Thermal Aspects in Deep Hole Drilling of Aluminium Cast Alloy Using Twist Drills and MQL

Biermann, Dirk; Iovkov, Ivan; Blum, H.; Rademacher, Andreas; Taebi, K.; Suttmeier, F. T.; Klein, N.

doi:10.1016/j.procir.2012.07.043

Cited by 48 publications

(23 citation statements)

References 7 publications

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“…This proves that the fluid flow through the drills flutes is mainly responsible for the chip transport and stringently required to achieve a sufficient chip extraction. This also corresponds to findings in deep hole drilling using MQL by Biermann et al [9]. Furthermore it was found out that an acceptable chip extraction is also achieved by pressured air without lubrication.…”

Section: Resultssupporting

confidence: 89%

Quantitative analysis of chip extraction in drilling of Ti6Al4V

2015

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Section: Resultssupporting

confidence: 89%

Quantitative analysis of chip extraction in drilling of Ti6Al4V

2015

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“…Richardson et al [6] compare analytically modeled temperatures and the temperatures of thermocouples of dry milling processes. Biermann et al [7] also use thermocouples but compare the measured temperatures of deephole drilling experiments to temperatures of a three dimensional Finite-Element-Method (FEM) simulation. The FEM is also used by a couple of other heat impact determinations.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification for Finite Element Based Models in Dry Machining Applications

Wernsing¹,

Büskens²

2015

Procedia CIRP

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Dry machining is a challenging topic in industrial manufacturing: The absence of coolants results in ecological and economical benefits, but also in a significant increase of the occurring thermal stress. This leads to geometrical deviations of the machined workpiece impairing the functional performance of the final part. The increased thermal stress is especially important in high-precision manufacturing and requires careful process planning. To determine process parameters in order to minimize geometrical errors, optimal control is an appropriate tool. A precise partial differential equation model representing the material behavior is required. The partial differential equation contains unknown physical quantities, such as heat fluxes, which cannot be measured directly. One common method to derive these quantities is solving an inverse problem by parameter identification, i.e. determining the model parameters, such that the model matches the measurements best. We will present a simultaneous analysis and design approach which states a numerical finite element discretization of the model as constraints of the resulting nonlinear optimization problem (NLP). The proposed method takes advantage of both, direct consideration of state constraints, like temperature boundaries, and lower computational costs compared to the nested analysis and design approach. The additional implementation costs can be significantly reduced by using an external finite element library for assembling the optimization constraints and its derivatives. This approach for determining physical parameters of milling experiments is user-independent and works fully automated. It is the first step for an optimal control of dry machining processes. We will give a description of this method as well as numerical identification results of real heat flux densities by the sparse NLP solver WORHP.

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“…Nevertheless, heat input Q 00 wp should be considered in deeper holes than 15 mm, where the difference, and as a consequence the error, between predicted and experimental results is much too important to dismiss [24]. On the other hand, in the cutting zone, heat transfer occurs along the drill's cutting edges and its chisel edge.…”

Section: Heat Fluxmentioning

confidence: 99%

“…However, to our knowledge, no research has used the inverse simulation technique to determine the amount of heat transferred to the workpiece based on the cutting conditions. Biermann et al studied deep hole drilling of aluminium cast alloy using twist drills with MQL [24,25] and concluded that the feed is the determining factor for both the mechanical and the thermal load of the workpiece: the lower the feed rate the higher the heat loading of the workpiece. They observed as well that the thermal load of the machined part depends significantly on the machining time.…”

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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Thermal Aspects in Deep Hole Drilling of Aluminium Cast Alloy Using Twist Drills and MQL

Cited by 48 publications

References 7 publications

Quantitative analysis of chip extraction in drilling of Ti6Al4V

Quantitative analysis of chip extraction in drilling of Ti6Al4V

Parameter Identification for Finite Element Based Models in Dry Machining Applications

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

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