Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

Hossain, Mohammad Ishtiyaq; Amin, A. K. M. Nurul; Patwari, Muhammed Anayet Ullah

doi:10.1109/iccce.2008.4580819

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…In cases where the quality of the surface has significant importance and requires an indicator, the arithmetic mean roughness (Ra) is often used. Some researchers have investigated the influence of cutting parameters (cutting speed, feed rate, axial and radial depth of the cut) on the arithmetic mean roughness (Ra) [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…The focus of this study is on the modeling of artificial neural networks for the prediction of the arithmetic mean roughness (Ra) in milling. Previous studies showed that neural networks can be applied for surface roughness predictions in different machining operations such as turning [5,[9][10][11]14,16,19], milling [3,4,[6][7][8]13,15,17,18,[20][21][22] and drilling [12].…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the cost of experiments, researchers often use the Taguchi design of experiments [5,6,15,17,20,21], although when the number of the factors and levels in the Taguchi design of the experiment is not too high, an experiment can be conducted as a full factorial [4,14]. The input-output datasets used in all the above-mentioned studies for handle surface roughness predictions can be considered small.…”

Section: Introductionmentioning

confidence: 99%

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Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

et al. 2022

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Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

et al. 2022

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“…ANN consists of input, hidden and output layers as shown in Figure 4. Initially, a network is created in the training of a feed forward network [10][11]. The ANN knowledge in interconnection weights is adjusted during training process.…”

Section: Neural Network Modelmentioning

confidence: 99%

Prediction of Surface Roughness for CNC Turning of EN8 Steel Bar Using Artificial Neural Network Model

Srivastava¹,

Sharma²,

Gaur³

et al. 2019

JESA

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Manufacturing organizations are under tremendous pressure to produce quality products at reduced manufacturing cost with shorter time-to-market. A good combination of process parameters for any manufacturing process may ensure production of quality parts at reduced time and cost. Aim of the present study is to develop an artificial neural network (ANN) model for CNC turning operation to predict surface roughness of EN8 bar for a specific set of parameters. In this study, effect of CNC turning parameters, namely spindle speed, depth of cut and feed rate, each with three levels, on surface roughness of EN8 steel bar has been studied experimentally. Total 27 experiments have been conducted as per full factorial design of experiment approach and surface roughness of the turned pieces is evaluated using a Mitutoyo make roughness tester. One set of the roughness values has been used to train the ANN model and another set is used to test the predictability of the model in MATLAB 8.0 software. The average error between the experimental and predicted values is found to be 7.85 %.

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“…The system produced an accuracy of around 90%. Hossain et al [12] developed ANN to predict surface roughness during high speed machining of Inconel 718 with a single-layer PVD TiAlN insert. A BP neural network with two hidden layers having 15 neurons each, three input neurons (cutting speed, feed rate, and depth of cut), and one output neuron (surface roughness).…”

Section: Surface Roughnessmentioning

confidence: 99%

Application of ANN in Milling Process: A Review

Al-Zubaidi

Ghani

Haron

2011

Modelling and Simulation in Engineering

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In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

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Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

Cited by 15 publications

References 7 publications

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Prediction of Surface Roughness for CNC Turning of EN8 Steel Bar Using Artificial Neural Network Model

Application of ANN in Milling Process: A Review

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