Influence of roller burnishing contact width and burnishing orientation on surface quality and tribological behaviour of Aluminium 6061

El-Tayeb, N. S. M.; Low, Kean Ong; Brevern, P. V.

doi:10.1016/j.jmatprotec.2006.12.044

Cited by 95 publications

(63 citation statements)

References 13 publications

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“…El Tayeb et al worked on Influence of roller burnishing contact width and burnishing orientation on surface quality and triblogical behavior of Aluminum 6061 Based on their work it was predicted that the Optimum ranges of burnishing speed and force are identified to be 250-420 rpm for 1mm roller contact width. [22] Burnishing force above 220N is capable of decreasing the surface roughness by 35%. Below this limit, the surface roughness starts to deteriorate plastically; Burnishing with smaller roller contact width (1 mm) is capable of improving the surface roughness up to 40%.…”

Section: Fig 20 True Strain Verses True Stressmentioning

confidence: 99%

Effect of Burnishing Process on Behavior of Engineering Materials- A Review

Ghodake¹

2013

IOSR-JMCE

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Section: Fig 20 True Strain Verses True Stressmentioning

confidence: 99%

Effect of Burnishing Process on Behavior of Engineering Materials- A Review

Ghodake¹

2013

IOSR-JMCE

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“…To make a successful ANN that can be used to predict the friction coefficient, a few steps need to be followed as below: a) Database collection: the friction coefficient data for Roller Burnishing AL6061 experiment El-Tayeb et al [6]. b) Generate the input and output for the data.…”

Section: Developing Of the Artificial Neural Networkmentioning

confidence: 99%

“…The dataset was taken from available experimental data of friction coefficient for roller burnishing AL6061 [6]. The tribo tests were conducted on dry burnishing surface produced by different widths of roller curvature (7.5mm, 8mm and 8.5mm), burnishing speeds (110rpm, 230rpm, 330rpm and 490rpm), and burnishing forces (155.06N, 197.45N, 239.83N and 282.22N).…”

Section: A Preparation Of Datamentioning

confidence: 99%

“…The experimental data taken from El-Tayeb et al [6] work were classified into inputs and outputs. Four parameters (roller curvature, burnishing speed, burnishing force and time) have been determined from the experimental data to be the inputs.…”

Section: A Preparation Of Datamentioning

confidence: 99%

“…The effect of roller burnishing parameters such as burnishing force, speed, feed, and roller width contact is studied by mean of experimental [6]. These experiments computed the hardness, roughness, friction and wear of the specimen.…”

Section: Introduction Heuristic Techniques Like Artificial Intellimentioning

confidence: 99%

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Artificial Neural Network (ANN) Approach for Predicting Friction Coefficient of Roller Burnishing AL6061

Tang¹,

Hakim²,

Khaksar³

et al. 2012

IJMLC

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Abstract-Artificial Neural Network (ANN) approach is a fascinating mathematical tool, which can be used to simulate a wide variety of complex scientific and engineering problems. Due to its highly reliable prediction quality, the usage of it is growing rigorously and had already become an ultimate tool for various applications in the field of engineering. In this study an ANN technique was used to predict friction coefficient of roller burnishing AL6061 for two orientations which is parallel burnishing orientation (PB) and cross burnishing orientation (CB). The input parameters were defined by widths of roller curvature (7.5mm, 8mm and 8.5mm), burnishing speeds (110rpm, 230rpm, 330rpm and 490rpm), and burnishing forces (155.06N, 197.45N, 239.83N and 282.22N) while the output parameter was friction coefficient. 173 data was used for training the ANN and another 115 data was used to test the ANN. 60 different configurations of ANN was trained by using 6 different training algorithms. It was found that feed-forward back-propagation network with 15 neurons in hidden layer that was trained by Levenberg-Marquardt training algorithm gave the best result when compared to other training algorithms used. From the results it was found that the training performance and prediction performance was 0.000809 and 0.710 respectively. From this study, it became obvious that the selected ANN with the configuration and training algorithm proved to be the most suitable among the other ANN investigated for similar applications.Index Terms-Friction coefficient; neural network; roller burnishing AL6061.

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Strength Parameters of Hardening Cylindrical Workpieces by Tapered Roller

Udalov

Васильевых

2019

Lecture Notes in Mechanical Engineering

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Influence of roller burnishing contact width and burnishing orientation on surface quality and tribological behaviour of Aluminium 6061

Cited by 95 publications

References 13 publications

Effect of Burnishing Process on Behavior of Engineering Materials- A Review

Effect of Burnishing Process on Behavior of Engineering Materials- A Review

Artificial Neural Network (ANN) Approach for Predicting Friction Coefficient of Roller Burnishing AL6061

Strength Parameters of Hardening Cylindrical Workpieces by Tapered Roller

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