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Surface roughness prediction of particulate composites using artificial neural networks in turning operation
2015
Decision Science Letters
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In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. ...
The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. ...
In this article, ANN model is used to predict the surface roughness in Particulate Reinforced Aluminum Matrix Composites (PAMCs) turning. ...
doi:10.5267/j.dsl.2015.3.001
fatcat:4gjpdhg5ofb2balz4bg5l7gafq
Using Multiple Linear Regression and Artificial Neural Network to Predict Surface Roughness in Turning Operations
2018
Figshare
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Two mathematical models are developed to predict the surface roughness and to select the required surface roughness by using the Multi-regression model and Artificial Neural Networks (ANN). ...
In this paper, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed rate, and depth of cut. ...
Conclusion In this study, multiple regression and artificial neural network approaches were used to predict the surface roughness of alloy steel HRC15. ...
doi:10.6084/m9.figshare.6364823.v1
fatcat:ic2uiybdlrak3gx5mpg6hzouvq
Tool Wear And Surface Roughness Prediction Using An Artificial Neural Network (Ann) In Turning Steel Under Minimum Quantity Lubrication (Mql)
2010
Zenodo
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This paper deals with developing an artificial neural network (ANN) model as a function of cutting parameters in turning steel under minimum quantity lubrication (MQL). ...
The results imply that the model can be used easily to forecast tool wear and surface roughness in response to cutting parameters. ...
ACKNOWLEDGMENT This research work has been conducted in the department of Industrial and production Engineering (IPE) in Bangladesh University of Engineering and Technology (BUET). ...
doi:10.5281/zenodo.1332488
fatcat:trgl6zxianf7xbqbod47uxkefy
Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
2011
Expert systems with applications
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Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. ...
Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. ...
Surface roughness prediction strategy using artificial neural network Artificial neural networks (ANNs) emulating the biological connections between neurons are known as soft computing techniques. ...
doi:10.1016/j.eswa.2010.11.041
fatcat:ctwnf2pc2nfdzgigcil2c72ofi
Surface Roughness Prediction using Artificial Neural Network in Hard Turning of AISI H13 Steel with Minimal Cutting Fluid Application
2014
Procedia Engineering
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In this paper an attempt was made to develop a model based on Artificial Neural Network to simulate hard turning of AISI H13 steel with minimal cutting fluid application. ...
This model is expected to predict the surface roughness in terms of cutting parameters. ...
Nalbant et al. developed a model based on Artificial Neural Network to predict surface roughness in CNC turning of AISI 1030 steel [11] . ...
doi:10.1016/j.proeng.2014.12.243
fatcat:zgsn4bpkbbefxbunmk2lpgp2tm
Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network
2017
International Journal of Industrial and Systems Engineering
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Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network. ...
For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. ...
In turning process: Pontes et al. (2012) proposed an RBF neural network method to predict R a using v, f and d as input variables of SAE 52100 hardened steel. ...
doi:10.1504/ijise.2017.085227
fatcat:tprun3qvfjfadevnczoncljsna
International journal of scientific processes research and application(IJSPRA) EFFECT OF HARDNESS ON SURFACE ROUGHNESS PREDICTION IN TURNING OPERATION USING ARTIFICIAL NEURAL NETWORK
www.ijspra.com
unpublished
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The artificial neural network model is developed for surface roughness prediction to investigate the effect of these three parameters. ...
In present study, the effect of work piece hardness, cutting tool nose radius and cutting speed on surface roughness in hard turning are experimentally investigated. ...
The present work conducted to help us to predict the Surface Roughness using the Artificial Neural Network in turning process. ...
fatcat:veatjrn7sneu3hskly7wkuzhlq
Comparison of two methods for predicting surface roughness in turning stainless steel AISI 316L
2018
Ingeniare : Revista Chilena de Ingeniería
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Keywords: AISI 316L stainless steel, analysis of regression, artificial neural network, surface roughness, dry turning. ...
The present study aimed to explore various models to predict the surface roughness in dry turning of AISI 316L stainless steel. ...
Strategy prediction using artificial neural networks Artificial neural networks (ANN) are widely used in many industry applications. ...
doi:10.4067/s0718-33052018000100097
fatcat:ivlpai7ievg3hj2vvdblmw7roe
Neural Image Processing of the Wear of Cutting Tools Coated with Thin Films
2006
Journal of materials engineering and performance (Print)
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In this work an artificial intelligent (AI) technique viz. artificial neural network (ANN) is applied for predicting output responses such as wear occurring at the flank face of the cutting insert and ...
the roughness of the machined workpiece's surface during the hard turning process. ...
The experimental investigation of the effects of uncoated, PVD-and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural ...
doi:10.1361/105994906x95922
fatcat:76gc24bucfh7dnwkiuxsv7hnkm
Study of the Influence of Process Parameters on Surface Roughness When Inconel 718 Is Dry Turned Using CBN Cutting Tool by Artificial Neural Network Approach
2014
International Journal of Materials Mechanics and Manufacturing
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Index Terms-Surface roughness, dry turning, Inconel 718, CBN cutting tool, artificial neural network. ...
The experiments are carried out using L9 orthogonal array. Artificial Neural Networks is used to validate the experimental results. ...
[1] utilised regression techniques and neural networks for predicting surface roughness and tool wear in hard turning. ...
doi:10.7763/ijmmm.2014.v2.152
fatcat:vz2kojwecbglncpm5y6xcn5zdu
PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK
2013
JES. Journal of Engineering Sciences
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Abouelatta, Prediction of machining operations and surface roughness using artificial neural network ...
The training data set has been used to train different artificial neural network (ANN) models in order to predict machining processes and surface roughness parameter values through back propagation network ...
using an in-process neural network-based surface roughness prediction system. ...
doi:10.21608/jesaun.2013.114779
fatcat:2n5tdjjwr5hq5ata77owhsltqu
Studies on Effect of Cutting Parameters on Surface Roughness of Al-Cu-TiC MMCs: An Artificial Neural Network Approach
2015
Procedia Computer Science
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To have all the data in a same scale the experimental results have been normalized before being used in the Artificial Neural Network model. ...
An artificial neural network model of 'Feed Forward Back Propagation' type is developed for the analysis and prediction of surface roughness, the relationship between cutting and process parameters of ...
There exist non-linear relationship between the cutting conditions and surface roughness parameters. This justifies the use of artificial neural network to develop the surface roughness model. ...
doi:10.1016/j.procs.2015.03.145
fatcat:7eth7enhprb43n53oveztvsftm
Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method
2017
The International Journal of Advanced Manufacturing Technology
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As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within ...
For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s00170-017-0678-2
fatcat:ltzcw5tplzhl3dp2wbu6l6onha
PREDICTION OF SURFACE ROUGHNESS IN HIGH SPEED MACHINING: A COMPARISON
2014
International Journal of Research in Engineering and Technology
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System (ANFIS) for predicting surface roughness in high speed machining operations. ...
This paper discusses the application of the Response Surface Methodology (RSM) and Artificial Intelligence (AI) based techniques namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference ...
In this study, artificial neural networks have been used to get the predicted values of surface roughness in turning operation. ...
doi:10.15623/ijret.2014.0315098
fatcat:53c77jzmingt5bdpbtlvnvheee
Modeling of Surface Roughness during Conventional Turning using a Hybrid GA-ANN Based Model
2016
IOSR Journal of Mechanical and Civil Engineering
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In the present study a hybrid GA-ANN based model has been developed for the prediction of Surface Roughness obtained in a conventional turning process. ...
Surface roughness is a very important property of a machined part and is hard to predict due to the complex nature of turning process. ...
Artificial Neural Network (ANN) An artificial neural network is a machine learning method developed by Warren McCulloh and Walter Pitts in 1943. ...
doi:10.9790/1684-1305040108
fatcat:fl5rg23xefdwpog63v3l2cddkq
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