Surface topography characterization using a simple optical device and artificial neural networks

Angermann, Christoph; Haltmeier, Markus; Laubichler, Christian; Jónsson, Steinbjörn; Schwab, Matthias; Moravová, Adéla; Kiesling, Constantin; Kober, Martin; Fimml, Wolfgang

doi:10.1016/j.engappai.2023.106337

Cited by 8 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical characterisation was found to be suitable for milled magnesium alloy roughness parameters supported by ANN methods [23]. ANN techniques are encouraged for studying surface topography as they use a simple optical device [24]. Furthermore, milled surface roughness profiles can be predicted with a connection of ANN and fractal geometry [25].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods

Podulka,

Kulisz,

Antosz

2024

Materials

View full text Add to dashboard Cite

Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods

Podulka,

Kulisz,

Antosz

2024

Materials

View full text Add to dashboard Cite

show abstract

Unsupervised single-shot depth estimation using perceptual reconstruction

Angermann

Schwab

Haltmeier

et al. 2023

Machine Vision and Applications

View full text Add to dashboard Cite

Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synthesis out of a simple RGB modality. Most of these models are based on paired RGB-depth data and/or the availability of video sequences and stereo images. However, the lack of RGB-depth pairs, video sequences, or stereo images makes depth estimation a challenging task that needs to be explored in more detail. This study builds on recent advances in the field of generative neural networks in order to establish fully unsupervised single-shot depth estimation. Two generators for RGB-to-depth and depth-to-RGB transfer are implemented and simultaneously optimized using the Wasserstein-1 distance, a novel perceptual reconstruction term, and hand-crafted image filters. We comprehensively evaluate the models using a custom-generated industrial surface depth data set as well as the Texas 3D Face Recognition Database, the CelebAMask-HQ database of human portraits and the SURREAL dataset that records body depth. For each evaluation dataset, the proposed method shows a significant increase in depth accuracy compared to state-of-the-art single-image transfer methods.

show abstract

Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds

Serna-Carrizales,

Zárate-Guzmán,

Flores-Ramírez

et al. 2024

Chemosphere

View full text Add to dashboard Cite

Surface topography characterization using a simple optical device and artificial neural networks

Cited by 8 publications

References 26 publications

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods

Unsupervised single-shot depth estimation using perceptual reconstruction

Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds

Contact Info

Product

Resources

About