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intelligent REactive polymer composites MOulding

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Optimising plastics’ production process

Composite materials made of reinforced plastics are the cornerstone of innumerable components, from car doors to bridge beams. Automated process control has decreased production time while enhancing quality.

Industrial Technologies

Resin transfer moulding (RTM) and vacuum infusion are two liquid polymer composite processing techniques for producing reinforced plastics. Solid reinforcing materials are placed into a mould wherein liquid resin is injected or infused. Following this, the entire system is heated and cured and then the mould and resulting piece are removed. Despite the superiority of parts achievable compared to other moulding techniques, control of the production cycle is complicated. As a result, the techniques are not widely used by manufacturers. Scientists supported in part by EU funding of the project 'Intelligent reactive polymer composites moulding' (IREMO) developed automated process monitoring and control solutions for liquid moulding of composite materials. The process monitoring system monitors all major types of reactive polymers with consistently high accuracy. In situ process control is achieved by adjusting process variables (output) according to the sensed input signals via a self-learning procedure. Wireless communications minimise wiring on the shop floor, and the IREMO system boasts a user-friendly interface. IREMO's system enabled the appropriate impregnation of fibres with resin and the optimal curing of composite parts with a reduction in cycle time of more than 25 %. Technology was retrofit on a two-component injection machine, enabling automatic sensing of material properties and control of temperature. The monitoring and control system was also integrated into a vacuum infusion process and a light RTM process with success. Commercialisation is expected to save time, effort and money on the part of the manufacturing community while enhancing product quality. Workplace safety also stands to benefit from automated control that requires far less intervention and contact with potentially irritating materials or heavy machinery. Integrated fast and wireless sensors combined with self-learning neural networks and self-calibrating process control should make the previously intractable manufacturing process a piece of cake.

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