Precision 3D volume measurement of transparent adhesives via spectrally optimized line laser scanning and enhanced centroid extraction
Transparent adhesives pose a fundamental difficulty for structured light and line laser measurement: the material scatters and refracts the illumination internally, producing multiple false stripe peaks and erroneous centroid estimates. Standard centroid extraction algorithms assume a single dominant peak and fail under these conditions, leading to large volume errors. This paper resolves the problem in two complementary steps. First, a spectral optimisation procedure selects the laser wavelength at which the adhesive has maximum absorption, minimising internal scattering. Second, an enhanced centroid extraction algorithm robustly identifies the true surface return even when secondary reflections are present. The integrated system is validated across a range of adhesive types, bead volumes, and substrate materials representative of automotive and consumer electronics assembly lines. Published in Measurement Science and Technology (2025), the work demonstrates sub-millimetre volumetric accuracy and provides a practical, drop-in replacement for existing line laser inspection stations without requiring hardware redesign.
This journal page also groups the earlier ICIVC 2025 conference version, which introduced the transparent-adhesive volume-measurement workflow based on line laser scanning.
Problem setting
Measuring the volume of transparent adhesive beads on production lines is a critical quality-control challenge because conventional line laser scanners suffer from light scattering and specular reflections inside the adhesive, corrupting the detected laser stripe centroid. This work addresses this by selecting an optimised laser wavelength that maximises absorption contrast within the adhesive, paired with an enhanced centroid extraction algorithm that suppresses multi-reflection noise. The resulting system achieves sub-millimetre volume accuracy on transparent adhesive samples across varying bead sizes and surface conditions.
In the broader publication record, this work appears in Measurement Science and Technology, 36(11):115206. The visual notes below pair the paper’s original figures with a concise reading of the method, experimental setup, and reported results.
Method and visual evidence
The method combines domain-specific measurements with an algorithmic representation that exposes the relevant structure, then refines it into a reconstruction, correspondence, segmentation, measurement, or decision result.
The extracted figures below show the main pipeline and representative experimental evidence.
Method overview. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.
Representation and setup. This image is extracted from an embedded PDF image object on page 10, then recomposed for web display.
Experimental evidence. This image is extracted from an embedded PDF image object on page 12, then recomposed for web display.
Result comparison. This image is extracted from an embedded PDF image object on page 12, then recomposed for web display.
Additional visual result. This image is extracted from an embedded PDF image object on page 15, then recomposed for web display.
Results and impact
The evaluation reported in Measurement Science and Technology, 36(11):115206 uses the extracted figures above to show the method’s measurement, reconstruction, segmentation, matching, or diagnostic behavior on representative experiments. These visuals are paired with the paper’s quantitative or qualitative analysis to make the workflow easier to inspect from the homepage.
Source handling
I extracted 29 candidate image objects from paper.pdf and generated the compressed WebP figures used on this page. The local PDF was also optimized from 2,365,417 bytes to 2,343,253 bytes.