Thin-Film Sensors Integrated Directly Onto Cavity Surface

The Fraunhofer Institute for Surface Engineering and Thin Films IST will introduce a multifunctional thin-film sensor system on an exchangeable mold insert at the Hannover Messe 2024. The specially adapted sensor design has 13 measuring points and enables spatially determined measurement of the entire flow front. The thermoresistive sensors are distributed to capture full component geometry.

Fraunhofer told Plastics Technology that the thin-film sensor is coated onto the tool surface in vacuum processes, enabling the technology to coat tools or inserts of varying geometries. The vacuum processes — which include physical vapor deposition or plasma-enhanced chemical vapor deposition processes, depending on the layer — enable strong adhesion to the base material.

The Fraunhofer spokesperson said that in this application example, the sensor is measuring the melt temperature and the movement of the flow front inside the cavity. In applications with constant temperatures, the thin-film layer system can also be used for pressure measurement.

The researchers believe the main benefit of this technology is the fact that the sensors are placed right on the surface of the tool, enabling them to measure in direct contact with the melt. “There is only a wear-protecting layer 2 to 3 µm thick between the sensor and the melt,” the spokesperson noted. “The measurement results are therefore of the highest quality.”

Fraunhofer says the wear-resistant thin-film system can withstand much higher pressures than those occurring in injection molding, ranging up to 1 GPa or gigapascal, equivalent to 145,038 psi. The sensor can withstand temperatures of up to 300°C (572°F), but the researcher noted Fraunhofer has created thin-film systems for aluminum die-casting or warm forming processes, where temperatures are much higher.

The measurement data obtained is processed and transmitted in real time by an electronic unit specially adapted to the sensor. This enables faults and potential weak points to be detected immediately so adjustments can be implemented quickly. Machine-learning algorithms implemented on an edge device also enable the molded part’s quality to be reliably determined. The results of the data analysis are output as a color signal on the system that transmits before the mold opens after the injection process.