Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
February 21, 2026Additive manufacturingOpen Access

Prediction of process instability by WAAM in-process monitoring and CTWD drift estimation

View Full Paper
Ask AI
Bookmark
Share

Authors

SOSoumaya OueslatiMRMathieu RitouBFBenoît Furet

Discussion

Loading...

Member takes

Overview

Predicts process instability based on CTWD drift in WAAM technology, suggesting a method for ensuring material integrity.

Key Points

  • The aim is to develop a predictive model for process instability in Wire Arc Additive Manufacturing caused by changes in CTWD.
  • Collected in-process monitoring data from a robotic cell.
  • Extracted sixteen features to estimate CTWD drift.
  • Performed feature selection and identified key indicators via regression analysis.
  • Developed a Support Vector Regression model to predict instability boundaries.
  • Predictions of instability effectively aligned with actual observations.
  • Metallographic examination confirmed instability effects on material integrity.
  • The approach integrated real-time monitoring with predictive modeling for improvement in WAAM control.

Cite This Study

Oueslati et al. (2026) studied this question.

synapsesocial.com/papers/69994b41873532290d01f65dhttps://doi.org/10.1016/j.addma.2026.105127
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multi-sensor in process monitoring for WAAM: Detection of process instability in electrical signals2024 · 1 citations
  2. 2Physical characterization and detection of process instabilities in wire arc additive manufacturing through arc-cycle feature extraction2026
  3. 3Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression2024 · 4 citations
  4. 4Vision based process monitoring in wire arc additive manufacturing (WAAM)2024 · 52 citations
  5. 5Heat input control and deep learning-based indirect measure of process and deposition stability in Wire Arc Additive Manufacturing2026 · 3 citations