On-machine and in-process surface metrology for precision manufacturing

Gao, Wei; Haitjema, Han; Fang, Fengzhou; Leach, Richard; Cheung, Chi Fai; Savio, Enrico; Linares, Jean‐Marc

doi:10.1016/j.cirp.2019.05.005

Cited by 300 publications

(140 citation statements)

References 222 publications

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“…Stylus instrument, also known as surface profilometer, is used as a contact‐type surface roughness measurement of the machined component. It consists of a diamond stylus probe that is moved perpendicularly to the direction of roughness, and a characteristic of surface roughness is recorded at the other ends . It is most widely used technique because of its advantages and generating a profile of an object along a well‐defined direction .…”

Section: Methodsmentioning

confidence: 99%

“…[10][11][12] Contact-type measurements are currently used due to the compact design, high measurement accuracy, and the ability to deliver consistent output for surface inspection. 13 However, the study did not provide universal testimony for the significance of these advantages for the accuracy of measurements. 14 Extensive research is carried out on non-contact-type assessment of surface roughness parameters using machine vision system and artificial intelligence technology, which include methods such as laser speckle, light scattering, and optical interference.…”

mentioning

confidence: 89%

“…13 It is most widely used technique because of its advantages and generating a profile of an object along a well-defined direction. 13 Surface roughness measurement of 12 CNC turned components has been carried out on contact-type stylus instrument called surface profilometer as shown in Figure. 1. The measuring conditions for measurement are given in Table 2.…”

Section: Measurement Of Surface Roughness Parameters Using Surface Prmentioning

confidence: 99%

“…Surface measurement is primarily divided into two categories: (a) contact measurement and (b) noncontact measurement . Contact‐type measurements are currently used due to the compact design, high measurement accuracy, and the ability to deliver consistent output for surface inspection . However, the study did not provide universal testimony for the significance of these advantages for the accuracy of measurements .…”

Section: Introductionmentioning

confidence: 99%

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Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 89%

Section: Measurement Of Surface Roughness Parameters Using Surface Prmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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“…As a result, the surface error “copied” from the motion error is not detectable through the probe with the same motion error. Therefore, calibration [ 195,196 ] or separation [ 197,198 ] of the motion error is indispensable. The on‐machine measurement is thus significantly different from the off‐machine measurement as shown in Figure a.…”

Section: Single‐point‐probe‐based Profilometrymentioning

confidence: 99%

Measurement of Freeform Optical Surfaces: Trade‐Off between Accuracy and Dynamic Range

Chen

Xue

Zhai

et al. 2020

Laser & Photonics Reviews

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Freeform optical surfaces are advantageous to optical designers, as they provide additional degrees of freedom for optimization. The loss of rotational symmetry, however, makes measurement of freeforms more challenging. Herein, advances in interferometric areal measurement of freeform optical surfaces, including null tests, non-null tests, near-null tests, and adaptive null tests, are reviewed. Some new developments, in the Hartmann test, deflectometry, and phase retrieval, as representatives of non-interferometric areal measurement, are then presented. Overall, the focus is on single-point-probe-based profilometry categorized into coordinate measurements and slope-or curvature-based measurements. Innovative measurement technology is trying to bridge the gap between high accuracy and high dynamic range as freeform optical surfaces are finding more applications in visible and shorter-wavelength optics.

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A Self‐Decoupled and High‐Resolution 2D Displacement Sensor Based on Triboelectric Nanogenerator for Manipulator Precision Positioning

He,

Feng,

et al. 2023

Adv Materials Technologies

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Abstract2D displacement sensor (2DDS) is significant in precision manufacturing positioning, machine condition monitoring, robotic manipulation, and human–computer interaction system. However, the reported 2DDSs have severe limitations, such as low accuracy, poor decoupling, and lack of smart signal analysis. Herein, a self‐decoupled and high‐resolution 2DDS based on two freestanding triboelectric nanogenerators (F‐TENGs) is presented. This sensor achieves self‐decoupling by two orthogonal and independent 1D displacement sensors with low‐cost grating‐structured PCB. The test results of the sensor exhibit a high accuracy ≥99.06% (1D) and 98.94% (2D). Also, the experimental and simulation results demonstrate that this sensor has a high resolution of 200 µm and the function of initial position identification. A 2D displacement sensing system is developed that is capable of collecting F‐TENGs’ open‐circuit voltages in real‐time and performing 2D trajectory synthesis and display. This work expands the potential application of the TENG‐based 2DDS in robotic precision machining.

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On-machine and in-process surface metrology for precision manufacturing

Cited by 300 publications

References 222 publications

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Measurement of Freeform Optical Surfaces: Trade‐Off between Accuracy and Dynamic Range

A Self‐Decoupled and High‐Resolution 2D Displacement Sensor Based on Triboelectric Nanogenerator for Manipulator Precision Positioning

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