An Efficient Method for Laser Welding Depth Determination Using Optical Coherence Tomography
<p>Laser welding depth measuring system using SD-OCT and two-step welding depth extraction method based on DBSCAN and percentile filter. The black points show the OCT measurements. The red points mark the detected noise.</p> "> Figure 2
<p>(<b>a</b>) Laser welding system; (<b>b</b>) SD-OCT system.</p> "> Figure 3
<p>(<b>a</b>) Raw OCT data for laser welding. (<b>b</b>) Comparison of the raw OCT data and the longitudinal cross section of the welded seam (micrograph). The blue line marks the actual weld depth, which is extracted from the longitudinal cross section.</p> "> Figure 4
<p>(<b>a</b>) Categorization of the raw OCT data into three regions: Wall, Bottom and Multi reflection. (<b>b</b>) Interaction of the OCT measuring beam with keyhole [<a href="#B27-sensors-23-05223" class="html-bibr">27</a>].</p> "> Figure 5
<p>Outlier detection of the measured points during the welding process using LOF. The red points mark the detected outliers.</p> "> Figure 6
<p>Schematic diagram of DBSCAN.</p> "> Figure 7
<p>Outlier detection of the measured points during the welding process using DBSCAN. The red points mark the detected outliers.</p> "> Figure 8
<p>Welding depth extracted from the raw OCT-data using percentile filter (red) and the actual weld depth (blue). The red arrows mark the incorrect welding depth extraction.</p> "> Figure 9
<p>Welding depth extracted from the cleaned OCT data. (<b>a</b>–<b>c</b>) Outlier detection using LOF; (<b>d</b>–<b>f</b>) outlier detection using DBSCAN.</p> "> Figure 10
<p>(<b>a</b>–<b>d</b>) Outlier detection of the measured points during the welding process using DBSCAN. The red points mark the detected outliers. The various colors in (<b>d</b>) show several groups of cluster. (<b>e</b>) Dependence of the average error in percent (color coded) on the epsilon and the minPts applied to DBSCAN. The parameters of DBSCAN are epsilon = 0.08, minPts = 750 for point A, epsilon = 0.05, minPts = 1000 for point B and epsilon = 0.1, minPts = 20 for point C.</p> "> Figure 10 Cont.
<p>(<b>a</b>–<b>d</b>) Outlier detection of the measured points during the welding process using DBSCAN. The red points mark the detected outliers. The various colors in (<b>d</b>) show several groups of cluster. (<b>e</b>) Dependence of the average error in percent (color coded) on the epsilon and the minPts applied to DBSCAN. The parameters of DBSCAN are epsilon = 0.08, minPts = 750 for point A, epsilon = 0.05, minPts = 1000 for point B and epsilon = 0.1, minPts = 20 for point C.</p> "> Figure 11
<p>Average error E in percent (color coded) as a function of percentile <span class="html-italic">p</span> and window length <span class="html-italic">L</span> applied to percentile filter.</p> "> Figure 12
<p>Two groups of experiments: (<b>a</b>–<b>c</b>) group 1; (<b>d</b>–<b>f</b>) group 2. The blue line in (<b>a</b>,<b>d</b>) marks the actual weld depth, which is extracted from the longitudinal cross section. The red points in (<b>b</b>,<b>e</b>) mark the detected outliers.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Laser Welding Depth Measurement Using SD-OCT
2.2. Experimental Setup
2.3. Analysis of the Raw OCT Data
2.4. Outlier Detection of the Raw OCT Data
- Core point, if the number of points in an epsilon radius of it is not less than minPts;
- Border point, if it is not a core point but within an epsilon radius of a core point;
- Outlier, if neither a core point nor a border point.
Algorithm 1 Abstract DBSCAN Algorithm |
Inputs: dataset, epsilon, minPts |
|
|
|
|
|
Output: outliers |
2.5. Percentile Filter
3. Results and Discussion
3.1. Welding Depth Extracted from the Cleaned OCT Data
3.2. Evaluation of the DBSCAN
3.3. Evaluation of the Percentile Filter
3.4. Repeatability Validation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Element | C | Si | Mn | P | S | Cr | Ni | Cu | Fe |
---|---|---|---|---|---|---|---|---|---|
Mass fraction /% | 0.18 | 0.25 | 0.5 | 0.016 | 0.018 | 0.01 | 0.01 | 0.01 | Others |
Percentile Filter | LOF + Percentile Filter | DBSCAN + Percentile Filter | |
---|---|---|---|
Average error (%) | 4.4 | 3.9 | 3.3 |
Processing time (ms) | 183 | 457 | 581 |
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Xie, G.; Wang, S.; Zhang, Y.; Hu, B.; Fu, Y.; Yu, Q.; Li, Y. An Efficient Method for Laser Welding Depth Determination Using Optical Coherence Tomography. Sensors 2023, 23, 5223. https://doi.org/10.3390/s23115223
Xie G, Wang S, Zhang Y, Hu B, Fu Y, Yu Q, Li Y. An Efficient Method for Laser Welding Depth Determination Using Optical Coherence Tomography. Sensors. 2023; 23(11):5223. https://doi.org/10.3390/s23115223
Chicago/Turabian StyleXie, Guanming, Sanhong Wang, Yueqiang Zhang, Biao Hu, Yu Fu, Qifeng Yu, and You Li. 2023. "An Efficient Method for Laser Welding Depth Determination Using Optical Coherence Tomography" Sensors 23, no. 11: 5223. https://doi.org/10.3390/s23115223