NORMALIZING FLOWS FOR INTELLIGENT MANUFACTURING

Matthew Russell, Peng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Monitoring machine health and product quality enables predictive maintenance that optimizes repairs to minimize factory downtime. Data-driven intelligent manufacturing often relies on probabilistic techniques with intractable distributions. For example, generative models of data distributions can balance fault classes with synthetic data, and sampling the posterior distribution of hidden model parameters enables prognosis of degradation trends. Normalizing flows can address these problems while avoiding the training instability or long inference times of other generative Deep Learning (DL) models like Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and diffusion networks. To evaluate normalizing flows for manufacturing, experiments are conducted to synthesize surface defect images from an imbalanced data set and estimate parameters of a tool wear degradation model from limited observations. Results show that normalizing flows are an effective, multi-purpose DL architecture for solving these problems in manufacturing. Future work should explore normalizing flows for more complex degradation models and develop a framework for likelihood-based anomaly detection. Code is available at https://github.com/uky-aism/flows-for-manufacturing.

Original languageEnglish
Title of host publicationManufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability
ISBN (Electronic)9780791887240
DOIs
StatePublished - 2023
EventASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023 - New Brunswick, United States
Duration: Jun 12 2023Jun 16 2023

Publication series

NameProceedings of ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
Volume2

Conference

ConferenceASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
Country/TerritoryUnited States
CityNew Brunswick
Period6/12/236/16/23

Bibliographical note

Publisher Copyright:
Copyright © 2023 by ASME.

Keywords

  • Condition Monitoring
  • Deep Generative Models
  • Normalizing Flows
  • Parameter Estimation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'NORMALIZING FLOWS FOR INTELLIGENT MANUFACTURING'. Together they form a unique fingerprint.

Cite this