Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms
| Autor: | Serkan Keser, Şekip Esat Hayber |
|---|---|
| Přispěvatelé: | Mühendislik-Mimarlık Fakültesi, Serkan Keser / 0000-0001-8435-0507, Şekip Esat Hayber / 0000-0003-0062-3817 |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Discrete wavelet transform
Computer science Feature vector SVM KNN Machine learning computer.software_genre Surface roughness Fiber optic tactile sensor Electrical and Electronic Engineering DWT Instrumentation Sandpaper business.industry Metals and Alloys Surface roughness recognition Condensed Matter Physics Surfaces Coatings and Films Electronic Optical and Magnetic Materials Support vector machine Analog signal Interferometry Kernel (statistics) Artificial intelligence business computer Algorithm Tactile sensor |
| Popis: | In this study, a sensor tip with a metallic hemispherical nozzle tip (MHNT) design based on the Fabry-Perot interferometer was developed for surface roughness recognition (SRR). Sandpaper samples with ten different arithmetical mean deviations of the surface (Sa) values were used as surfaces to be recognized. The feature vectors were found by applying the discrete wavelet transform (DWT) to the analog signals obtained from the sandpaper samples. Machine learning (ML) algorithms K-nearest neighbor (KNN) and support vector machine (SVM) were used for classification. An in-depth recognition process was carried out using the classifiers’ different length criteria and kernel types. In the test process, each category consists of two sub-categories as testing within the training dataset (TWITD) and testing without the training dataset (TWOTD). The experiments were carried out in a controlled manner with the conveyor belt system (CBS) and manual. As a result of the experimental studies, the average recognition rates (Rave) for CBS were found as 84.2% and 81.6% for TWITD and TWOTD, while the Rave for the manual are found as 80% and 77.5% for TWITD and TWOTD, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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