Non-contact method to assess the surface roughness of metal castings by 3D laser scanning

Vijayarangan, Jaipravin

doi:10.31274/etd-180810-5928

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…In [242], a 3D laser scanning is demonstrated to test the surface roughness of casting surfaces using the PCA flat fitted to point cloud data using a built R software. The findings from this report are as follows:…”

Section: Surface Characteristics Measurement Using Laser Speckle Tech...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 Laser Speckle Tech...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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“…For the contactless format, the amount of data collected cannot be controlled. The amount of data will depend on the number of passes the laser line does on the part and the environmental conditions like external light while scanning [6]. When collecting contact data, we utilized two machines.…”

Section: Robotics and Automation Engineering Journalmentioning

confidence: 99%

A Comparison of CMM and CMA Capabilities in Product Feature Verification Metrology

Raman

2023

RAEJ

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To control variations and guarantee that a part will function as intended in an assembly, measurement verification must be conducted with specialized metrology equipment. The Coordinate Measuring Machine (CMM) and the Articulated Arm CMM (AA-CMM) are analyzed in this comparative study, to identify their respective advantages and disadvantages when measuring flatness. The goal of this pilot study is to detect the measuring limitations of each machine and propose a methodology to identify appropriate use of each device based on its capabilities. For measuring flatness, contact and contactless measurement experiments were designed. As expected, it is observed that the CMM performs better than the CMA in flatness measurement because it has a higher resolution [1]. By looking at the measured flatness obtained during the experiment, it was determined that the CMM had a smaller minimum measurable tolerance in all permutations of the test when compared to its counterpart. Resulting in the proposal of different functional working envelopes for these machines. Establishing working envelopes permits the use of both machines for overlapping working purposes as the versatility of CMAs in Geometry verification in settings where CMMs cannot be deployed, could have a great impact as long as they operate on the corresponding working envelopes defined in this research, which were defined as follows: CMM has a minimum measurable tolerance of 0.0006in, CMA contact, has a minimum measurable tolerance of 0.0024in and CMA contactless, has a minimum measurable tolerance of 0.0045in.

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“…It uses a high-resolution camera to capture the reflection of line laser from the pavement surface to generate 2D/3D texture images based on the laser triangulation principle. More information of 3D laser imaging technology can be found in previous studies [ 59 , 60 ]. The Safety Sensor is 406.4 mm (16 inches) higher than the pavement surface and continuously scans a 469.9 mm (18.5 inches) wide area via 4096 pixels in a non-contact manner along left pavement wheel path.…”

Section: Field Sites and Data Collectionmentioning

confidence: 99%

A Novel 0.1 mm 3D Laser Imaging Technology for Pavement Safety Measurement

Yang

Wang

et al. 2022

Sensors

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Traditionally, pavement safety performance in terms of texture, friction, and hydroplaning speed are measured separately via different devices with various limitations. This study explores the feasibility of using a novel 0.1 mm 3D Safety Sensor for pavement safety evaluation in a non-contact and continuous manner with a single hardware sensor. The 0.1 mm 3D images were collected for pavement safety measurement from 12 asphalt concrete (AC) and Portland cement concrete (PCC) field sites with various texture characteristics. The results indicate that the Safety Sensor was able to measure pavement texture data as traditional devices do with better repeatability. Moreover, pavement friction numbers can be estimated using 0.1 mm 3D data via the proposed 3D texture parameters with good accuracy using an artificial neural network, especially for asphalt pavement. Lastly, a case study of pavement hydroplaning speed prediction was performed using the Safety Sensor. The results demonstrate the potential of using ultra high-resolution 3D imaging to measure pavement safety, including texture, friction, and hydroplaning, in a non-contact, continuous, and accurate manner.

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Non-contact method to assess the surface roughness of metal castings by 3D laser scanning

Cited by 4 publications

References 11 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

A Comparison of CMM and CMA Capabilities in Product Feature Verification Metrology

A Novel 0.1 mm 3D Laser Imaging Technology for Pavement Safety Measurement

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