Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis

Mario Vozza, Joseph Polden, Giulio Mattera (), Gianfranco Piscopo, Silvestro Vespoli and Luigi Nele
Additional contact information
Mario Vozza: Department of Control and Computer Engineering (DAUIN), Polytechnic University of Turin, 10129 Turin, Italy
Joseph Polden: School of Mechanical Materials Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2500, Australia
Giulio Mattera: Department of Chemical, Materials and Production Engineering, University of Naples Federico II, 80126 Naples, Italy
Gianfranco Piscopo: Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, 80126 Naples, Italy
Silvestro Vespoli: Department of Chemical, Materials and Production Engineering, University of Naples Federico II, 80126 Naples, Italy
Luigi Nele: Department of Chemical, Materials and Production Engineering, University of Naples Federico II, 80126 Naples, Italy

Mathematics, 2024, vol. 12, issue 21, 1-17

Abstract: Anomaly detection is an important feature in modern additive manufacturing (AM) systems to ensure quality of the produced components. Although this topic is well discussed in the literature, current methods rely on black-box approaches, limiting our understanding of why anomalies occur, making complex the root cause identification and the consequent decision support about the action to take to mitigate them. This work addresses these limitations by proposing a structured workflow designed to enhance the explainability of anomaly detection models. Using the wire arc additive manufacturing (WAAM) process as a case study, we examined 14 wall structures printed with INVAR36 alloy under varying process parameters, producing both defect-free and defective parts. These parts were classified based on surface appearance and welding camera images. We collected welding current and voltage data at a 5 kHz sampling rate and extracted features from both time and frequency domains using a knowledge-based approach. Isolation Forest, k-Nearest Neighbor, Artificial Neural Network, XGBoost, and LGBM models were trained on these features, and the results shown best performance of boosting models, achieving F1 scores of 0.927 and 0.945, respectively. These models presented higher performance compared to other models like k-Nearest Neighbor, whereas Isolation Forest and Artificial Neural Network posses lower performance due to overfitting, with an F1 score of 0.507 and 0.56, respectively. Then, by leveraging the feature importance capabilities of these models, we identified key signal characteristics that distinguish between normal and anomalous behavior, improving the explainability of the detection process and in general about the process physics.

Keywords: machine learning; explainable AI; anomaly detection; additive manufacturing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/21/3414/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/21/3414/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:21:p:3414-:d:1511392

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().