Response surface and neural network based predictive models of cutting temperature in hard turning

Mia, Mozammel; Dhar, Nikhil Ranjan

doi:10.1016/j.jare.2016.05.004

Cited by 106 publications

(43 citation statements)

References 23 publications

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“…The techniques for experimental measurements, especially those for temperature distribution, are briefly explained below. Further information can be found in References [7,38].…”

Section: Experimental Measurementsmentioning

confidence: 99%

“…The techniques for experimental measurements, especially those for temperature distribution, are briefly explained below. Further information can be found in References [7,38]. To measure the temperature at the PSZ, an IR camera was placed straight above the rake face of the tool so as to measure the temperature on the chip's free side, as shown in Figure 3.…”

Section: Experimental Measurementsmentioning

confidence: 99%

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Prediction of Temperature Distribution in Orthogonal Machining Based on the Mechanics of the Cutting Process Using a Constitutive Model

Ning

Liang

2018

JMMP

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This paper presents an original method of predicting temperature distribution in orthogonal machining based on a constitutive model of various materials and the mechanics of their cutting process. Currently, temperature distribution is commonly investigated using arduous experiments, computationally inefficient numerical analyses, and complex analytical models. In the method proposed herein, the average temperatures at the primary shear zone (PSZ) and the secondary shear zone (SSZ) were determined for various materials, based on a constitutive model and a chip-formation model using measurements of cutting force and chip thickness. The temperatures were determined when differences between predicted shear stresses using the Johnson-Cook constitutive model (J-C model) and those using a chip-formation model were minimal. J-C model constants from split Hopkinson pressure bar (SHPB) tests were adopted from the literature. Cutting conditions, experimental cutting force, and chip thickness were used to predict the shear stresses. The temperature predictions were compared to documented results in the literature for AISI 1045 steel and Al 6082-T6 aluminum in multiple tests in an effort to validate this methodology. Good agreement was observed for the tests with each material. Thanks to the reliable and easily measurable cutting forces and chip thicknesses, and the simple forms of the employed models, the presented methodology has less experimental complexity, less mathematical complexity, and high computational efficiency.

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“…The techniques for experimental measurements, especially those for temperature distribution, are briefly explained below. Further information can be found in References [7,38].…”

Section: Experimental Measurementsmentioning

confidence: 99%

Section: Experimental Measurementsmentioning

confidence: 99%

Prediction of Temperature Distribution in Orthogonal Machining Based on the Mechanics of the Cutting Process Using a Constitutive Model

Ning

Liang

2018

JMMP

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“…There are numerous ANN network struct ures and architectures in the literature. These includes the feed-forward back-propagation network which is commonly used for engineering and estimation operations [2,7,11]. ANN architecture for TTT estimation is "3-hl-1" (Figure 1).…”

Section: Artificial Neural Network Model For Tool Tip Temperaturementioning

confidence: 99%

“…In other words, TTT detection can be difficult to determine even after measurements. Therefore, different mathematical approaches can be used to calculate the surface temperature correctly [1, 4, 6,7,8,9,10]. In metal cutting, creating a model for determining TTT that includes all the cutting parameters and tool geometry is very difficult for such a non-linear and complex field.…”

Section: Introductionmentioning

confidence: 99%

“…In literature reviews; studies like various software that performs analysis based of the finite element technique [19], experimental, mathematical [4,5,6,18], statistical [20,15] and analytical models [21,12], three-dimensional model [13], optimization (using Taguchi method etc.) [14,17] and artificial intelligence methods [20,7,16,11] are used for modeling the temperature. The purpose of this study is to estimate and analyze TTT (T-0 C) in the turning process of metal cutting.…”

Section: Introductionmentioning

confidence: 99%

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

Taşdemir

2018

ijisae

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Technological improvements put computer systems in the center of our life and various scientific disciplines. These can range from controlling a device in our home to public institutions and the industry. One of these disciplines is a sub-area in mechanical engineering called machining is concerned with not only mechanical systems but also computer aided systems. Artificial Neural Networks -an area of artificial intelligence-which is concerned with learning and decision making of computers is a field that scientists are very interested in. In this study, an Artificial Neural Network system was designed for predicting the temperature at the tool tip in the machining process. In the metal cutting process, tool tip temperature is one of the conditions that must be identified, analyzed and monitored. For this purpose, an ANN model was developed to determine the tool tip temperature in the turning process. In the designed ANN model, parameters consisting of three inputs and one output were used. The three input variables were rake angle (γ-o ), approaching angle (-o ), feedrate (fmm/rev) respectively. The output parameter was the tool tip temperature (T-0 C). The most appropriate model was determined according to Mean Squared Error ratio. In the test phase of the Artificial Neural Network, the smallest Mean Squared Error was obtained with the Artificial Neural Network topology formed as 3-4-1. In this Artificial Neural Network model, calculations were Mean Squared Error=0.00144, R 2 =0.9956 (absolute fraction of variance) in the training phase and Mean Squared Error=0.00231, R 2 =0.9954 in the test phase. The results show that the designed Artificial Neural Network model can be used for predicting and analyzing tool tip temperature.

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Modeling and Optimization Algorithms in Rapid Prototyping, Submerged Arc Welding, and Turning

Gupta

Mia

Gupta³

et al. 2021

Modeling and Optimization in Manufacturing

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Response surface and neural network based predictive models of cutting temperature in hard turning

Abstract: Graphical abstract

Cited by 106 publications

References 23 publications

Prediction of Temperature Distribution in Orthogonal Machining Based on the Mechanics of the Cutting Process Using a Constitutive Model

Prediction of Temperature Distribution in Orthogonal Machining Based on the Mechanics of the Cutting Process Using a Constitutive Model

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

Modeling and Optimization Algorithms in Rapid Prototyping, Submerged Arc Welding, and Turning

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