This paper considers the fitting of a CAD template model to tessellated data as strategy to implement a reverse engineering process that aims at the reconstruction of a parametric associative CAD model. The reconstruction methodology, called Template-Based CAD Reconstruction (TCRT), has been presented and fully discussed in a previous paper Buonamici et al. (J Comput Des Eng 5:145–159, 2018). The present paper focuses on the study of a fast and robust strategy to perform the fitting of the Template CAD Model to reference data. The study explores how different optimization strategies and evaluation metrics can affect a parametric CAD-fitting methodology. Two different optimization algorithms (PSO and GA) and three formulations of the objective function are tested to find the most effective combination. Reconstruction test cases are presented and discussed in the text.
Reverse engineering by CAD template fitting: study of a fast and robust template‑fitting strategy / Francesco Buonamici, Monica Carfagni, Rocco Furferi, Yary Volpe, Lapo Governi. - In: ENGINEERING WITH COMPUTERS. - ISSN 0177-0667. - ELETTRONICO. - 37:(2021), pp. 2803-2821. [10.1007/s00366-020-00966-4]
Reverse engineering by CAD template fitting: study of a fast and robust template‑fitting strategy
Francesco Buonamici;Monica Carfagni;Rocco Furferi;Yary Volpe;Lapo Governi
2021
Abstract
This paper considers the fitting of a CAD template model to tessellated data as strategy to implement a reverse engineering process that aims at the reconstruction of a parametric associative CAD model. The reconstruction methodology, called Template-Based CAD Reconstruction (TCRT), has been presented and fully discussed in a previous paper Buonamici et al. (J Comput Des Eng 5:145–159, 2018). The present paper focuses on the study of a fast and robust strategy to perform the fitting of the Template CAD Model to reference data. The study explores how different optimization strategies and evaluation metrics can affect a parametric CAD-fitting methodology. Two different optimization algorithms (PSO and GA) and three formulations of the objective function are tested to find the most effective combination. Reconstruction test cases are presented and discussed in the text.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.