20.500.12556/RUL-167737 AI-enabled manufacturing process discovery 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. deep learning manufacturing data-driven modeling variational autoencoder globoko učenje proizvodni procesi podatkovno vodeno modeliranje variacijski avtoenkoder true false true Angleški jezik Slovenski jezik Neznano 2025-03-10 11:43:55 2025-03-10 11:44:00 2025-10-24 12:40:40 0000-00-00 00:00:00 2025 0 0 13 str. no. 2 Vol. 4 February 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-02-01 621 2752-6542 10.1093/pnasnexus/pgaf054 228422915 RAZ_Quispe_Daniel_2025.pdf RAZ_Quispe_Daniel_2025.pdf 1 D48054DA619E325FB4C9106A11937196 3173179c5c77ad5c37068b36e9029a575f58b6cf0a60f0de3ae7849d6264644d 0de829a3-fd9c-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=200471 https://academic.oup.com/pnasnexus/article/4/2/pgaf054/8026683 1 9c3b0df9-fd9b-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=200469 Fakulteta za strojništvo 0 0 0