The role of stepover ratio in prediction of surface roughness in flat end milling

Topal, Eyüp Sabri

doi:10.1016/j.ijmecsci.2009.09.003

Cited by 32 publications

(9 citation statements)

References 17 publications

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“…In other machining processes: Topal (2009) proposed a backpropagation algorithm discovering the role of stepover besides the standard cutting conditions on R a prediction in flat end milling operation of AISI1040 steel. Al Hazza and Adesta (2013) built up an ANN model to predict best cutting parameters for minimum values of R a in end milling machining of AISI H13.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Benamar

Noureddine

Boukezzi

et al. 2017

IJISE

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Surface roughness is a very important measurement in machining process and a determining factor describing the quality of machined surface. This research aims to analyse the effect of cutting parameters [cutting speed (v), feed rate (f) and depth of cut (d)] on the surface roughness in turning process. For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. The ANN model shows a good correlation between the predicted and the experimental surface roughness values, which indicates its validity and accuracy. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Benamar

Noureddine

Boukezzi

et al. 2017

IJISE

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“…Machining experiments were performed under various cutting conditions by using sample specimens. The surface roughness of these specimens was measured [13,14].…”

Section: Literature Surveymentioning

confidence: 99%

Optimization Technique For Surface Roughness Prediction in Turning Operation

Ojha¹,

Yadav²,

Yadav

2015

SIJSMS

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“…In many manufacturing sectors, end milling operation is most utilizing processes for making the variety of components such as die and mold [8]. Milling of these components is performed by employing different tool paths such as linear and non-linear tool path strategies [9].…”

Section: Introductionmentioning

confidence: 99%

Parametric Optimization of Trochoidal Step on Surface Roughness and Dish Angle in End Milling of AISID3 Steel Using Precise Measurements

Santhakumar

Iqbal

2019

Materials

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Tool steel play a vital role in modern manufacturing industries due to its excellent properties. AISI D3 is a cold work tool steel which possess high strength, more hardenability and good wear resistance properties. It has a wide variety of applications in automobile and tool and die making industries such as blanking and forming tools, high stressed cutting, deep drawing and press tools. The novel ways of machining these steels and finding out the optimum process parameters to yield good output is of practical importance in the field of research. This research work explores an attempt to identify the optimized process parameter combinations in end milling of AISI D3 steel to yield low surface roughness and maximum dish angle using trochoidal milling tool path, which is considered as a novelty in this study. 20 experimental trials based on face centered central composite design (CCD) of response surface methodology (RSM) were executed by varying the input process factors such as cutting speed, feed rate and trochoidal step. Analysis of variance (ANOVA) was adopted to study the significance of selected process parameters and its relative interactions on the performance measures. Desirability-based multiple objective optimization was performed and the mathematical models were developed for prediction purposes. The developed mathematical model was statistically significant with optimum conditions of cutting speed of 41m/min, feed rate of 120 mm/min and trochoidal step of 0.9 mm. It was also found that the deviation between the experimental and predicted values is 6.10% for surface roughness and 1.33% for dish angle, respectively.

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The role of stepover ratio in prediction of surface roughness in flat end milling

Cited by 32 publications

References 17 publications

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Optimization Technique For Surface Roughness Prediction in Turning Operation

Parametric Optimization of Trochoidal Step on Surface Roughness and Dish Angle in End Milling of AISID3 Steel Using Precise Measurements

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