Efficient mold cooling optimization by using model reduction

Schmidt, Fabrice; Pirc, Nicolas; Mongeau, Marcel; Chinesta, Francisco

doi:10.1007/s12289-010-0988-5

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…Shape optimization was performed by considering all the geometrical parameters as extra-coordinates, leading to the model solution in any of the geometries generated by the parameters considered as extra-coordinates [88]. This strategy could be an alternative to the POD-based shape optimization considered in [117]. Inverse methods in the context of real-time simulations were addressed in [55] and were coupled with control strategies in [53] as a first step towards DDDAS (dynamic data-driven application systems).…”

Section: Real-time Simulation Dddas and Morementioning

confidence: 99%

A Short Review on Model Order Reduction Based on Proper Generalized Decomposition

Chinesta

Ladevèze²,

Cueto³

2011

Arch Computat Methods Eng

Self Cite

561

437

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This paper revisits a new model reduction methodology based on the use of separated representations, the so called Proper Generalized Decomposition-PGD. Space and time separated representations generalize Proper Orthogonal Decompositions-POD-avoiding any a priori knowledge on the solution in contrast to the vast majority of POD based model reduction technologies as well as reduced bases approaches. Moreover, PGD allows to treat efficiently models defined in degenerated domains as well as the multidimensional models arising from multidimensional physics (quantum chemistry, kinetic theory descriptions, . . .) or from the standard ones when some sources of variability are introduced in the model as extra-coordinates.

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Section: Real-time Simulation Dddas and Morementioning

confidence: 99%

A Short Review on Model Order Reduction Based on Proper Generalized Decomposition

Chinesta

Ladevèze²,

Cueto³

2011

Arch Computat Methods Eng

Self Cite

561

437

View full text Add to dashboard Cite

show abstract

“…This set of vectors is optimal in the sense that they are orthogonal to each other while minimizing the distance between the original collection of snapshots and its reduced representation. More details regarding POD and its implementation are given in [10] and [11].…”

Section: Construction Of a Basis Using Podmentioning

confidence: 99%

Toward a Real Time Control of Toolpath in Milling Processes

Poulhaon

Rauch

Leygue

et al. 2013

KEM

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Real-time control of manufacturing processes is a challenging issue for nowadays industry. The need for ever more efficient production requires new strategies in order to make correct decisions in an acceptable time. In a large number of cases, operators working on a CNC machine tool have a reduced number of possibilities for interacting in real-time with the machine. Numerical simulation based control is in that sense an appealing alternative to the conventional approach since it provides the operator with an additional source of information, confirming his choices or in reverse suggesting a more adapted strategy. The main goal of this work is to propose a method to move from a bilateral approach (operator and CNC controller) to a trilateral one where the simulation is an active component of the manufacturing process. This paper focuses on a simple issue sometimes encountered in milling processes: how to remove a constant thickness of material at the surface of a part whose exact geometry is unknown? The difficulty lies in the choice of an appropriate trajectory for the tool. So far the method which is employed consists in acquiring the geometry of the part thanks to a palpation step made prior to milling. However, this step has to be repeated for each part and can become rather fastidious as the size of the part increases. The approach presented here gets rid of the palpation step and makes use of online measurements for identifying the real geometry and correcting the trajectory of the tool in accordance. By monitoring the forces applying on the tool (directly on the NC), we have access to the milling depth and therefore to the geometry of the part at several locations along the trajectory of the tool. This information is used as an input data for our numerical model running on an external device, which finally derives an approximation for the geometry. An optimized trajectory is then obtained and is updated on the machine. This procedure is repeated as the tool moves forward and it allows for a fast and robust on-line correction of the toolpath.

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Section: Introductionmentioning

confidence: 99%

“…The thermal conductivity of polymers is another important property for industrial processing like injection molding, in particular for optimizing heat transfer [ 56 ]. If the heat transfer is not fully optimized, the process cycle time can be longer than necessary and hot spots can occur, leading to high scrap rates [ 56 ]. On the other hand, there are many reasons to benefit from thermally conductive polymer-based composites in various industrial applications, mainly in electronic devices, heat exchangers and functional materials [ 4 , 6 , 7 , 57 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Melt Shear Viscosity, Specific Volume and Thermal Conductivity of Low-Density Polyethylene/Multi-Walled Carbon Nanotube Composites Using Capillary Flow

2020

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Understanding the flow behavior of polymer/carbon nanotube composites prior to melt processing is important for optimizing the processing conditions and final product properties. In this study, the melt shear viscosity, specific volume and thermal conductivity of low-density polyethylene (LDPE) filled with multi-walled carbon nanotubes (MWCNTs) were investigated for representative processing conditions using capillary rheometry. The experimental results show a significant increase in the melt shear viscosity of the LDPE/MWCNT composite with nanotube loadings higher than 1 wt.%. Upon increasing shear rates, the composites flow like a power-law fluid, with a shear-thinning index less than 0.4. The specific volume decreases with increasing pressure and nanotube loading, while the pVT transition temperature increases linearly with increasing pressure. The thermal conductivity of the LDPE/MWCNT composite is nearly independent of nanotube loading up to the thermal percolation threshold of 1 wt.% and increases linearly with further increases in nanotube loading, reaching 0.35 W/m·K at 5 wt.%. The Carreau–Winter and Cross viscosity models and Tait equation, respectively, are able to predict the shear viscosity and specific volume with a high level of accuracy. These results can be used not only to optimize processing conditions through simulation but also to establish structure–property relationships for the LDPE/MWCNT composites.

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Efficient mold cooling optimization by using model reduction

Cited by 12 publications

References 27 publications

A Short Review on Model Order Reduction Based on Proper Generalized Decomposition

A Short Review on Model Order Reduction Based on Proper Generalized Decomposition

Toward a Real Time Control of Toolpath in Milling Processes

Experimental Investigation of the Melt Shear Viscosity, Specific Volume and Thermal Conductivity of Low-Density Polyethylene/Multi-Walled Carbon Nanotube Composites Using Capillary Flow

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