Optimising the flow pipe arrangement for resin infusion under flexible tooling

Kessels, J.F.A.; Jonker, Attie; Akkerman, Remko

doi:10.1016/j.compositesa.2007.04.008

Cited by 20 publications

(17 citation statements)

References 15 publications

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“…In contrast to the distance criterion proposed in [2], [3], [20], [21], where the optimal flow pattern in each time instant must be vent oriented, if the FPDS-1D is developed from the inlet point, the flow front appears as a straight line, as the flow has a radial inlet behaviour, Figure 4 (bottom). Then, the ideal flow front behaviour is only vent oriented nearby the outlet but is inlet-gate oriented, nearby the inlet.…”

Section: Figure 5 Skeleton (Left)optimal Inlet (Right)mentioning

confidence: 88%

“…The flow front must be vent oriented nearest to the vent, [2], [3], [20], [21]. This map is defined in a Flow Pattern Configuration Space proposed in our previous works, [5][6][7] for the sampling rate reduction.…”

Section: Discussionmentioning

confidence: 99%

“…The key idea to use these spaces in on-line control strategies is to represent the mould geometry in an alternative Cartesian space using optimization variables like distance, time, incubation time, etc,. In this sense, one of the common variables used in optimization and on-line control strategies is the node to node distance, [2], [3], [20], [21]. In particular, this criterion is based on the flow front distance to the vent imposing that the optimal flow pattern must be vent oriented, that is, the flow front nodes should have the similar distance to the vent in each time instant.…”

Section: Previous Workmentioning

confidence: 99%

“…Therefore, if the mould geometry is represented from the outlet point in a class of polar space, the flow front must be represented as a straight line if this optimal criterion is accomplished, see This space can be computed for 2.5D complex geometries by simply computing the geodesic distance instead of the Euclidean distance, [5][6][7]. If the inlet/outlet is a pipe shape like in RI, the minimum distance proposed in [20], [21] between each mould node to the pipe nodes is used as a distance criterion. This distance model is used in LCM processes, [20], [21], based on the assumption that the resin first fills the nodes that are closest to the inlet, then the next closest, etc,.…”

Section: Previous Workmentioning

confidence: 99%

“…If the inlet/outlet is a pipe shape like in RI, the minimum distance proposed in [20], [21] between each mould node to the pipe nodes is used as a distance criterion. This distance model is used in LCM processes, [20], [21], based on the assumption that the resin first fills the nodes that are closest to the inlet, then the next closest, etc,.…”

Section: Previous Workmentioning

confidence: 99%

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A topological approach for the flow behaviourcharacterization in LCM processes

2009

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In this work we propose a topological approach to explain the dynamical behaviour of a LCM flow. To this end we shall use a class of homotopy maps in order to characterize an idealized mould filling process. By means this benchmark model we can construct a functional that can be used to give the reference in an on-line control system. In our previous work, [1], is proposed the use of the artificial vision technique to sense the mould as a discretized mesh that can be simulated off-line and used as a benchmark model in real-time control systems. The resulting FEM simulation represents theoretical flow behaviour. In this sense, the ideal flow behaviour must be defined based on the optimal criterion. For instance in [2], [3], the flow front in each time instant is desirable to be vent-oriented for dry spot prevention. Whatever of these optimal laws can be defined as a homotopy map where the flow front is defined as a parametric curve that moves with the flow. In order to reduce the sampling rate control system, the homotopy map is defined in a Flow Pattern Configuration Space (FPCS). This tool permits to represent the mould filling in an alternative space representation developed with the optimal criterion [5][6][7]. Using this space, the homotopy map is defined as a mono-dimensional parametric function for whatever mould dimension. Some numerical examples are given at the end of the paper.

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Section: Figure 5 Skeleton (Left)optimal Inlet (Right)mentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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A topological approach for the flow behaviourcharacterization in LCM processes

2009

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A Comprehensive Review on Single- and Multi-Objective Optimization of Liquid Composite Moulding Process

Zade

Kuppusamy

2021

Lecture Notes in Mechanical Engineering

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Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning

Szarski

Chauhan

2022

J Intell Manuf

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Carbon fibre reinforced plastic (CFRP) manufacturing cycle time is a major driver of production rate and cost for aerospace manufacturers. In vacuum assisted resin transfer molding (VARTM) where liquid thermoset resin is infused into dry carbon reinforcement under vacuum pressure, the design of a resin distribution network to minimize fill time while ensuring the preform is completely full of resin is critical to achieving acceptable quality and cycle time. Complex resin distribution networks in aerospace composites increase the need for quick, optimized virtual design feedback. Framing the problem flow media placement in terms of reinforcement learning, we train a deep neural network agent using a 3D Finite Element based process model of resin flow in dry carbon preforms. Our agent learns to place flow media on thin laminates in order to avoid resin starvation and reduce total infusion time. Due to the knowledge the agent has gained during training on a variety of thin laminate geometries, when presented with a new thin laminate geometry it is able to propose a good flow media layout in less than a second. On a realistic aerospace part with a complex 12-dimensional flow media network, we demonstrate our method reduces fill time by 32% when compared to an expert designed placement, while maintaining the same fill quality.

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Optimising the flow pipe arrangement for resin infusion under flexible tooling

Cited by 20 publications

References 15 publications

A topological approach for the flow behaviourcharacterization in LCM processes

A topological approach for the flow behaviourcharacterization in LCM processes

A Comprehensive Review on Single- and Multi-Objective Optimization of Liquid Composite Moulding Process

Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning

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