Optimization of surface roughness in end milling Castamide

Bozdemir, Mustafa; Aykut, Şeref

doi:10.1007/s00170-011-3840-2

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…Number of neurons is also another effective parameter in finding best correlation. Having more neurons can increase efficacy of the model up to certain values as can be seen in literature [ 16 , 17 ]. Even though Bozdemir has selected one layer, he could manage to get correlation as much as ours by means of using 13 neurons while we have only 2-3 neurons in each layer.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials

Bozdemir

2018

Computational Intelligence and Neuroscience

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To know the impact of processing parameters of PA6G under different humidity conditions is important as it is vulnerable to humidity up to 7 %. This study investigated the effect of cutting parameters to surface roughness quality in wet and dry conditions. Artificial Neural Network (ANN) modeling is also developed with the obtained results from the experiments. Humidity condition, tool type, cutting speed, cutting rate, and depth of cutting parameters were used as input and average surface roughness value were used as output of the ANN model. Testing results showed that ANN can be used for prediction of average surface roughness.

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Section: Resultsmentioning

confidence: 99%

“…The experimental results were used to train and test. 100 experimental results were used, from the total of 120, as data sets to train the network, while 20 results were used as test data [ 16 ]. Bozdemir and Aykut used more data in another study and studied LM, SCG, and CGP algorithms and found LM algorithm to be the best [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials

Bozdemir

2018

Computational Intelligence and Neuroscience

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“…Artificial neural network modeling technique was advanced with the results acquired from the experiments. In this model, cutting depth, cutting speed and material type parameters are used [11]. As known from the literature, additively manufactured pieces almost require post-processing to improve surface quality features and relieve residual stresses.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Hole Quality of Polyamide Materials Produced by Traditional and Additive Manufacturing

Tezel¹

2020

International Journal of Advances in Engineering and Pure Sciences

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Polyamide, commonly used in engineering plastics, has wear resistance and high strength. In this study, polyamide, which is produced with casting and three-dimensional printer, has been drilled and reamed in numerical controlled machine. Thus, the effect of production technique, machining allowance and tool feed rate on hole surface and dimensional accuracy was investigated. In general, the surface quality of the holes in the polyamide material produced by the two production techniques was directly affected by the machining allowance and the increased feed rate. In addition, the size of the holes created with the three-dimensional printer has reached the desired level after reaming process.

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“…Bozdemir et al [7] developed an artificial neural network (ANN) modeling technique with the results obtained from the experiments, in this way, average surface roughness could be estimated without performing actual application for those values. Elhami et al [8] arranged an experimental scheme by using Taguchi method and proposed a genetically optimized neural network system (GONNS) to predict the constrained optimal cutting conditions in face milling of high-silicon austenitic stainless steel, found that the feed is the dominant factor affecting the surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration

Zhang

Jian

Cao

et al. 2014

Int J Adv Manuf Technol

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Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In order to avoid the costly trial-and-error process in machining parameters determination, the Gaussian process regression (GPR) was proposed for modeling and predicting the surface roughness in end face milling. Cutting experiments on C45E4 steel were conducted and the results were used for training and verifying the GPR model. Three parameters, spindle speed, feed rate, and depth of cut were considered; the experiment results showed that depth of cut is the main factor affecting the surface roughness and regression results showed that the GPR model has a good precision in predicting the surface roughness in different cutting conditions. The prediction accuracy was nearly about 84.3 %. Based on the GPR prediction model, 3D-maps of surface roughness under various cutting parameters could be obtained. It is very concise and useful to select the appropriate cutting parameters according to the maps. As experimental results did not conform to the empirical knowledge, frequency spectrums of the tool were analyzed according to the 3D-maps, it was found that tool vibration is also a crucial factor affecting the machined surface quality.

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Optimization of surface roughness in end milling Castamide

Cited by 7 publications

References 26 publications

Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials

Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials

Comparison of Hole Quality of Polyamide Materials Produced by Traditional and Additive Manufacturing

Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration

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