Estimation of heat flux imposed on the rake face of a cutting tool: A nonlinear, complex geometry inverse heat conduction case study

Samadi, Forooza; Kowsary, Farshad; Sarchami, Araz

doi:10.1016/j.icheatmasstransfer.2011.10.007

Cited by 32 publications

(7 citation statements)

References 8 publications

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“…Figure 9(a) shows good agreement between calculated and experimental temperatures. However, Figure 9(b) shows that the residue is lower for temperatures calculated with exponential heat flux (7). Figure 10 shows the temperature profile in the tool for the following cutting conditions: feed rate = 0.138 mm/rot, cutting speed = 142 m/min (900 rpm), and cutting depth = 1.0 mm, where the maximum temperature calculated at the chip-tool interface was 600 ∘ C.…”

Section: Methodsmentioning

confidence: 99%

“…In Samadi et al [7] the sequential function specification method was used with simulated temperature data to estimate the transient heat flux imposed on the rake face of a cutting tool during the cutting operation with two different hypotheses. The thermal conductivity was considered constant in one, and in the other it varied with temperature.…”

Section: Introductionmentioning

confidence: 99%

“…Temperature profile for feed rate = 0.138 mm/rot, cutting speed = 142 m/min (900 rpm), and cutting depth = 1.0 mm: (a) tool, (b) cutting interface temperatures for exponential heat flux(7), and (c) cutting interface temperatures for uniform heat flux(6).…”

mentioning

confidence: 99%

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Analyses of Effects of Cutting Parameters on Cutting Edge Temperature Using Inverse Heat Conduction Technique

Santos

Silva

Machado

et al. 2014

Mathematical Problems in Engineering

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During machining energy is transformed into heat due to plastic deformation of the workpiece surface and friction between tool and workpiece. High temperatures are generated in the region of the cutting edge, which have a very important influence on wear rate of the cutting tool and on tool life. This work proposes the estimation of heat flux at the chip-tool interface using inverse techniques. Factors which influence the temperature distribution at the AISI M32C high speed steel tool rake face during machining of a ABNT 12L14 steel workpiece were also investigated. The temperature distribution was predicted using finite volume elements. A transient 3D numerical code using irregular and nonstaggered mesh was developed to solve the nonlinear heat diffusion equation. To validate the software, experimental tests were made. The inverse problem was solved using the function specification method. Heat fluxes at the tool-workpiece interface were estimated using inverse problems techniques and experimental temperatures. Tests were performed to study the effect of cutting parameters on cutting edge temperature. The results were compared with those of the tool-work thermocouple technique and a fair agreement was obtained.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyses of Effects of Cutting Parameters on Cutting Edge Temperature Using Inverse Heat Conduction Technique

Santos

Silva

Machado

et al. 2014

Mathematical Problems in Engineering

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“…(7)(8)(9), N x and N y are the total number of nodes along the x and y directions, respectively. i and j represent the node locations along the x and y directions.…”

Section: A Nonlinear Direct Problemmentioning

confidence: 99%

“…IHCPs have been studied extensively due to their applications in various engineering disciplines. High interest in these kinds of problems makes inverse problems in heat conduction widely discussed in the literature (see, e.g., [2][3][4][5][6][7][8][9][10]). …”

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confidence: 99%

Robust Inverse Approach for Two-Dimensional Transient Nonlinear Heat Conduction Problems

Cui

Liu

et al. 2015

Journal of Thermophysics and Heat Transfer

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Inverse heat conduction analysis provides an efficient approach for estimating the thermophysical properties of materials, the boundary conditions, or the initial conditions. In this paper, two-dimensional transient nonlinear inverse heat conduction problems are investigated, for estimating time-and space-dependent boundary heat flux, as well as the temperature-dependent thermal conductivities. Modifications are carried out to extend the previous onedimensional inversion algorithm to solve the two-dimensional transient nonlinear heat conduction problem, to overcome the frequently occurring divergence issues, and to improve the stability of the inversion algorithm. Boundary-only measurements are used as additional information, and a dimensionless objective function is adopted. In the direct problem, formulations for solutions to the two-dimensional transient nonlinear heat conduction problem are derived and validated. Numerical examples show that the inversion algorithm is effective, efficient, accurate, and robust, for recovering multiple parameters, with and without a functional form. Nomenclature a = coefficient to be recovered in Eq. (27), W∕m 2 b = coefficient to be recovered in Eq. (27), 1∕s c = heat capacity, J∕kg · K d = coefficient to be recovered in Eq. (27) E rms = root mean square deviation betweenthe recovered/ inverted and the exact/real values Fo = Fourier number Fo x = λ∕ρc × Δτ∕Δx 2 Fo y = λ∕ρc × Δτ∕Δy 2 fX = real function with variable X fX ih = complex function with real variable X and imaginary part h, to be expanded into Taylor series h = imaginary part in complex variable i = the ith node numberalong x coordinate j = the jth node numberalong y coordinate K = the iteration number k = the kth time interval L = the length of the modeled object in x direction, m M = total number of measured temperatures N = total number of inverted parameters N x = total number of nodes along x coordinate N y = total number of nodes along y coordinate q = heat flux, W∕m 2 R = residual vector S = dimensionless objective function t = temperature, K W = the width of the modeled object in y direction, m w = relaxation factor X = real variable in function f x = x coordinate, m y = y coordinate, m z = vector of inverted parameters Greek γ = exact value Δ = change in variable ζ = random measurement error η = random number λ = thermal conductivity, W∕m · K ξ = small positive number ρ = density, kg∕m 3 χ = recovered/inverted value τ = heating time, s Subscripts 0 = initial time b = bottom exact = exact i = the ith component of a vector i 1∕2 = midpoint between nodes i and i 1 i; j = node location at the ith node number along the x direction, and the jth node along the y direction j = the jth component of a vector l = left r = right u = upper Superscripts 0 = initial guess * = measured

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A novel numerical modeling approach to determine the temperature distribution in the cutting tool using conjugate heat transfer (CHT) analysis

Pervaiz

Deiab

Wahba

et al. 2015

Int J Adv Manuf Technol

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Estimation of heat flux imposed on the rake face of a cutting tool: A nonlinear, complex geometry inverse heat conduction case study

Cited by 32 publications

References 8 publications

Analyses of Effects of Cutting Parameters on Cutting Edge Temperature Using Inverse Heat Conduction Technique

Analyses of Effects of Cutting Parameters on Cutting Edge Temperature Using Inverse Heat Conduction Technique

Robust Inverse Approach for Two-Dimensional Transient Nonlinear Heat Conduction Problems

A novel numerical modeling approach to determine the temperature distribution in the cutting tool using conjugate heat transfer (CHT) analysis

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