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

Patel, Dhiren; Kiran, M.B.; Vakharia, Vinay

doi:10.1002/eng2.12119

Cited by 24 publications

(11 citation statements)

References 34 publications

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“…Patel et al [174] explicitly showed that a vision strategy for the machine can be applied to test the ma-chined surface roughness. The fitness of experimental measurements is measured by multiple regression analyses.…”

Section: Surface Characteristics Measurement Using Machine Vision Tec...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 Machine Vision Tec...mentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

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“…In this stylus, a small conical diamond tip is touched and across the along surface of the specimen, while its deflection is recorded, the reading appears on the device's screen which represents the roughness value of the specimen in the micrometer unit (Ra in μm). [13]. Three randomly selected locations on the specimen surface was operated by a stylus to record the desired measurement of roughness values [14].…”

Section: Surface Roughness Testingmentioning

confidence: 99%

Influence of Different Wax Hardening Agents on Surface Roughness of Refractory Cast Investment Materials

Yassen

Hummudi

2021

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Background: Phosphate-bonded investments produce a refractory cast model with rough surface and undesirable properties. Dental Surface hardening agents are then applied to refractory investment materials to enhance its properties. Purpose: compare and evaluate the effects of different dental cast hardening agents (Bees wax, Carnauba wax, and Ceresin wax) on surface roughness of refractory model investment material. Approach: 30 specimens were constructed from commercially available phosphate bonded investment material. Specimens were subjected to a roughness test before dipping in handing agents, then divided into three groups according to the type of wax in which they were dipped. (10 specimens for each type). Surface roughness test was done by a profilometer where three readings were taken for all the specimens' surfaces randomly. The mean was then extracted for the roughness values before and after dipping in waxes. Results: the mean and standard deviation were tabulated and statistically analyzed using students T- test. The roughness values were decreased after the specimens were dipped in waxes (P value < 0.001 HS highly significant). Conclusion: The dipping process with hardening agents has significantly improved the surface roughness of the specimens' investment materials.

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“…A machine vision system is offered, which captures images and extracts surface texture features of machined detail's surface [10]. The texture parameters are extracted, using the gray-level co-occurrence spacial matrix and correlated with different surface roughness parameters recorded by a contact-type profilometer.…”

Section: Problem's Statementmentioning

confidence: 99%

Measurement Errors of the Shape’s Parameters of Detail’s Surface by Optical Instruments

Skytsiouk

Klotchko

2020

BNTUU"KPI".SIM

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Introduction. The main problem of all product condition monitoring systems is the untimely determination of the moment of their contact and the determination of measurement errors. Now problems of surface quality control, measurements of current geometric parameters must be solved at the stage of production preparation when choosing the metrological support of the technological process. Therefore, we can identify the main disadvantages that relate to the currently known methods and devices for determining the parameters of forming the detail, in particular in the presence of a complex shape and internal surfaces, such as holes. First, it is clear that a contact measuring tool can degrade of the precision detail's surface. Secondly, studies have shown that all the described methods and devices work only to control and measure the parameters of the outer detail's surfaces.Main part. Therefore, it is an interesting task to control the quality of internal detail's surfaces, for example, holes. In this case, the task becomes more complicated, due to the instrumental features of registration of the current parameters of detail's shaping.Therefore, it is necessary to consider a model for determining the error's distribution at process of control details. If we have the presence of waviness, surface roughness, or other deviations of submicrogeometry, this leads to the phenomenon of scattering of incident radiation and ultimately affects the actual reflectivity.These dependences need to be considered at creation of devices of control and measurements of parameters of forming of a detail's surface. So, for example, thus, it is possible to offer the device of the control of a condition of internal surfaces of a detail which contains the scheme of a fiber-optic three-channel meter.In addition, if you use radiation sources with different wavelengths, you can get different values of intensities, taking into account the radiation parameters recorded by the photodetector modules, and at the same time get greater accuracy by comparing these values and determining the measurement error.Conclusions. Thus, taking into account the proposed method for determining the surface roughness of the part, it is possible to determine the roughness by the intensity of the light flux reflected from the surface. The surface absorption by mass of the technological object is taken into account, then the degree of height of the rough surface introduces a coefficient that determines the roughness parameters. This takes into account the time required for processing, i.e., which takes into account the dynamics of both manufacturing and changes occurring on the surface of a technological object, which is controlled.

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

Cited by 24 publications

References 34 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

Influence of Different Wax Hardening Agents on Surface Roughness of Refractory Cast Investment Materials

Measurement Errors of the Shape’s Parameters of Detail’s Surface by Optical Instruments

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