Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid

Davoodi, Behnam; Tazehkandi, Ahmadreza Hosseini

doi:10.1016/j.jclepro.2013.12.056

Cited by 106 publications

(40 citation statements)

References 15 publications

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“…The parts of thermocouple covered with insulation (which is heat-resistance even in 1400 C) are attached to the tool to measure its temperature. The attached point has a 0.5 mm distance from the tool tip (Davoodi and Tazehkandi, 2014b). Moreover, a MCS640 mode l thermal imager was utilized in order to monitor the temperatures produced during machining.…”

Section: Cutting Forces Surface Roughness and Tool Tip Temperature Mmentioning

confidence: 99%

Application of liquid nitrogen and spray mode of biodegradable vegetable cutting fluid with compressed air in order to reduce cutting fluid consumption in turning Inconel 740

Tazehkandi

Shabgard

Pilehvarian

2015

Journal of Cleaner Production

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Section: Cutting Forces Surface Roughness and Tool Tip Temperature Mmentioning

confidence: 99%

Application of liquid nitrogen and spray mode of biodegradable vegetable cutting fluid with compressed air in order to reduce cutting fluid consumption in turning Inconel 740

Tazehkandi

Shabgard

Pilehvarian

2015

Journal of Cleaner Production

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“…Nevertheless, empirical models are limited to specific experimental conditions. The Taguchi method and response surface methodology (RSM) are the most often employed statistical techniques for determining the relationship between different controllable parameters and output performance (Davoodi and Tazehkandi, 2014;Hewidy et al, 2005).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production

Maher

Sarhan

Barzani

et al. 2015

Journal of Cleaner Production

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“…For this purpose, a model was designed to enable the network to learn the pattern between input and output data [2,3,11]. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid

Cited by 106 publications

References 15 publications

Application of liquid nitrogen and spray mode of biodegradable vegetable cutting fluid with compressed air in order to reduce cutting fluid consumption in turning Inconel 740

Application of liquid nitrogen and spray mode of biodegradable vegetable cutting fluid with compressed air in order to reduce cutting fluid consumption in turning Inconel 740

Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production

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

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