Modelling of cutting induced workpiece temperatures for dry milling

Richardson, David J.; Keavey, Mike; Dailami, Farid

doi:10.1016/j.ijmachtools.2005.08.008

Cited by 76 publications

(41 citation statements)

References 10 publications

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“…For operations like grinding and milling, it is recommended to use the thermocouple embedded in the work piece to measure rise in work piece temperature. Richardson et al [43], have noticed uniform rise of the work piece temperature and thus used one central thermocouple to measure the temperature rise. The similar strategy was used in the current work.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

Bhirud¹,

Gawande²

2017

Archive of Mechanical Engineering

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During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool-chip and tool-work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L 18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.

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Section: Methodsmentioning

confidence: 99%

Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

Bhirud¹,

Gawande²

2017

Archive of Mechanical Engineering

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“…In an operation of milling, the temperature of the edge of cut increases according to the duration of the milling and cutting speed [5]. Energy necessary to the operation is provided by the spindle and can be determined by the measurement of the torque and number of revolutions.…”

Section: Estimation Of the Total Power Due To Millingmentioning

confidence: 99%

Thermal modelling of cooling tool cutting when milling by electrical analogy

Benabid

Arrouf

Assas

et al. 2010

EPJ Web of Conferences

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Abstract. Measurement temperatures by (some devises) are applied immediately after shut-down and may be corrected for the temperature drop that occurs in the interval between shut-down and measurement. This paper presents a new procedure for thermal modelling of the tool cutting used just after machining; when the tool is out off the chip in order to extrapolate the cutting temperature from the temperature measured when the tool is at stand still. A fin approximation is made in enhancing heat loss (by conduction and convection) to air stream is used. In the modelling we introduce an equivalent thermal network to estimate the cutting temperature as a function of specific energy. In another hand, a local modified element lumped conduction equation is used to predict the temperature gradient with time when the tool is being cooled, with initial and boundary conditions. These predictions provide a detailed view of the global heat transfer coefficient as a function of cutting speed because the heat loss for the tool in air stream is an order of magnitude larger than in normal environment. Finally we deduct the cutting temperature by inverse method.

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“…Analytical studies on thermal balance in the cutting zone have been carried out by numerous authors [6][7][8][9][10][11][12] in the past, but in most cases, these reports have foused on the chip and/or the tool. Trigger and Chao [6] and Loewen and Shaw [7] proposed two different thermal balance analytical models.…”

Section: Introductionmentioning

confidence: 99%

Heat-flow determination through inverse identification in drilling of aluminium workpieces with MQL

Segurajauregui

Arrazola

2015

Prod. Eng. Res. Devel.

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One of the current trends in machining is the reduction or elimination of the use of cutting fluids to reduce economic and ecological costs. As a consequence, heating of the workpieces is increased, leading to greater thermal distortion. This distortion can be predicted using the finite-element method (FEM), provided heat input to the workpiece for the machining operation is known. The aim of this research is to determine the quantity and ratio of heat generated in drilling operations and to quantify the heat transferred to the workpiece based on cutting speed and feed rate. Thus, a detailed study of workpiece heating during the drilling process with minimum quantity of lubricant was conducted, and a combination of experimental and numerical data was used to obtain these parameters. The experiments were carried out on aluminium (Al2017) samples. Drilling torque and temperature of the workpiece were measured. The cutting procedure involved drilling a Ø10 9 15 mm blind hole using uncoated cemented carbide tools. Temperature was measured by infrared methods, and cutting torque was obtained using a piezoelectric dynamometer. The FEM model was developed using the general-purpose software ABAQUS/ StandardÓ v6.5 and the inverse simulation technique was used to calculate heat input to the workpiece for each machining condition. As a general conclusion, an increase in cutting speed and feed rate leads to a decrease in heat input and temperature in the workpiece and thus, thermal distortion of the part.

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Modelling of cutting induced workpiece temperatures for dry milling

Cited by 76 publications

References 10 publications

Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

Thermal modelling of cooling tool cutting when milling by electrical analogy

Heat-flow determination through inverse identification in drilling of aluminium workpieces with MQL

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