Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction cap...

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Main Authors: Shubrajit Bhaumik, Viorel Paleu, Dhrubajyoti Chowdhury, Adarsh Batham, Udit Sehgal, Basudev Bhattacharya, Chiradeep Ghosh, Shubhabrata Datta
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Materials
Subjects:
Online Access:https://www.mdpi.com/1996-1944/15/23/8445
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author Shubrajit Bhaumik
Viorel Paleu
Dhrubajyoti Chowdhury
Adarsh Batham
Udit Sehgal
Basudev Bhattacharya
Chiradeep Ghosh
Shubhabrata Datta
author_facet Shubrajit Bhaumik
Viorel Paleu
Dhrubajyoti Chowdhury
Adarsh Batham
Udit Sehgal
Basudev Bhattacharya
Chiradeep Ghosh
Shubhabrata Datta
author_sort Shubrajit Bhaumik
collection DOAJ
description The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.
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spelling doaj.art-f2a6c03cf9584c0ca57f937665ce8baf2023-11-24T11:28:22ZengMDPI AGMaterials1996-19442022-11-011523844510.3390/ma15238445Tribological Investigation of Textured Surfaces in Starved Lubrication ConditionsShubrajit Bhaumik0Viorel Paleu1Dhrubajyoti Chowdhury2Adarsh Batham3Udit Sehgal4Basudev Bhattacharya5Chiradeep Ghosh6Shubhabrata Datta7Tribology and Interactive Surface Research Laboratory (TRISUL), Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai 601103, IndiaMechanical Engineering, Mechatronics and Robotics Department, Mechanical Engineering Faculty, “Gheorghe Asachi” Technical University of Iași, 63 D. Mangeron Blvd., 700050 Iași, RomaniaDepartment of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaDepartment of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaDepartment of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaResearch and Development and Scientific Services, Tata Steel Limited, Jamshedpur 831001, IndiaResearch and Development and Scientific Services, Tata Steel Limited, Jamshedpur 831001, IndiaDepartment of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaThe present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.https://www.mdpi.com/1996-1944/15/23/8445materialsfriction reductionmicro-surface texturesvertical milling machineartificial neural networksoptimal design
spellingShingle Shubrajit Bhaumik
Viorel Paleu
Dhrubajyoti Chowdhury
Adarsh Batham
Udit Sehgal
Basudev Bhattacharya
Chiradeep Ghosh
Shubhabrata Datta
Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
Materials
materials
friction reduction
micro-surface textures
vertical milling machine
artificial neural networks
optimal design
title Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
title_full Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
title_fullStr Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
title_full_unstemmed Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
title_short Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
title_sort tribological investigation of textured surfaces in starved lubrication conditions
topic materials
friction reduction
micro-surface textures
vertical milling machine
artificial neural networks
optimal design
url https://www.mdpi.com/1996-1944/15/23/8445
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