Abstract
The scope of this work is to present a reverse engineering (RE) methodology to achieve accurate polygon models for 3D printing or additive manufacturing (AM) applications, as well as NURBS (Non-Uniform Rational B-Splines) surfaces for advanced machining processes. The accuracy of the 3D models generated by this RE process depends on the data acquisition system, the scanning conditions and the data processing techniques. To carry out this study, workpieces of different material and geometry were selected, using X-ray computed tomography (XRCT) and a Laser Scanner (LS) as data acquisition systems for scanning purposes. Once this is done, this work focuses on the data processing step in order to assess the accuracy of applying different processing techniques. Special attention is given to the XRCT data processing step. For that reason, the models generated from the LS point clouds processing step were utilized as a reference to perform the deviation analysis. Nonetheless, the proposed methodology could be applied for both data inputs: 2D cross-sectional images and point clouds. Finally, the target outputs of this data processing chain were evaluated due to their own reverse engineering applications, highlighting the promising future of the proposed methodology.
Highlights
IntroductionReverse Engineering (RE) is defined as the regression of a common forward engineering process
Reverse Engineering (RE) is defined as the regression of a common forward engineering process.This technique was boosted during the Second World War, and, currently, it is widely implemented in the industrial sector [1,2,3,4] and the medical field [5,6,7], as well as in other areas
The threshold values estimated by implementing these processing techniques were analyzed, due to the accuracy of the 3D models generated from these methods depending on these calculated values
Summary
Reverse Engineering (RE) is defined as the regression of a common forward engineering process This technique was boosted during the Second World War, and, currently, it is widely implemented in the industrial sector [1,2,3,4] and the medical field [5,6,7], as well as in other areas. This fact is due to its usefulness for getting technical information from a real part by using different data acquisition methods and processing this information in order to achieve the virtual model of the part. The outputs of these methods are frequently 2D cross-sectional images or point clouds, depending on the selected method. 2D cross-sectional images are the general output
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