A simplified numerical model to predict geometrical distortions of thin-walled aluminum airframe components
Machined thin-walled airframe components frequently show geometrical distortions due to the alteration of initial bulk residual stresses (IBRS) and machining-induced residual stresses (MIRS) in the final surface. Many authors have proposed numerical models to predict distortions of machined thin-walled components considering the material removal sequence as well as the MIRS. The weight of the component and the boundary conditions also significantly affect the part distortion measurement. Unfortunately, these effects have not been addressed in the literature. This work aims to develop a simplified numerical model to predict geometrical distortions in thin-walled parts. This model does not simulate the material removal process, and IBRS are directly assigned to the final part. The model includes the weight of the part, and an algorithm is proposed to identify the contact points while measuring the part distortion automatically. To validate the model, an aluminum 7475-T7351 part was machined, and geometrical distortions were measured in a coordinate measurement machine under different configurations. These measurement configurations allowed us to address the effect of the weight and boundary conditions on geometrical distortions. The proposed model predicted distortions with a deviation < 15% with respect to experiments and reduced the computational cost by approximately 10 times with respect to the complete model.
Authors
- A. Madariaga (ORCID: https://orcid.org/0000-0002-3952-8457)
- G. Ortiz-de-Zarate (ORCID: https://orcid.org/0000-0001-5329-1751)
- P.J. Arrazola (ORCID: https://orcid.org/0000-0001-5500-8956)
- Zeeshan Yousaf Warraich
Institutions
- Mondragon Unibertsitatea (ES)
Publication Details
- Journal
- Procedia CIRP
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.procir.2025.02.093
- Primary Topic
- Aluminum Alloy Microstructure Properties
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Eusko Jaurlaritza
- Ministerio de Ciencia e Innovación