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Sensitivity analysis of geometric errors in additive manufacturing medical models
Jose Miguel Pinto1, Cristobal Arrieta1, Marcelo E Andia2
1Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile.
Medical Engineering & Physics
|February 5, 2015
Summary
Additive manufacturing (AM) model accuracy is vital for medical uses. This study quantifies errors from AM steps, finding triangulation resolution and segmentation significantly impact model accuracy, often leading to overestimations.
Area of Science:
- Medical Engineering
- Biomedical Imaging
- Additive Manufacturing
Background:
- Additive manufacturing (AM) models are crucial for medical applications like surgical planning and prosthesis design.
- Ensuring the accuracy of these AM models is paramount for their effective use in clinical settings.
Purpose of the Study:
- To identify and quantify the contribution of individual building steps to the overall error in AM models.
- To determine which factors most significantly impact the accuracy of medical AM models.
Main Methods:
- A sensitivity analysis was performed by modifying parameters of each AM building step (image acquisition, segmentation, triangulation, printing, infiltration).
- Global and local indexes were used to evaluate overall and surface-distributed errors, respectively, comparing modified models to a reference.
Main Results:
- The standard AM process generally leads to overestimation of model dimensions compared to original structures.
- Triangulation resolution and segmentation threshold were identified as critical factors influencing error magnitude.
- Errors were found to be concentrated in regions of high curvature on the AM models.
Conclusions:
- Significant improvements in AM model accuracy can be achieved by optimizing triangulation and printing resolutions.
- There is a critical need to refine standard building processes, especially segmentation algorithms, to reduce errors in medical AM applications.
