Unlocking the first few seconds of the Injection Moulding Cycle using operando X-ray scattering Measurements

Injection moulding is the most common technology employed for shaping polymers. Despite its long history dating back to 1872, it remains a remarkably under instrumented technology. The vast majority of quality control and scientific study takes place on products postproduction. There is a substantial body of studies using simulation, but again there is little data to validate the predicts of simulations. In this work, a recent project performed at CDRSP in partnership with the ALBA Synchrotron Light Source focused on developing an industrially relevant operando X-ray scattering during injection moulding capability is presented. The challenges in such a project are identified and the design of the equipment to meet those challenges are described. In fact, the powerful integration of this equipment with the NCD-SWEET beamline, the SAXS/WAXS beamline at ALBA, enabled the capture of time-resolved structural information during dynamic processes. The advantages of these operando measurements are identified, in which the different processes are separated in time, rather than consolidated into a single average data set available from post-product analysis. The operando system is equipped with pressure and temperature sensors, and we correlate these measurements with the data available from X-ray scattering, which also includes a transmission monitor that enables the thickness of material in the mould cavity to be evaluates as a function of time. We contrast the transformation from the melt to the solid state for both synthetic and biobased polymers. The scope for dynamic measurements during the moulding cycle is explored and future planned work for detecting moulding defects such as weld lines will be critically discussed. The role of this equipment in enhancing sustainability will be underlined.

This work was supported by the Fundação para a Ciência e Tecnologia (Portugal) through the Project references: https://doi.org/10.54499/UIDB/04044/2023 and MIT-EXPL/TDI/0044/2021 and PRR INOVAM C644865234-00000004.