Neural network-based estimation and compensation of friction for enhanced deep drawing process control

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

Fluctuating process conditions, such as lubrication, can disturb the production process and lead to faulty components that have cracks or wrinkles. Real-time identification of process parameters can detect deviations in sheet forming operations and enable the process parameters to be adjusted. To increase process robustness, closed-loop control is often used to monitor and influence the material draw-in, which corresponds to the material flow and can be measured by camera systems inside the deep-drawing press. The aim of this work is to develop a control concept that can predict the optimum blank holder force by estimating the coefficient of friction based on the material draw-in of the last stroke. Using a cross-die geometry, it is shown how the material draw-in can be determined experimentally by means of a camera system and numerically by FE simulations. Finally, artificial neural network-based models are trained through simulations and are subsequently tested on a numerical case study in which the coefficient of friction is changed as a disturbance variable and must be compensated for. The widely applicable control concept has the potential to incorporate additional softsensors, for example to determine material properties, and other target variables, such as the punch force, into the optimization algorithm.
Original languageEnglish
Title of host publicationMaterial Forming ESAFORM 2024 : The 27th International ESAFORM Conference on Material Forming – ESAFORM 2024 – held in Toulouse (France), at the Pierre Baudis Convention Center between 24-26th April, 2024
EditorsAnna Carla Araujo, Arthur Cantarel, France Chabert, Adrian Korycki, Philippe Olivier, Fabrice Schmidt
Number of pages10
Place of PublicationMillersville
PublisherMaterialsResearchForum LLC
Publication date15.05.2024
Pages1462-1471
Article number162
ISBN (print)9781644903131
ISBN (electronic)978-1-64490-313-1
DOIs
Publication statusPublished - 15.05.2024
Event27th International ESAFORM Conference on Material Forming - ESAFORM 2024 - Pierre Baudis Convention Center, Toulouse, France
Duration: 24.04.202426.04.2024
Conference number: 27
https://esaform24.fr/

Bibliographical note

Publisher Copyright:
© 2024, Association of American Publishers. All rights reserved.

    Research areas

  • Engineering - Deep Drawing, Material Draw-In, predictive modelling, Friction Estimation, closed-loop control, Process Monitoring and Stabilization, Particle Swarm Optimization

Recently viewed

Publications

  1. Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing
  2. Using Language Learning Resources on YouTube
  3. Resource extraction technologies - is a more responsible path of development possible?
  4. Guest Editorial Special Issue on Sensors in Machine Vision of Automated Systems
  5. Emergency detection based on probabilistic modeling in AAL-environments
  6. Overcoming Multi-legacy Application Challenges through Building Dynamic Capabilities for Low-Code Adoption
  7. Energy Optimization in Motion Planning of a Two-Link Manipulator using Bernstein Polynomials
  8. Language and Mathematics - Key Factors influencing the Comprehension Process in reality-based Tasks
  9. Public perceptions of CCS in context
  10. Mechanical performance prediction for friction riveting joints of dissimilar materials via machine learning
  11. Don’t underestimate the problems of user centredness in software development projectsthere are many!?
  12. Watershed groundwater balance estimation using streamflow recession analysis and baseflow separation
  13. Model-based logistic controlling of converging material flows
  14. Geodesign as a boundary management process
  15. Institutional Proxy Representatives of Future Generations
  16. Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules
  17. Toward a methodical framework for comprehensively assessing forest multifunctionality
  18. Anatomy of Discrete Kalman Filter and Its Implementation for Sensorless Velocity Estimation of Organic Actuator
  19. A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
  20. Technical concept and evaluation design of the state subsidized project [Level-Q]
  21. Is sensitivity for the complexity of mathematics teaching measurable?
  22. On the origin of passive rotation in rotational joints, and how to calculate it
  23. Automated Invoice Processing: Machine Learning-Based Information Extraction for Long Tail Suppliers
  24. Failed mobility transition in an ideal setting and implications for building a green city
  25. What Makes for a Good Theory? How to Evaluate a Theory Using the Strength Model of Self-Control as an Example