Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel

Quiza, Ramón; Figueira, Luis; Davim, J. Paulo

doi:10.1007/s00170-007-0999-7

Cited by 128 publications

(44 citation statements)

References 17 publications

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“…The adequacy of model has been checked using correlation coefficients. Quiza et al (2008) performed experiment on hard machining of D2 steel (60 HRC) using ceramic cutting tools. Neural network model was found to be better predictions of tool wear than regression model.…”

Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

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The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R 2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

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Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…The process modeling by response surface methodology (RSM) using statistical design of experiments is proved to be an efficient modeling tool [33,34]. The methodology not only reduces the cost and time but also gives the required information about the main and interaction effects [35].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Machinability During Hard Turning of Cold Work Tool Steel (Type: AISI D2)

Gaitonde

Karnik

Figueira

et al. 2009

Materials and Manufacturing Processes

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109

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Hard turning is an attractive replacement for grinding operations due to numerous advantages such as low capital investment, shorter setup time, higher material removal rate, better surface integrity, and elimination of cutting fluids. As a potential alternative process, there is a need to assess the machinability in high-precision and high-hardened components. The current study establishes the relationships between the cutting conditions (cutting speed, feed rate, and machining time) on machinability aspects (machining force, power, specific cutting force, surface roughness, and tool wear). The response surface methodology-based mathematical models are proposed for modeling and analyzing the effects of process parameters on machinability during turning of high chromium AISI D2 cold work tool steel using CC650WG wiper ceramic inserts. The experiments have been planned as per full factorial design. From the parametric analysis, it is revealed that the power increases with increase in feed rate, while the specific cutting force decreases, whereas the requirement of machining force is low at low values of feed rate and machining time. The response surface analysis also indicates that the surface roughness can be reduced at lower values of feed rate and machining time with higher values of cutting speed, while the maximum tool wear occurs at a cutting speed of 150 m/min for all values of feed rate.

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“…One rare example is the work of Quiza, Figueira and Davim (2008), where an experimental design is employed to configure a neural network of MLP architecture intended to predict tool flank wear in hard machining of D2 AISI steel. The following factors are employed in the experimental design: learning rate, moment constant, training epochs and number of neurons in hidden layer.…”

Section: Network Topology Definitionmentioning

confidence: 99%

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

Pontes¹,

Silva²,

Ferreira³

et al. 2010

J. Braz. Soc. Mech. Sci. & Eng.

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Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel

Cited by 128 publications

References 17 publications

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Analysis of Machinability During Hard Turning of Cold Work Tool Steel (Type: AISI D2)

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

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