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Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach
The profile generation is highly complex for roughness measurement using a capacitive sensor because of the small peak-to-peak width of the machined surface and the close proximity of the sensor setting with the machining setup which has the chance of damaging the sensor. Considering these shortcomi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607500/ https://www.ncbi.nlm.nih.gov/pubmed/36296099 http://dx.doi.org/10.3390/mi13101746 |
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author | Mathiyazhagan, Rajendran SampathKumar, SenthamaraiKannan Karthikeyan, Palanisamy |
author_facet | Mathiyazhagan, Rajendran SampathKumar, SenthamaraiKannan Karthikeyan, Palanisamy |
author_sort | Mathiyazhagan, Rajendran |
collection | PubMed |
description | The profile generation is highly complex for roughness measurement using a capacitive sensor because of the small peak-to-peak width of the machined surface and the close proximity of the sensor setting with the machining setup which has the chance of damaging the sensor. Considering these shortcomings, a higher sensor sensing diameter with an appropriate resolution has been selected for a single-shot approach. An automated micro gantry XYZ system is integrated with a capacitive sensor to precisely target, move, and measure the roughness. For investigation, a vertical milled surface with a wide roughness range has been prepared. A Stylus profilometer has been used to measure the roughness (R(a)) of the specimens for comparison. An experiment has been conducted on the above system with a 5.6 mm capacitance sensor, and an estimation model using regression has been obtained using sensor data to estimate R(a). In conclusion, the single-shot approach with a 5.6 mm sensing diameter sensor, the proposed micro gantry system, and the estimation model performs better in instantaneous noncontact measurement in the range of 0.3 µm to 2.9 µm roughness estimation. The influence of tilt and waviness has also been discussed using FEA analysis. |
format | Online Article Text |
id | pubmed-9607500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96075002022-10-28 Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach Mathiyazhagan, Rajendran SampathKumar, SenthamaraiKannan Karthikeyan, Palanisamy Micromachines (Basel) Article The profile generation is highly complex for roughness measurement using a capacitive sensor because of the small peak-to-peak width of the machined surface and the close proximity of the sensor setting with the machining setup which has the chance of damaging the sensor. Considering these shortcomings, a higher sensor sensing diameter with an appropriate resolution has been selected for a single-shot approach. An automated micro gantry XYZ system is integrated with a capacitive sensor to precisely target, move, and measure the roughness. For investigation, a vertical milled surface with a wide roughness range has been prepared. A Stylus profilometer has been used to measure the roughness (R(a)) of the specimens for comparison. An experiment has been conducted on the above system with a 5.6 mm capacitance sensor, and an estimation model using regression has been obtained using sensor data to estimate R(a). In conclusion, the single-shot approach with a 5.6 mm sensing diameter sensor, the proposed micro gantry system, and the estimation model performs better in instantaneous noncontact measurement in the range of 0.3 µm to 2.9 µm roughness estimation. The influence of tilt and waviness has also been discussed using FEA analysis. MDPI 2022-10-15 /pmc/articles/PMC9607500/ /pubmed/36296099 http://dx.doi.org/10.3390/mi13101746 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mathiyazhagan, Rajendran SampathKumar, SenthamaraiKannan Karthikeyan, Palanisamy Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach |
title | Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach |
title_full | Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach |
title_fullStr | Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach |
title_full_unstemmed | Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach |
title_short | Estimation of Surface Roughness on Milled Surface Using Capacitance Sensor Based Micro Gantry System through Single-Shot Approach |
title_sort | estimation of surface roughness on milled surface using capacitance sensor based micro gantry system through single-shot approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607500/ https://www.ncbi.nlm.nih.gov/pubmed/36296099 http://dx.doi.org/10.3390/mi13101746 |
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