On the approximating criteria of parabolic asperities for measured surface profiles

Zhu, Linbo; Chen, Jian; Sun, Yanhui

doi:10.1063/5.0046579

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…¥ representing the strength of the connection. Such a graph can be represented by a symmetric adjacency matrix W shown in equation (13). The weighted links between two points are computed as the Euclidean distance between measurable values on these nodes in figure 3(b).…”

Section: Graph Evaluation For Frequency Domainmentioning

confidence: 99%

“…The main frequency domain values of the middle row of the low roughness feature texture are selected as priori knowledge in figure 11. According to the corresponding equation (13), the corresponding graph matrix can be constructed in figure 12(a). After modeling, it is diagonalized and the generated eigenvalues are [−2.2756, −0.3846, −0.1945, −0.1122, −0.0262, −0.0091, −0.0051, −0.0021, 3.0093], and the corresponding eigenvectors are shown in figure 12(b).…”

Section: Similarity Evaluationmentioning

confidence: 99%

“…Generally, the machined surface profile is mainly composed of roughness profile, waviness profile, and shape profile [13]. Among them, the surface roughness is mainly affected by the high-frequency component of the relative vibration in the process system.…”

Section: Introductionmentioning

confidence: 99%

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Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

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Section: Graph Evaluation For Frequency Domainmentioning

confidence: 99%

Section: Similarity Evaluationmentioning

confidence: 99%

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Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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show abstract

“…The rough asperities can be modelled as the set of the elasto-plastically deformed hemispheres [154] to [157], hemi-ellipsoids [157] and [158] or paraboloids [159] and [160] with various distributions.…”

Section: The Effect Of Lubrication On the Contact Pressure In Contact...mentioning

confidence: 99%

The Measurement of the Wear of Tie Rod End Components

Woźniak¹,

Rylski²,

Siczek³

2022

sv-jme

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This study aimed to determine the wear intensity of the bearing mating with the ball stud in the tie rod end. The course of the vehicle driving during the period between repairs of the steering assembly was assumed. The modelled scheme of cornering was assumed to be close to the real one. The model of steering mechanism allowing determination of the load of the stud of the tie rod end as a function of the steering angle was elaborated and presented. The model of the ball stud was realized and described. The values of wear intensity for components were calculated and presented.

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Evaluating surface roughness of curved surface with circular profile based on arithmetic circular arc fitting

Li,

Xu,

Shen

et al. 2023

AIP Advances

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A procedure based on Kåsa fit for evaluating the surface roughness of a curved surface with a circular arc profile was suggested in this paper. To investigate the effectiveness of the method, some curved surface profiles with preset surface roughness were numerically generated, and their surface roughness was evaluated by the procedure based on Kåsa fit. And the surface roughness of two experimental surface profiles evaluated by the method based on Kåsa fit was compared with those evaluated by linear fitting and quadratic polynomial fitting to demonstrate both the effectiveness and the preservation of Kåsa fit-based procedure. Results show that the evaluated value of surface roughness by Kåsa fit-based procedure only deviated from the actual value by less than 10%, even in the case of considerable fitting parameters’ error (>30%). Also, the Kåsa fit can report a reasonable value of surface roughness with better preservation of shift and rotation motion of the surface profile compared to both linear fit and quadratic polynomial fitting, especially for the curved surface with a short radius. This procedure based on Kåsa fit can be used in evaluating surface roughness of curved surface with circular arc profile no matter what pose of the object under observed.

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On the approximating criteria of parabolic asperities for measured surface profiles

Cited by 3 publications

References 33 publications

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

The Measurement of the Wear of Tie Rod End Components

Evaluating surface roughness of curved surface with circular profile based on arithmetic circular arc fitting

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