Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschung

Authors

In the last decades, there has been an increase in the number of successful machine learning models that have served as a key to identifying and using linkages within the process-structure–property-performance chain for vastly different problems in the domains of materials mechanics. The consideration of physical laws in data-driven modelling has recently been shown to enable enhanced prediction performance and generalization while requiring less data than either physics-based or data-driven modelling approaches independently. In this contribution, we introduce a simulation-assisted machine learning framework applied to the solid-state layer deposition technique friction surfacing, suitable for solid-state additive manufacturing as well as repair or coating applications. The objective of the present study is to use machine learning algorithms to predict and analyse the influence of process parameters and environmental variables, i.e. substrate and backing material properties, on process behaviour and deposit geometry. The effects of maximum process temperatures supplied by a numerical heat transfer model on the predictions of the targets are given special attention. Numerous different machine learning algorithms are implemented, optimized and evaluated to take advantage of their varied capabilities and to choose the optimal one for each target and the provided data. Furthermore, the input feature dependence for each prediction target is evaluated using game-theory related Shapley Additive Explanation values. The experimental data set consists of two separate experimental design spaces, one for varying process parameters and the other for varying substrate and backing material properties, which allowed to keep the experimental effort to a minimum. The aim was to also represent the cross parameter space between the two independent spaces in the predictive model, which was accomplished and resulted in an approximately 44 % reduction in the number of experiments when compared to carrying out an experimental design that included both spaces.
OriginalspracheEnglisch
Aufsatznummer116453
ZeitschriftComputer Methods in Applied Mechanics and Engineering
Jahrgang418
AusgabenummerPart A
Anzahl der Seiten26
ISSN0045-7825
DOIs
PublikationsstatusErschienen - 01.01.2024

Bibliographische Notiz

Funding Information:
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 101001567).

Funding Information:
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 101001567 ).

Publisher Copyright:
© 2023 The Author(s)

DOI

Zuletzt angesehen

Publikationen

  1. Q-Adaptive Control of the nonlinear dynamics of the cantilever-sample system of an Atomic Force Microscope
  2. Model-based logistic controlling of converging material flows
  3. From Open Access to Open Science
  4. Detection time analysis of propulsion system fault effects in a hexacopter
  5. Nonlinear PD fault-tolerant control for dynamic positioning of ships with actuator constraints
  6. Passive Rotation Compensation in Parallel Kinematics Using Quaternions
  7. Erroneous examples as desirable difficulty
  8. Optimal scheduling of AGVs in a reentrant blocking job-shop
  9. You cannot not transact - Big Data und Transaktionalität
  10. Wavelet functions for rejecting spurious values
  11. Frame-based Data Factorizations
  12. Developing a Complex Portrait of Content Teaching for Multilingual Learners via Nonlinear Theoretical Understandings
  13. Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions:
  14. Using measures of reading time regularity (RTR) to quantify eye movement dynamics, and how they are shaped by linguistic information
  15. Early Edema Detection Based on the Examination of Multidimensional Ultra-Wide band Data
  16. Effectiveness of the world network of biosphere reserves in maintaining forest ecosystem functions
  17. Sliding Mode Control of an Inductive Power Transmission System with Maximum Efficiency
  18. Cascade PID Controllers Applied on Level and Flow Systems in a SMAR Didactic Plant
  19. Audio-Hacks
  20. A Two-Stage Sliding-Mode High-Gain Observer to Reduce Uncertainties and Disturbances Effects for Sensorless Control in Automotive Applications
  21. What motivates people to use energy feedback systems? A multiple goal approach to predict long-term usage behaviour in daily life
  22. An Outcome-Oriented, Social-Ecological Framework for Assessing Protected Area Effectiveness
  23. Dealing with inclusion–teachers’ assessment of internal and external resources
  24. A utilitarian notion of responsibility for sustainability
  25. Emancipative Values and Non-violent Protest
  26. Pushing the Envelope: Creating Public Value in the Labor Market
  27. A generalized α-level decomposition concept for numerical fuzzy calculus
  28. Disentangling who is who during rhizosphere acidification in root interactions: combining fluorescence with optode techniques
  29. The Mobile Phone: From an Instrument of Microcoordination to a Universal Control Device
  30. Modeling Grounding Processes in Chat-based CSCL