Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit a self-excited vibration. In this paper, an artificial neural network (ANN)—a data learning model—is applied to model turning stability. The novel approach is to use a physics-based process model—the analytical stability limit—to generate a (synthetic) data set that trains the ANN. This enables the process physics to be combined with data learning in a hybrid approach. As anticipated, it is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well.

This study demonstrates the potential of different cinnamate esters, namely methyl trans‐cinnamate (MC), isobutyl cinnamate (IBC), allyl cinnamate (AC), and ethyl cinnamate (EC), as environmentally friendly plasticizers for polylactide (PLA). Plasticized PLA formulations with a constant nominal wt% of 20 of each plasticizer are compounded in a twin‐screw co‐rotating extruder and subsequently processed by injection molding. The results show exceptional plasticization efficiency of AC with an increase in elongation at break from 3.9% (neat PLA), up to 339.4% and a remarkable increase in impact strength, thus showing great potential to overcome the main drawback related to PLA intrinsic brittleness. The incorporation of cinnamates in a PLA matrix leads to a decrease in T_g from 61.7 °C down to 36.1 °C for the plasticized PLA formulation containing 20 wt% AC. Thermogravimetry analysis reveals that a slight plasticizer loss occurs during processing, as it happens with other monomeric plasticizers, but this phenomenon does not limit their plasticization efficiency. Therefore, the results obtained in this study confirm the suitability of a new family of organic compounds derived from the esterification of cinnamic acid as green plasticizers for PLA with improved toughness, with comparable, or even superior, ductile properties of typical plasticizers for PLA.

Bamboo fiber is one of the strongest natural fibers with high strength‐to‐weight and stiffness‐to‐weight ratios and can be used economically for manufacturing fiber‐reinforced composites. In this paper, bamboo fabric‐reinforced epoxy composite is manufactured and its tribological properties for load‐bearing applications are investigated. Sliding wear tests are conducted using a linear reciprocating tribometer and the effect of dry and lubricated contact conditions, applied load, sliding speed, temperature, and woven fabric direction on the coefficient of friction and wear rate are investigated. A scanning electron microscope is used to define the wear mechanisms at room and elevated temperatures. It is observed that the fabric orientation influences the mechanical and tribological performances of the composite material. Wear rate increases at higher loads and working temperatures; however, the effect of sliding speed is not remarkable, especially under lubricated contact conditions. The results present in this paper can be used for designing bamboo‐reinforced epoxy composites for load‐bearing applications, under different working conditions.