Summary Very high mortality rates of coronavirus pandemic (COVID‐19) are observed around the world due to lack of medical equipment. The increased need for medical devices and personal protective equipment (PPE) has kept several healthcare professionals at risk. Fortunately, 3D printing technology allows to overcome the lack of medical supplies. This study highlights the impact of 3D printing on the combat against COVID19, and its importance in the medical product supply chain. Indeed, the existing medical equipment fabricated by 3D printing technology and its role in the management of Covid19 pandemic is presented. Moreover, the last works are examined to know whether the models of the medical equipment are free of use and whether useful informations are presented (eg, available design data and setup guidelines).

Abstract3D polymer-based printers have become easily accessible to the public. Usually, the technology used by these 3D printers is Fused Deposition Modelling (FDM). The majority of these 3D printers mainly use acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) to fabricate 3D objects. In order for the printed parts to be useful for specific applications, the mechanical properties of the printed parts must be known. The aim of this study is to determine the tensile strength and elastic modulus of printed materials in polylactic acid (PLA) according to three important printing parameters such as deposition angle, extruder temperature and printing speed. The central composite design (CCD) was used to reduce the number of tensile test experiments. The obtained results show that the mechanical properties of printed parts depend on printing parameters. Empirical models relating response and process parameters are developed. The analysis of variance (ANOVA) was used to test the validity of models relating response and printing parameters. The optimal printing parameters are determined for the desired mechanical properties.

A B S T R A C TFiber reinforced composites are increasingly used in several fields such as aeronautics and civil engineering due to their increased strength, durability, corrosion resistance, resistance to fatigue and damage tolerance characteristics. The embedding of sensor networks into such composite structures can be achieved. In the present study, glass fiber reinforced Epoxy composite with integrated strain gage was analysed. Firstly, the mechanical behaviour of this material with embedded strain gage is investigated. The as-prepared samples have been tested under tensile and flexural loading in order to study the effects of the strain gage embedding on the structural stiffness and strength of the composite. It was found that the tensile stiffness decreases by 5.8% and the tensile strength decrease by 1.5% when the strain gage embedded in the material. On the other hand, the flexural strength and stiffness is increased, respectively, by 1.5% and 5.5% with an embedded strain gage. The experiments showed that embedded strain gage is functional and demonstrated the successful integration of sensor networks into composite parts. The obtained results confirm that integrated strain gage can be used for the Structural Health Monitoring (SHM) of glass fiber reinforced Epoxy composite.

A series of compacted exfoliated vermiculite samples were prepared, and their mechanical behaviour was experimentally studied. The vermiculite was first exfoliated and after compacted in order to obtain a material with good thermal and mechanical properties. The as-prepared samples have been tested under compressive loading. Some parameters effect was studied, as the porosity and the type of the compacted exfoliated vermiculite. The samples of this porous media display two steps for the stress-strain behaviour under uniaxial compressive loading, that is initial nonlinear deformation, strain-hardening 'pseudo-platform' stage.

The preservation of the environment has become a priority and a subject that is receiving more and more attention. This is particularly important in the field of precision agriculture, where pesticide and herbicide use has become more controlled. In this study, we propose to evaluate the ability of the deep learning (DL) and convolutional neural network (CNNs) technology to detect weeds in several types of crops using a perspective and proximity images to enable localized and ultra-localized herbicide spraying in the region of Beni Mellal in Morocco. We studied the detection of weeds through six recent CNN known for their speed and precision, namely, VGGNet (16 and 19), GoogLeNet (Inception V3 and V4) and MobileNet (V1 and V2). The first experiment was performed with the CNNs architectures from scratch and the second experiment with their pre-trained versions. The results showed that Inception V4 achieved the highest precision with a rate of 99.41% and 99.51% on the mixed image sets and for its version from scratch and its pre-trained version respectively, and that MobileNet V2 was the fastest and lightest with its size of 14 MB.