Artificial Neural Network Model for Prediction of Tool Tip Temperature and Analysis

Taşdemir, Şakir

doi:10.18201/ijisae.2018637937

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…In (7.2) and (7.3), y i represents the actual output, y p i represents the ANN predicted output, and ȳ represents the mean of the actual outputs. The network architecture with the highest R 2 and least MSE on the test data set is concluded to be the suitable network [36].…”

Section: A Combined Finite Element and Machine Learning Approach In Machining 71mentioning

confidence: 99%

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A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining

Mekarthy¹,

Hashemitaheri²,

Cherukuri³

2021

etna

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In machining, specific cutting forces and temperature fields are of primary interest. These quantities depend on many machining parameters, such as the cutting speed, rake angle, tool-tip radius, and uncut chip thickness. The finite element method (FEM) is commonly used to study the effect of these parameters on the forces and temperatures. However, the simulations are computationally intensive and thus, it is impractical to conduct a simulation-based parametric study for a wide range of parameters. The purpose of this work is to present, as a proof-of-concept, a hybrid methodology that combines the finite element method (FE method) and machine learning (ML) to predict specific cutting forces and maximum tool temperatures for a given set of machining conditions. The finite element method was used to generate the training and test data consisting of machining parameter values and the corresponding specific cutting forces and maximum tool temperatures. The data was then used to build a predictive model based on artificial neural networks. The FE models consist of an orthogonal plane-strain machining model with the workpiece being made of the Aluminum alloy Al 2024-T351. The finite element package Abaqus/Explicit was used for the simulations. Specific cutting forces and maximum tool temperatures were calculated for several different combinations of uncut chip thickness, cutting speed and the rake angle. For the machine learning-based predictive models, artificial neural networks were selected. The neural network modeling was performed using Python with Adam as the training algorithm. Both shallow neural networks (SNN) and deep neural networks (DNN) were built and tested with various activation functions (ReLU, ELU, tanh, sigmoid, linear) to predict specific cutting forces and maximum tool temperatures. The optimal neural network architecture along with the activation function that produced the least error in prediction was identified. By comparing the neural network predictions with the experimental data available in the literature, the neural network model is shown to be capable of accurately predicting specific cutting forces and temperatures.

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Section: A Combined Finite Element and Machine Learning Approach In Machining 71mentioning

confidence: 99%

“…Abdullah et al [38] and Tasdemir in [35,36] determined the best neural network architecture by monitoring statistical results obtained by computing the mean squared error (MSE) and the coefficient of determination R 2 . The model with the least MSE and highest R 2 was selected to be the most suitable network architecture.…”

mentioning

confidence: 99%

A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining

Mekarthy¹,

Hashemitaheri²,

Cherukuri³

2021

etna

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“…ANN provides effective solutions in system modeling [9]. Although FLCs are effective in many applications, both of the software and hardware design of a FLC's presents a number of difficulties.…”

Section: Related Workmentioning

confidence: 99%

Imitation of Fuzzy Logic Controller Based Artificial Neural Network, and Application of Inverted Pendulum System Control

Can

Sam

2019

European Journal of Technic

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Fuzzy Logic Controllers (FLCs) are effective solutions for nonlinear and parameter variability systems, but it contains multiple mathematical operations causing the controller to react slowly. This study aims to obtain a controller that can imitate the effective control performance of the FLC, which is easy to design both in software and hardware, and has a short response time. Artificial neural networks (ANNs) provide effective solutions in system modeling. Modeling of FLC has been realized by using of ANN's learning and parallel processing capability. The design process of the FLC and the training processes of the ANN were studied in Matlab SIMULINK environment. In the study, FLC was modelled at high similarity ratio with small ANN structure. ANN results were obtained very faster than the FLC control performance. The control performances of two controllers were observed to be very close to each other. As a result, ANN model has smaller structure than FLC, which makes it possible to implement the controller easily in terms of hardware and software.

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“…GF addition also reduces the crack width and negative temperature effects [12,13]. Data based prediction models including ANN, Multiple Linear Regression (MLR) are widely used in various engineering applications [14][15][16][17]. These models can give further information for a better understanding of the material properties [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

Yıldızel¹,

Uzun²,

Çöğürcü³

2019

ijisae

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Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.

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Artificial Neural Network Model for Prediction of Tool Tip Temperature and Analysis

Cited by 5 publications

References 14 publications

A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining

A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining

Imitation of Fuzzy Logic Controller Based Artificial Neural Network, and Application of Inverted Pendulum System Control

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

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