Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning

Elangovan, M; Sakthivel, N. R.; Saravanamurugan, S.; Nair, Binoy B.; Sugumaran, V.

doi:10.1016/j.procs.2015.04.047

Cited by 60 publications

(36 citation statements)

References 7 publications

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“…In conclusion, surface complexity analysis is proving to be central for many applications and different approaches are being applied to the related computer-aid design tools, as Bspline [30], Bezier mathematical techniques [31] and machine learning [32], [33]. The latter, representing a current and growing field of research in a multitude of areas ([34]- [37]) would be certainly promising in the area of surface complexity analysis [38]. In this framework, deep learning techniques, i.e., convolutional neural networks [39]- [41] which solve the central problem of representation learning by building complex features out of simpler features, could benefit of the proposed approach.…”

Section: Discussionmentioning

confidence: 99%

New Method for Estimating Fractal Dimension in 3D Space and Its Application to Complex Surfaces

Babič¹,

Miliaresis

Mikoš

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The concept of "surface modeling" generally describes the process of representing a physical or artificial surface by a geometric model, namely a mathematical expression. Among the existing techniques applied for the characterization of a surface, terrain modeling relates to the representation of the natural surface of the Earth. Cartographic terrain or relief models as threedimensional representations of a part of the Earth's surface convey an immediate and direct impression of a landscape and are much easier to understand than two-dimensional models. This paper addresses a major problem in complex surface modeling and evaluation consisting in the characterization of their topography and comparison among different textures, which can be relevant in different areas of research. A new algorithm is presented that allows calculating the fractal dimension of images of complex surfaces. The method is used to characterize different surfaces and compare their characteristics. The proposed new mathematical method computes the fractal dimension of the 3D space with the average space component of Hurst exponent H, while the estimated fractal dimension is used to evaluate, compare and characterize complex surfaces that are relevant in different areas of research. Various surfaces with both methods were analyzed and the results were compared. The study confirms that with known coordinates of a surface, it is possible to describe its complex structure. The estimated fractal dimension is proved to be an ideal tool for measuring the complexity of the various surfaces considered.

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Section: Discussionmentioning

confidence: 99%

New Method for Estimating Fractal Dimension in 3D Space and Its Application to Complex Surfaces

Babič¹,

Miliaresis

Mikoš

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…It is also common to report the standard deviation and the uncertainty of the measurement, however these techniques have the problem that can only be applied to relatively flat surfaces and that can scratch the surfaces of soft materials. Other alternatives are mechanical techniques as the vibration measurement with accelerometers to estimate the parameters of roughness [23].…”

Section: Techniques For the Measurement Of The Surface Roughnessmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

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“…The availability of, e.g. quality-related data offers potential to improve process and product quality sustainably (Elangovan, Sakthivel, Saravanamurugan, Nair, & Sugumaran, 2015). However, it has been recognized that much information can also propose a challenge and may have a negative impact as it can, e.g.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

709

438

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The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning

Cited by 60 publications

References 7 publications

New Method for Estimating Fractal Dimension in 3D Space and Its Application to Complex Surfaces

New Method for Estimating Fractal Dimension in 3D Space and Its Application to Complex Surfaces

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Machine learning in manufacturing: advantages, challenges, and applications

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