Portable non-contact surface roughness measuring device

Abidin, Zainal; Hung, Tsung-Pin; Zahid, Mohsine

doi:10.1088/1757-899x/469/1/012074

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…(27) The selection of a stylus profilometer to measure the surface roughness was due to the ability of the contact profilometer to detect surface roughness over large area which can give an overview to the entire surface. (28) Dental materials' surface roughness significantly impacted the buildup of plaque, staining, wear and esthetical appearance of direct and indirect restorations. Several studies revealed that the threshold of surface roughness for bacterial plaque retention was 0.2μm (200nm).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of color change and surface roughness for two indirect esthetic materials submitted to different immersion protocol.

Mohamed,

Wazz,

Gamal

2024

Egyptian Dental Journal

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Aim: This study aimed to evaluate the color stability and surface roughness of hybrid ceramic and resin composite blocks materials after different immersion protocol.Methods: Forty samples were prepared and divided into 2 groups according to the type of material. Group A hybrid ceramic (Cerasmart) and Group B composite resin blocks (Brilliant Crios). Samples of each group were subdivided into 4subgroups (n=5) according to immersion protocol. subgroup I artificial saliva, subgroup II artificial saliva and citric acid, subgroup III artificial saliva followed by citric acid and tea without sugar and subgroup Ⅳ artificial saliva followed by citric acid and tea with sugar. All subgroups were submitted to the immersion protocol for 28 days. Then samples were tested for color stability and surface roughness measurements before and after immersion by spectrophotometer and stylus profilometer respectively.Results: For color stability and surface roughness, all subgroups for both groups showed statistically significant mean values comparing before. And after immersion the ∆E and Ra values for group B in all subgroups were significantly higher than group A. The ΔE and Ra values of subgroup III in both groups had the highest mean values. Conclusion:Immersion protocol associated with tea without sugar showed higher stainability and affect surface roughness more than tea with sugar. Moreover, both materials exceed the clinical acceptance level (∆E=3.7) of color change.

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Section: Discussionmentioning

confidence: 99%

Evaluation of color change and surface roughness for two indirect esthetic materials submitted to different immersion protocol.

Mohamed,

Wazz,

Gamal

2024

Egyptian Dental Journal

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“…The stylus is a sharp tip of a diamond having a very small radius through which it touches traces the surface. Although this method has advantages like the mechanical method is easy to use and creates reliable measurements of the surface, but in the meanwhile the tip can also scratch the surface, causing damage to it [75]. According to a study, contact-based methods usually involve the use of atomic microscopes and stylus profilers in which the stress is applied to the surface, causing damage to the surface [76].…”

Section: Comparison Of Techniques Of Surface Measurementmentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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“…Meanwhile, the light sensor is also proposed as a portable device to measure the surface roughness of the workpiece. It consists of an optical system that is used to measure the light intensity reflected from the surface of the machined component (67). In the advanced technology, the laser triangulation method is used to measure the surface roughness of the machined component, where the laser source implements line structured light.…”

Section: Ecs Transactions 107 (1) 1327-1354 (2022)mentioning

confidence: 99%

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

Anand¹,

Sivakumar²,

Prabadevi³

2022

ECS Trans.

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Recently, the development of the grinding process is improved by including automatic sensors, actuators, control systems, artificial intelligence, and industrial internet of things in the grinding machine. Already existing contact type techniques are replaced with the automatic sensor system to incorporate artificial intelligence in the normal grinding process. Therefore, optical and laser technology is emerging as a smart device to observe the surface topography of the grinding wheel, surface finish of the ground surface, and automatic dressing process. Moreover, artificial intelligence consists of machine learning, which teaches the grinding machine based on the existing data available to improve the quality and the production rate of the grinding process. This can be achieved by controlling the process parameter, monitoring the machine health, and attaining optimum condition. The present review paper addresses the existing problems associated with the contact type and non-contact type measurement. The study also emphasizes the importance of machine learning algorithms to predict the failure of the grinding wheel and surface roughness of workpiece material in the grinding process.

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Portable non-contact surface roughness measuring device

Cited by 5 publications

References 19 publications

Evaluation of color change and surface roughness for two indirect esthetic materials submitted to different immersion protocol.

Evaluation of color change and surface roughness for two indirect esthetic materials submitted to different immersion protocol.

Surface Characteristics Measurement Using Computer Vision: A Review

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

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