Delamination analysis of the helical milling of carbon fiber-reinforced plastics by using the artificial neural network model

Qin, Xuda; Wang, Bin; Wang, Guofeng; Li, Hao; Jiang, Yuedong; Zhang, Xinpei

doi:10.1007/s12206-013-1135-2

Cited by 43 publications

(20 citation statements)

References 14 publications

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“…Recent research [26] shows that helical milling of CFRP results in much lower delamination than that in other processing methods including high-speed drilling. This is because helical milling reduces the thrust force, and more space is available to expel the chips.…”

Section: Delamination In Millingmentioning

confidence: 99%

5. Delamination in composite materials: measurement, assessment and prediction

Babu¹,

Philip²,

Zacharia³

et al. 2015

Machinability of Fibre-Reinforced Plastics

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Delamination of composites is the dominant machining defect, and its elimination by choosing proper machining parameters can bring in benefits in terms of service reliability and cost reduction. Drilling-induced delamination usually occurs at the start of drilling -called peel-up delamination -and at the exit of the drill, called push-out delamination. The common methods to measure the extent of delamination are briefly described in this chapter. These measurements are used to develop assessment factors, both dimensional and non-dimensional, for comparing delamination damage resulting from different machining methods and parameters. The delamination factors proposed by different researchers are explained while pointing out their merits and demerits. A universally applicable delamination measure is yet to be developed. Modeling techniques such as regression analysis, artificial neural network and FE simulation used by different researchers for predicting the delamination damage are also described.

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Section: Delamination In Millingmentioning

confidence: 99%

5. Delamination in composite materials: measurement, assessment and prediction

Babu¹,

Philip²,

Zacharia³

et al. 2015

Machinability of Fibre-Reinforced Plastics

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show abstract

“…To correctly identify texture images,machine learning is considered to be a prominent techique. It is used for classification and regression and is used by many authors in the variety of applications such as EEG signal classification [14], Fault diagnosis [15,16], Manufacturing [17,18] etc. Support Vector Machine (SVM) [19], Artificial Neural Network (ANN) [20], Naive Bayes (NB) are widely used machine learning algorithms and is used by many authors for classification purpose.…”

Section: Introductionmentioning

confidence: 99%

Texture classification of machined surfaces using image processing and machine learning techniques

Patel¹,

Vakharia²,

Kiran³

2019

FME Transactions

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The identification of surface texture images from machining surfaces using image processing techniques has been a prominent research area in the recent decades. The aim of this paper is to identify various machined surface texture images using machine learning techniques. Charge coupled device is used to capture images of machined components. Based on captured images, twelve statistical features are extracted and feature vector is formed. Grey Level Co-occurrence Matrix is used to extract statistical features from the machined surface images. Four Machine learning algorithms such as Random Forest, Support Vector Machine, Artificial Neural Network and J48 were utilized to characterize machined surfaces. Training and Tenfold cross validation process is utilized for identification of machined component images. It is found that Artificial Neural Network and Random forest give100 % training accuracy and 99% cross validation accuracy. Results obtained demonstrate the efficiency of proposed methodology, which is useful for identifying texture images.

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“…Artificial neural network model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. Qin et al [11] explored the better hole quality with one-time machining by helical milling in a carbon fiber-reinforced plastic. A neural network model developed based on the correlation between the delamination and the process parameters.…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural network models for the prediction of thrust force and torque in drilling operation of Al7075

Kyratsis

Efkolidis

Daniel³

et al. 2018

MATEC Web Conf.

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Abstract. This study investigates the thrust force (Fz) and torque (Mz) in a drilling process of an Al7075 workpiece using solid carbide tools (Kennametal KC7325), depending on the effects of crucial cutting parameters such as cutting velocity, feed rate and tool diameter of 10mm, 12mm and 14mm. Artificial neural networks (ANN) methodology is used in order to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. The ANN results showed that the best prediction topology of the network for the thrust force was the one with five neurons in the hidden layer, while for the case of Mz the best network topology for the prediction of the experimental values was the one with six neurons in the hidden layer. Based on the results acquired, the ANN models achieved accuracy of 1,96% and 1,95% for both the thrust force and torque measured, while the R coefficient for the prediction model of the thrust force is 0.99976 and 00.99981 for the torque. As a result they can be considered as very accurate and appropriate for their prediction.

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Delamination analysis of the helical milling of carbon fiber-reinforced plastics by using the artificial neural network model

Cited by 43 publications

References 14 publications

5. Delamination in composite materials: measurement, assessment and prediction

5. Delamination in composite materials: measurement, assessment and prediction

Texture classification of machined surfaces using image processing and machine learning techniques

Using artificial neural network models for the prediction of thrust force and torque in drilling operation of Al7075

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