Blending of biodegradable polymers in combination with low-price organic fillers has proven to be a suitable approach to produce cost-effective composites in order to address pollution issues and develop products with superior mechanical properties. In the present research work PBAT/PHB/Babassu composites with 25, 50, and 75% of each polymer and 20% of Babassu were produced by melting extrusion. Their thermal, mechanical, and morphological behavior was investigated by differential scanning calorimetry (DSC), tensile testing, and scanning electron microscopy (SEM). Blending PBAT with PHB inhibited the crystallization of both polymers whereas adding Babassu did not significantly change their melting behaviour. Incorporation of Babassu reduced the tensile strength of its respective blends between 4.8 and 32.3%, and elongation at break between 26.0 and 66.3%. PBAT as highly ductile and low crystalline polymer may be seen as a crystallization tool control for PHB as well as a plasticizer to PBAT/PHB blends and PBAT/PHB/Babassu composites. As PBAT content increases: (i) elongation at break increases and (ii) surface fracture becomes more refined indicating the presence of more energy dissipation mechanisms. As PBAT/PHB/Babassu composites are biodegradable, environmental friendly, and cost effective, products based on these compounds have a great potential since their mechanical properties such as ductility, stiffness, and tensile strength are still suitable for several applications even at lower temperatures (−40 °C).

Toughened epoxy adhesives are widely used for structural applications, especially in the design of lightweight products. During their service life, bonded joints are exposed to cyclic loading under a broad range of temperatures. In this study the influence of temperature on the fatigue behaviour of a toughened epoxy adhesive was investigated by experimental determination of SN curves under tension–tension cyclic loading. Bulk adhesive specimens and single lap, thick adherend and scarf joints were tested at a stress ratio of R = 0.1 and a frequency of f = 7 Hz at five different temperatures (−35°C, −10°C, RT, +50°C, +80°C). Dynamic mechanical analysis (DMA) was conducted to evaluate the temperatures of the phase transitions in the adhesive. Based on the experimental findings an interpolation method was proposed for estimating SN parameters at temperatures unaffected by phase transitions. The fatigue tests showed there was a reduction in fatigue lifetime with increasing temperature. DMA results indicated the adhesive underwent two phase transitions. The results from interpolations for different types of samples showed excellent agreement with the experimental findings, confirming the validity of the method, providing no phase transitions are within or close to the interpolation range.

Since fatigue investigations are expensive and time consuming, models capable of predicting lifetime by leveraging existing experimental data are desirable. Here, this task is accomplished by combining machine learning (ML) and finite element analysis (FEA). The dataset contains 365 points comprising four adhesives with four different joint types. The model is fed with four input parameters: stress ratio and stress amplitude (functions of the applied load), and stress concentration factor and multiaxiality, which are obtained from FEA. An extremely randomized trees (ERT) algorithm, capable of dealing with small and noisy datasets, is used to design the model. After calibration, the model's performance was assessed on unseen data and compared with a linear regression model. The ERT predictions yield a significantly smaller error factor (ER) of 2.13 than that of the linear model (ER = 5.89). A relevance analysis shows that at least one FEA‐based parameter must be fed into the model.