Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy
The topography of surfaces produced by metal additive manufacturing is a challenge for optical measurement systems such as focus variation microscopes. These irregularities can lead to artifacts, such as incorrectly measured protrusions or spikes, hampering reliable topographic characterization. In...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/11/11/1011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846152649979723776 |
|---|---|
| author | Xin Xu Tobias Pahl Sebastian Hagemeier Peter Lehmann |
| author_facet | Xin Xu Tobias Pahl Sebastian Hagemeier Peter Lehmann |
| author_sort | Xin Xu |
| collection | DOAJ |
| description | The topography of surfaces produced by metal additive manufacturing is a challenge for optical measurement systems such as focus variation microscopes. These irregularities can lead to artifacts, such as incorrectly measured protrusions or spikes, hampering reliable topographic characterization. In order to eliminate this problem, we introduce a new algorithm based on dual convolving a vertical Sobel operator with cross sections of an image stack parallel to the scanning direction of the so-called depth scan. This has proven beneficial in order to distinguish the focus region from out-of-focus areas where outliers are frequently detected. This paper introduces a method for deriving self-adaptive thresholds from the convolution result and compares the effects of different operators in creating self-adaptive thresholds. Additionally, a simulation model of focus variation microscopy is introduced to validate both the measuring system and the proposed algorithm, thereby enhancing the overall performance of focus variation microscopy. Finally, comparisons of measurement results on rough metal additive manufacturing workpieces with and without self-adaptive thresholds are discussed to demonstrate the algorithm’s effectiveness.The utilization of self-adaptive thresholds demonstrably reduces the uncertainty range in roughness parameter calculations. For example, in the case of an additive manufactured metal sample due to outlier elimination, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>z</mi></mrow></semantics></math></inline-formula> roughness value reduces from 543 µm to 413 µm. |
| format | Article |
| id | doaj-art-858d06ce0f154adc94a5ab9f2118da2e |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-858d06ce0f154adc94a5ab9f2118da2e2024-11-26T18:18:16ZengMDPI AGPhotonics2304-67322024-10-011111101110.3390/photonics11111011Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation MicroscopyXin Xu0Tobias Pahl1Sebastian Hagemeier2Peter Lehmann3Measurement Technology Group, Faculty of Electrical Engineering and Computer Science, University of Kassel, Wilhelmshoeher Allee 71, 34121 Kassel, GermanyMeasurement Technology Group, Faculty of Electrical Engineering and Computer Science, University of Kassel, Wilhelmshoeher Allee 71, 34121 Kassel, GermanyMeasurement Technology Group, Faculty of Electrical Engineering and Computer Science, University of Kassel, Wilhelmshoeher Allee 71, 34121 Kassel, GermanyMeasurement Technology Group, Faculty of Electrical Engineering and Computer Science, University of Kassel, Wilhelmshoeher Allee 71, 34121 Kassel, GermanyThe topography of surfaces produced by metal additive manufacturing is a challenge for optical measurement systems such as focus variation microscopes. These irregularities can lead to artifacts, such as incorrectly measured protrusions or spikes, hampering reliable topographic characterization. In order to eliminate this problem, we introduce a new algorithm based on dual convolving a vertical Sobel operator with cross sections of an image stack parallel to the scanning direction of the so-called depth scan. This has proven beneficial in order to distinguish the focus region from out-of-focus areas where outliers are frequently detected. This paper introduces a method for deriving self-adaptive thresholds from the convolution result and compares the effects of different operators in creating self-adaptive thresholds. Additionally, a simulation model of focus variation microscopy is introduced to validate both the measuring system and the proposed algorithm, thereby enhancing the overall performance of focus variation microscopy. Finally, comparisons of measurement results on rough metal additive manufacturing workpieces with and without self-adaptive thresholds are discussed to demonstrate the algorithm’s effectiveness.The utilization of self-adaptive thresholds demonstrably reduces the uncertainty range in roughness parameter calculations. For example, in the case of an additive manufactured metal sample due to outlier elimination, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>z</mi></mrow></semantics></math></inline-formula> roughness value reduces from 543 µm to 413 µm.https://www.mdpi.com/2304-6732/11/11/1011metal additive manufactured surfacesreduction of measurement artifactsfocus variation microscopyprofilometry3D surface topography measurementroughness measurement |
| spellingShingle | Xin Xu Tobias Pahl Sebastian Hagemeier Peter Lehmann Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy Photonics metal additive manufactured surfaces reduction of measurement artifacts focus variation microscopy profilometry 3D surface topography measurement roughness measurement |
| title | Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy |
| title_full | Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy |
| title_fullStr | Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy |
| title_full_unstemmed | Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy |
| title_short | Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy |
| title_sort | adaptive threshold algorithm for outlier elimination in 3d topography data of metal additive manufactured surfaces obtained from focus variation microscopy |
| topic | metal additive manufactured surfaces reduction of measurement artifacts focus variation microscopy profilometry 3D surface topography measurement roughness measurement |
| url | https://www.mdpi.com/2304-6732/11/11/1011 |
| work_keys_str_mv | AT xinxu adaptivethresholdalgorithmforoutliereliminationin3dtopographydataofmetaladditivemanufacturedsurfacesobtainedfromfocusvariationmicroscopy AT tobiaspahl adaptivethresholdalgorithmforoutliereliminationin3dtopographydataofmetaladditivemanufacturedsurfacesobtainedfromfocusvariationmicroscopy AT sebastianhagemeier adaptivethresholdalgorithmforoutliereliminationin3dtopographydataofmetaladditivemanufacturedsurfacesobtainedfromfocusvariationmicroscopy AT peterlehmann adaptivethresholdalgorithmforoutliereliminationin3dtopographydataofmetaladditivemanufacturedsurfacesobtainedfromfocusvariationmicroscopy |