Observation of ground surface roughness values obtained by stylus profilometer and white light interferometer for common metal materials

Lu, Enhui; Zhang, Ruting; Liu, Jian; Yin, Shu; Ohmori, Hitoshi

doi:10.1002/sia.7068

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Its DOI: 10.1002/adts.202301100 shortcomings include a small measurement range, easy scratching of the workpiece surface, and low efficiency. [3,4] In noncontact measurement methods, measurement methods based on machine vision are receiving more and more attention. Machine vision measurement is a measurement method based on the principle of optical imaging, which collects images through industrial cameras, then extracts feature indices associated with surface roughness parameters from the images, and further predicts the corresponding unknown surface roughness by known image feature index values.…”

Section: Introductionmentioning

confidence: 99%

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Yi,

Feng,

Shu

2024

Advcd Theory and Sims

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The surface‐roughness machine vision measurement method requires training numerous samples with uniform roughness values to construct a prediction model. Insufficient actual sample size will make the model lack generalization ability. So, a grinding surface‐roughness measurement method combined with simulation data and transfer learning is proposed. First, through surface simulation, inverse fitting, and three‐dimensional (3D) modeling, more realistic simulation samples are obtained, real samples are prepared, and a simulation imaging system and an actual imaging experiment to acquire surface images are designed. Second, the data distribution difference between the simulation domain and the actual domain through transfer component analysis and the use of the adjusted simulation‐domain data and its label to train a support vector regression model is adjusted. Finally, the model predicts the surface roughness of the uncalibrated actual‐domain sample. For shared features of transferable simulation images and actual images, using the high sensitivity of color information, image features are enhanced and a proportion of pure color region index is designed, strongly correlated within the domain but less differentiated between domains. The results show that the transfer learning prediction accuracy based on this index exceeds 90%, verifying the feasibility of the proposed method and providing a new improvement strategy for sample expansion in visual roughness measurement.

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Section: Introductionmentioning

confidence: 99%

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Yi,

Feng,

Shu

2024

Advcd Theory and Sims

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“…Generally, surface roughness measurement includes contact and non-contact detection methods [3]. The contact approach usually adopts a profilometer to measure surface roughness, which can be regarded as a reference standard due to its high accuracy and good traceability [4]. However, it is greatly affected by the vibration, and its measurement efficiency is low.…”

Section: Introductionmentioning

confidence: 99%

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Huang,

Zhang,

Shao

et al. 2023

Meas. Sci. Technol.

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Surface roughness measurement is of great importance for monitoring machining quality. To address the performance degradation of deep models caused by low illumination and ambient noise, this study proposes a non-contact surface roughness detection method based on an adaptive deep fusion capsule network (ADFCNet) model. Firstly, principal competent analysis-based color image enhancement is employed to augment initial surface images collected from normal illumination. Then, consisting of a deep multi-model fusion for high-level feature representation and a capsule classifier for roughness recognition, the ADFCNet model is designed to detect roughness grades by using workpiece surface images. The key hyperparameters of the model are automatically determined by a genetic algorithm. Finally, laboratory and factory experiments under low illumination are carried out to validate the effectiveness and superiority of the proposed method. Experimental results demonstrate that the proposed method has strong low-illumination and noise robustness and generalization capability, indicating a better application prospect in actual scenarios.

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“…However, the method has the disadvantages of high cost, significant environmental impact, inconvenient operation, and low efficiency. These drawbacks have prevented the widespread use of optical methods in the engineering field [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…There are several mainstream research points in the field of machine vision-based roughness measurement method, including electromagnetic wave scattering simulation from rough surface [15,16], roughness-correlated image-based characteristic index design [17], surface texture parameter calibration [2], imaging scheme design [18], prediction model design [19], performance evaluation method and experimental parameter robustness analysis [20].…”

Section: Introductionmentioning

confidence: 99%

A new grinding surface roughness measurement method based on image quality algorithm and BP neural network

Guo²,

Zhu³

et al. 2022

Surf. Topogr.: Metrol. Prop.

Self Cite

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Due to the problem of single factor and no benchmark in the design of image characteristic index, the measurement results of machine vision-based grinding surface roughness measurement are greatly influenced by the light source brightness, and the accuracy is limited. To address this problem, a method based on the combination of a full-reference (FR) image quality algorithm visual saliency-induced index (VSI) and a backward propagation (BP) neural network for grinding surface roughness measurement is proposed. First, the VSI is applied to characterize grinding surface roughness. Its performance is then compared with the mainstream color image-based indices. Meanwhile, the ability of anti-interference to light source is analyzed for the VSI. Furthermore, in order to improve the prediction accuracy, the BP neural network is selected and applied to construct the roughness prediction model. The experimental results show that the VSI has significant advantages in the abilities of measurement accuracy and anti-interference to light source brightness levels. BP can significantly improve the prediction accuracy for VSI.

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Observation of ground surface roughness values obtained by stylus profilometer and white light interferometer for common metal materials

Cited by 6 publications

References 25 publications

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

A new grinding surface roughness measurement method based on image quality algorithm and BP neural network

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