Optimising cooling performance of calibrators for extruded profiles

Abstract

In a previous work dealing with the parameters influencing the calibration/cooling stage of profile extrusion, it was concluded that the two main objectives of the calibration stage are conflicting, i.e. conditions leading to a high efficiency will generally promote high temperature gradients at the same location, and vice versa. The exceptions were the reduction of the extrusion velocity and splitting the calibrator into several units, separated by annealing zones. Therefore, the only obvious way to improve the cooling performance of the calibrator without compromising the production rate is the division of the total cooling length into several independent units. In the present work, an optimisation methodology (encompassing a numerical simulation code, an objective function and an optimisation routine) is used to optimise the lengths of the individual cooling units and of the annealing zones existing between them with a view to improve the cooling performance of the calibration/cooling stage.

Profile Extrusion: Experimental Assessment of a Numerical Code to Model the Temperature Evolution in the Cooling/Calibration Stage

“…The results obtained allowed to conclude that the interface heat transfer coefficient depends on both the calibrator geometry and on the extrusion flow rate. Later, an algorithm comprising a nonisothermal 3D code was developed by Nóbrega et al . In the first stage of the work, a nonisothermal 3D code was developed and validated with the analytical solution of a simple problem, with the results obtained from the literature, and with the results predicted by a commercial software .…”

“…Later, this motivated the development of a prototype system and corresponding methodology aimed at characterizing the value of this parameter, under typical and controlled calibration conditions . Still in this framework, an optimization routine was coupled to the modeling code to aid the identification of the optimal system design . However, the numerical code was limited to structured meshes and was, thus, unable to handle complex geometries, common in industrial applications.…”

Prototype and methodology for the characterization of the polymer-calibrator interface heat transfer coefficient

“…In fact, in continuous processes, such as extrusion, the cooling stage is usually responsible for limiting the maximum velocity of production, whereas in cyclic processes, such as injection moulding, thermoforming and blow-moulding it determines a significant part of the total cycle time (that can be of the order of 80%). Low thermal diffusivity is also responsible for the development of considerable thermal gradients during the cooling stage [1][2][3][4] and, consequently, for the development of stresses [5][6][7][8][9][10][11][12][13] that can be frozen in the product (generally referred as residual thermal stresses) that will affect negatively the mechanical performance of the product in use [14][15][16][17][18].…”

“…Having in mind the above, the research team of the current work developed and validated an algorithm for the thermal design of calibrators for thermoplastic profiles, encompassing a non-isothermal 3D code based on the finite volume method (FVM) to model the thermal interchanges during the extrusion calibration/cooling stages, geometry and mesh generators, and an optimization routine aiming at determining the optimal cooling conditions [15][16][17][18]. A major difficulty to be faced in the use of the referred modelling code is an adequate prescription of the heat transfer coefficient, h, between the plastic profile surface and the cooling medium, i.e., calibrator internal walls, water or air, which must include the effect of the contact resistance.…”

Multi-objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches

“…Real‐world multi‐objective optimization problems (MOOP) often require the use of complex “black‐box” modeling routines able to link the design variables with the optimization criteria. This is frequently done through the resolution of the process governing equations and using expensive numerical methods, such as finite differences, volumes and/or elements (Carneiro and Nobrega, 2004; Covas et al, 1999; Gaspar‐Cunha, 2000; Gaspar‐Cunha et al, 2002; Nobrega and Carneiro, 2006; Rauwendaal, 1986).…”