With the increase use of plastics, there is currently a concern with the waste of materials, resulting in a series of challenges and opportunities for the waste management sector. In the present work, poly(ethylene terephthalate) (PET) foam was produced from recycled PET (RPET) from used water bottles. The recycled material was manually prepared and foamed in batches with the assistance of nitrogen gas as the physical blowing agent. RPET was characterized using Differential Scanning Calorimetry (DSC), Dynamic Mechanical Analysis (DMA), Fourier Transform Infrared Spectroscopy (FTIR) and Thermogravimetric Analysis (TGA). The influence of the pressure on the foam formation was studied and the results obtained showed that this variable influences the final product characteristics. To evaluate the behavior of the foams, their morphology, response to deformation when subject to compression and their thermal conductivities were studied. The morphology analysis showed that operating at higher-pressure results in bigger pore size but also in an increased pore size heterogeneous distribution, and foams that exhibit a higher thermal conductivity.
In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors’ knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns.
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