Investigation of Surface Texture Using Image Processing Techniques

Srivani, A.; Xavior, M. Anthony

doi:10.1016/j.proeng.2014.12.348

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…In [170], a vision-based method was tested using the gray-size imaging technique to characterize the surface texture. An image histogram for further analysis has been analyzed to better the image.…”

Section: Surface Characteristics Measurement Using Machine Vision Tec...mentioning

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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Section: Surface Characteristics Measurement Using Machine Vision Tec...mentioning

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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“…Near-infrared reflectography and infrared thermography were also used for NDT and E of artworks in [ 146 , 147 ]. In [ 148 , 149 ], a laser profiler was used to improve the quality of the pipe images obtained by CCTV technology. In order to measure surface quality accurately, a computer vision system was used in [ 150 , 151 , 152 , 153 ] to characterize the nature of a surface texture.…”

Section: Pipeline Inspection Gauge (Pig) and Other Un-piggable Robotic Inspection Systemsmentioning

confidence: 99%

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Qiu

Tian

Zeng

et al. 2021

Sensors

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Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

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“…This third and last case study will be about inspection of wood surface (refer to [36] for data provenance or more details), since the quality of surface influences the components suitability for a specific application, more attention had been given by researchers to measure the surface quality accurately. A reference image is given to compare segmentation quality, after finding the best segmented image and so the optimal operators' and their parameters combination, a measure is taken manually using an interactive Matlab tool.…”

Section: Case Study 3: Investigation Of Surface Texturementioning

confidence: 99%

An ant colony based model to optimize parameters in industrial vision

Benchikhi

Sadgal²,

Elfazziki³

et al. 2017

ELCVIA

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Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.

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Investigation of Surface Texture Using Image Processing Techniques

Cited by 14 publications

References 11 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

Pipeline In-Line Inspection Method, Instrumentation and Data Management

An ant colony based model to optimize parameters in industrial vision

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