Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant

In this paper, we present machining tests by turning En-31 steel alloy with tungsten carbide inserts using soluble oil-water mixture lubricant under different machining conditions. First-order and second-order cutting force prediction models were developed by using the experimental data by applying...

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Main Authors: L B Abhang, M Hameedullah
Format: Article
Language:English
Published: Growing Science 2011-04-01
Series:Management Science Letters
Subjects:
Online Access:http://www.growingscience.com/msl/Vol1/msl_2010_16.pdf
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spelling doaj-d7709d4590294d5ab353b477765334a72020-11-24T20:57:10ZengGrowing ScienceManagement Science Letters1923-93351923-93432011-04-0112167180Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant L B AbhangM HameedullahIn this paper, we present machining tests by turning En-31 steel alloy with tungsten carbide inserts using soluble oil-water mixture lubricant under different machining conditions. First-order and second-order cutting force prediction models were developed by using the experimental data by applying response surface methodology combined with factorial design of experiments. Analysis of variance (ANOVA) is also employed to check the adequacy of the developed models. The established equations show that feed rate and depth of cut are the main influencing factors on the cutting force followed by tool nose radius and cutting velocity. It increases with increase in the feed rate, depth of cut and tool nose radius but decreases with an increase in the cutting velocity. The predicted cutting force values of the samples have been found to lie close to that of the experimentally observed values with 95% confident levels. Moreover, the surface response counters have been generated from the model equations. Desirability function is used for single response optimization.http://www.growingscience.com/msl/Vol1/msl_2010_16.pdfCutting forceResponse surface methodologyMetal cuttingFactorial designStatistical modeling
collection DOAJ
language English
format Article
sources DOAJ
author L B Abhang
M Hameedullah
spellingShingle L B Abhang
M Hameedullah
Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
Management Science Letters
Cutting force
Response surface methodology
Metal cutting
Factorial design
Statistical modeling
author_facet L B Abhang
M Hameedullah
author_sort L B Abhang
title Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
title_short Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
title_full Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
title_fullStr Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
title_full_unstemmed Statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
title_sort statistical modeling of main cutting force produced by wet turning using soluble oil-water mixture lubricant
publisher Growing Science
series Management Science Letters
issn 1923-9335
1923-9343
publishDate 2011-04-01
description In this paper, we present machining tests by turning En-31 steel alloy with tungsten carbide inserts using soluble oil-water mixture lubricant under different machining conditions. First-order and second-order cutting force prediction models were developed by using the experimental data by applying response surface methodology combined with factorial design of experiments. Analysis of variance (ANOVA) is also employed to check the adequacy of the developed models. The established equations show that feed rate and depth of cut are the main influencing factors on the cutting force followed by tool nose radius and cutting velocity. It increases with increase in the feed rate, depth of cut and tool nose radius but decreases with an increase in the cutting velocity. The predicted cutting force values of the samples have been found to lie close to that of the experimentally observed values with 95% confident levels. Moreover, the surface response counters have been generated from the model equations. Desirability function is used for single response optimization.
topic Cutting force
Response surface methodology
Metal cutting
Factorial design
Statistical modeling
url http://www.growingscience.com/msl/Vol1/msl_2010_16.pdf
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