Embedding a surface acoustic wave sensor and venting into a metal additively manufactured injection mould tool for targeted temperature monitoring

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) "stop-and-go" approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

The Microcellular Structure of Injection Molded Thick-Walled Parts as Observed by In-Line Monitoring

The aim of the study was to detect the influence of nitrogen pressure on the rheological properties and structure of PA66 GF30 thick-walled parts, produced by means of microcellular injection molding (MIM), using the MuCell^® technology. The process was monitored in-line with pressure and temperature sensors assembled in the original injection mold. The measured data was subsequently used to evaluate rheological properties inside an 8.4 mm depth mold cavity. The analysis of the microcellular structure was related to the monitored in-line pressure and temperature changes during the injection process cycle. A four-times reduction of the maximum filling pressure in the mold cavity for MIM was found. At the same time, the holding pressure was taken over by expanding cells. The gradient effect of the cells distribution and the fiber arrangement in the flow direction were observed. A slight influence of nitrogen pressure on the cells size was found. Cells with a diameter lower than 20 µm dominate in the analyzed cases. An effect of reduction of the average cells size in the function of distance to the gate was observed. The creation of structure gradient and changes of cells dimensions were evaluated by SEM images and confirmed with the micro CT analysis.

In-Mold Sensors for Injection Molding: On the Way to Industry 4.0

The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the plastic part is formed. Manufacturing process data in the mold cavity can be obtained with the help of sensors. Although many sensors are available nowadays, those appropriate for in-mold measurements have certain peculiarities. This study presents a comprehensive overview of in-mold process monitoring tools and methods for injection molding process control. It aims to survey the recent development of standard sensors used in the industry for the measurement of in-mold process parameters, as well as research attempts to develop unique solutions for solving certain research and industrial problems of injection molding process monitoring. This review covers the established process monitoring techniques—direct temperature and pressure measurement with standard sensors and with the newly developed sensors, as well as techniques for the measurement of indirect process parameters, such as viscosity, warpage or shrinkage.

Validation of an In-Mold Multivariate Sensor for Measurement of Melt Temperature, Pressure, Velocity, and Viscosity

A multivariate sensor (MVS) is described for measurement of melt temperature, melt pressure, melt velocity, and melt viscosity. Melt pressure and temperature are respectively obtained through the incorporation of a piezo-ceramic element and infrared thermopile. Melt velocity is derived from the initial response of the melt temperature as the polymer melt flows across the sensor lens. The apparent melt viscosity is then derived based on the melt velocity and the time derivative of the increasing melt pressure. The response of the MVS is analyzed using an instrumented mold including piezoelectric pressure sensors, an infrared pyrometer, and thermocouples. A 12-run, blocked half-fractional design of experiments (DOE) was run to characterize the effect of melt temperature, mold temperature, packing pressure, and ram velocity. The results show that the MVS provides excellent measurement of melt temperature and pressure. The accuracy of the melt velocity estimations depended on the ram velocity set-point, yielding a coefficient of determination of 0.91 for the lower ram velocities, and reaching saturation for melt velocities about 450 mm/s. The apparent melt viscosity estimated by the MVS are close to those predicted by the Cross-WLF model, exhibiting appropriate shear thinning but behavior but inconsistent temperature dependence.

Monitoring of the injection and holding phases by using a modular injection mold

Introducing pressure and temperature sensors into the mold cavity allows us to determine the rheological properties of the polymeric materials being processed. Furthermore, signals registered by sensors inside the mold cavity allow us to monitor the injection and holding phases as well as the subsequent solidification of the polymer melt. The aim of this research work was to verify the operational abilities of a laboratory injection mold with pressure and temperature sensors and was designed for research purposes. Standardized test pieces to investigate the mechanical properties of polymeric materials were obtained by using a laboratory injection mold. This article presents the investigation results of the influence of microcellular foaming of polyamide on the conditions inside an injection mold cavity. Apparent viscosity fluctuations of polymer melt in subsequent processing cycles were investigated as well. Additionally, the effect of changes in the injection speed of the polypropylene melt on its rheological parameters were investigated.

Estimation of Bulk Melt-Temperature from In-Mold Thermal Sensors for Injection Molding, Part A: Method

To improve part quality and consistency injection molders strive to control the temperature of the molten plastic during cavity filling. In-mold temperature sensors can effectively measure the temperature of the mold surface that contacts the melt; however, they do not provide a measure of the bulk melt temperature. An analysis was developed to use data from in-mold thermocouples to predict the plastic's bulk melt temperature. This analysis integrates the heat flux through the mold steel to calculate the bulk melt temperature. A mold instrumented with an in-mold thermocouple and an IR temperature sensor was used to validate the predictions. The effects of changing barrel temperature, coolant temperature, injection velocity, and cooling time were studied using a 21 run DOE. Validation was performed by comparing bulk temperature data from the IR temperature sensor with temperature predictions derived from the in-mold temperature sensor. Trends in the melt temperature were consistently predicted, however, the magnitudes were low due to residual heat remaining in the part at the end of the molding cycle. The analysis was more sensitive to process changes than raw data from the in-mold temperature sensor, thereby improving process observability.