Detection of paint polishing defects

Rebeggiani, Sabina; Wagner, Michael; Mazal, Jonas; Rosén, Bengt-Göran; Dahlén, Malin

doi:10.1088/2051-672x/aabfb5

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The system originates from a patented technology [15], that has been modernized and enhanced to quantify polishing roses [16][17][18]. It is based on two sets of four RGB COB LED bars containing eight 30 W COB LEDs each [19].…”

Section: Developed Measurement Set-upmentioning

confidence: 99%

A measure of perceived quality to secure the polishability of new paint systems

Rebeggiani

Rosén

2023

Surf. Topogr.: Metrol. Prop.

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The automotive industry continuously strives to reduce their environmental impact. For the paint shop it means to introduce more sustainable paint concepts, while maintaining the production rate and retain the right surface appearance that is crucial for the vehicle's perceived quality. Today most painted parts are visually inspected and, if needed, manually repaired by abrasive polishing to eliminate spot defects. The repair process consists of one sanding step to remove the defect, and one or two rubbing/polishing steps to restore the surface, but still it tends to be a non-reliable process leaving patterns or clusters of shallow micro scratches seen as three-dimensional shapes moving over the surface when viewed from different angles like holograms. These so called ‘polishing roses’ are hard to detect in artificial light but clearly visible in sunlight and therefore they constitute a constant quality issue. Accurate polishing procedures in combination with more objective inspection techniques would secure a high surface quality – but what is ‘accurate’? The overall scope of the study was to deepen the knowledge of paint systems to develop test routines for the polishability of coatings already during the development stage, and thereby ease the implementation of new coating systems in production. The study was based on collected process data from professional polishers to define a process window based on key parameters for successful end-of-line repairs of coated surfaces, i.e. strategies minimizing the occurrence of visible polishing traces. A CNC-machine was built up for the purpose to systematically test and evaluate new coating systems and repair procedures. The surface estimation was made by visual inspections as well as by a further developed photometric stereo system providing quantitative images of remaining repair traces.

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Section: Developed Measurement Set-upmentioning

confidence: 99%

A measure of perceived quality to secure the polishability of new paint systems

Rebeggiani

Rosén

2023

Surf. Topogr.: Metrol. Prop.

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“…With the development of intelligent manufacturing, the surface quality of workpieces is of great importance for product supply quality [1]. However, caused by the various influence factors (e.g.…”

Section: Introductionmentioning

confidence: 99%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

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An improved MobileNet-SSD algorithm for automatic defect detection on vehicle body paint

Zhang

Zhu

et al. 2020

Multimed Tools Appl

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Detection of paint polishing defects

Cited by 4 publications

References 9 publications

A measure of perceived quality to secure the polishability of new paint systems

A measure of perceived quality to secure the polishability of new paint systems

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

An improved MobileNet-SSD algorithm for automatic defect detection on vehicle body paint

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