Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression

Giulio Mattera, Gianfranco Piscopo, Maria Longobardi, Massimiliano Giacalone () and Luigi Nele
Additional contact information
Giulio Mattera: Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, 80125 Naples, Italy
Gianfranco Piscopo: Department of Mathematics and Applications “Renato Caccioppoli”, University of Naples “Federico II”, 80125 Naples, Italy
Maria Longobardi: Department of Mathematics and Applications “Renato Caccioppoli”, University of Naples “Federico II”, 80125 Naples, Italy
Massimiliano Giacalone: Department of Economics, University of Campania “Luigi Vanvitelli”, 81043 Capua, CE, Italy
Luigi Nele: Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, 80125 Naples, Italy

Mathematics, 2024, vol. 12, issue 16, 1-18

Abstract: Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well.

Keywords: clustering regression; multivariate regression; applied statistics; additive manufacturing; neural networks; machine learning (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/2227-7390/12/16/2559/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/16/2559/ (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:16:p:2559-:d:1459302

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 ().