Several natural materials and vegetable waste have relevant mechanical properties, mainly in its fiber format. Particularly, banana fiber (BF) provides a close behavior to the widely spread glass fibers, which places it in an advantageous position for use as a reinforcing material in plastic composites. This work characterizes the behavior of acrylonitrile butadiene styrene (ABS), high impact polystyrene (HIPS), and high density polyethylene (HDPE) reinforced with short fibers of bananas from the Canary Islands for its application in molding processes. Several thermal analyses (Thermal Gravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), and Melt Flow Index (MFI)) and mechanical tests (tensile, flexural, impact, and Dynamic Mechanical Analysis (DMA)) were carried out in composites with different percentages of banana fiber. The thermal results show that the use of banana fiber is viable as a reinforcement in composites for injection molding processes and the mechanical tests indicate an increase in stiffness and an improvement in maximum flexural stress by increasing the fiber content in composites, so the banana fiber turns out to be a natural alternative for the reinforcement of injected plastic components.

This paper presents an improved monitoring system for the failure detection of engraving tool steel inserts during the injection molding cycle. This system uses acoustic emission PZT sensors mounted through acoustic waveguides on the engraving insert. We were thus able to clearly distinguish the defect through measured AE signals. Two engraving tool steel inserts were tested during the production of standard test specimens, each under the same processing conditions. By closely comparing the captured AE signals on both engraving inserts during the filling and packing stages, we were able to detect the presence of macro-cracks on one engraving insert. Gabor wavelet analysis was used for closer examination of the captured AE signals’ peak amplitudes during the filling and packing stages. The obtained results revealed that such a system could be used successfully as an improved tool for monitoring the integrity of an injection molding process.

This paper presents measurements of acoustic emission (AE) signals during the injection molding of polypropylene with new and damaged mold. The damaged injection mold has cracks induced by laser surface heat treatment. Standard test specimens were injection molded, commonly used for examining the shrinkage behavior of various thermoplastic materials. The measured AE burst signals during injection molding cycle are presented. For injection molding tool integrity prediction, different AE burst signals’ descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented to define a feature subset in an appropriate multidimensional space to characterize the integrity of the injection molding tool and the injection molding process steps. The feature subset was used for neural network pattern recognition of AE signals during the full time of the injection molding cycle. The results confirm that acoustic emission measurement during injection molding of polymer materials is a promising technique for characterizing the integrity of molds with respect to damage, even with resonant sensors.