Optimization of Artec Leo 3D Scanner parameters for object accuracy using the Taguchi Method

Ananda Jafron Rhionaldo; Rifelino Rifelino; Delima Yanti Sari; Febri Prasetya

doi:10.58712/jerel.v4i2.186

Authors

Ananda Jafron Rhionaldo Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Padang, Indonesia
Rifelino Rifelino Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Padang, Indonesia
Delima Yanti Sari Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Padang, Indonesia
Febri Prasetya Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Padang, Indonesia

DOI:

https://doi.org/10.58712/jerel.v4i2.186

Keywords:

3D scanner, Artec Leo, scanning accuracy, Taguchi, geometric deviation

Abstract

This study focuses on optimizing the scanning parameters of the Artec Leo 3D Scanner to enhance scanning accuracy by minimizing geometric deviations. The experimental design utilizes the Taguchi L4(2³) orthogonal array method to examine the influence of three scanning factors: distance, angle, and lighting at two levels. A 16-inch car wheel, chosen for its geometric complexity, was scanned under various parameter combinations. The results indicated that the combination of indoor lighting, a 45° angle, and a scanning distance of 100 cm yielded the smallest deviation (0.5%) and the highest signal-to-noise (S/N) ratio (6.02 dB). Analysis of variance (ANOVA) revealed that the scanning distance contributed the most to the variation in scanning accuracy (65.09%), followed by lighting (34.64%) and angle (0.27%). A confirmation test with the optimal parameters further reduced the deviation to 0.4%, validating the effectiveness of the Taguchi method for parameter optimization. This study’s findings contribute valuable insights for industries that require high-precision 3D models, such as aerospace, automotive, and healthcare. The research demonstrates the importance of optimizing scanning parameters and offers a practical approach to improving 3D scanning processes. Future research can expand by exploring environmental conditions, scan resolution, and machine learning integration for real-time adjustments.

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