence

• Licenca: CC BY-NC 4.0, Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc/4.0/deed.sl
• Opis: Licenca Creative Commons, ki prepoveduje komercialno uporabo, vendar uporabniki ne rabijo upravljati materialnih avtorskih pravic na izpeljanih delih z enako licenco.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: globoko učenje, proizvodni procesi, podatkovno vodeno modeliranje, variacijski avtoenkoder

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: USA, Department of Defense
• Številka projekta: N00014-19-1-2642
• Naslov: Vannevar Bush Faculty Fellowship