Additive manufacturing (AM) has emerged as a commonly utilized technique in the manufacturing process of a wide range of materials. Recent advances in AM technology provide precise control over processing parameters, enabling the creation of complex geometries and enhancing the quality of the final product. Moreover, Machine Learning (ML) has become widely used to make systems work better by using materials and processes more intelligently and controlling their resulting properties. In industrial settings, implementing ML not only reduces the lead time of manufacturing processes but also enhances the quality and properties of produced parts through optimization of process parameters. Also, ML techniques have facilitated the advancement of cyber manufacturing in AM systems, thereby revolutionizing Industry 4.0. The current review explores the application of ML techniques across different aspects of AM including material and technology selection, optimization and control of process parameters, defect detection, and evaluation of properties results in the printed objects, as well as integration with Industry 4.0 paradigms. The progressive phases of utilizing ML in the context of AM, including data gathering, data preparation, feature engineering, model selection, training, and validation, have been discussed. Finally, certain challenges associated with the use of ML in the AM and some of the best-practice solutions have been presented.

In this study some new concepts regarding certain aspects related to shape memory polymers are presented. A blend of polylactic acid (PLA) (80%) and polybutylene succinate (PBS) (20%) was prepared first by extrusion, then by injection molding to obtain the samples. Tensile, stress-relaxation and recovery tests were performed on these samples at 70 °C. The results indicated that the blend can only regain 24% of its initial shape. It was shown that, this partial shape memory effect could be improved by successive cycles of shape memory tests. After a fourth cycle, the blend is able to regain 82% of its shape. These original results indicated that a polymer without (or with partial) shape memory effect may be transformed into a shape memory polymer without any chemical modification. In this work, we have also shown the relationship between shape memory and property memory effect. Mono and multi-frequency DMA (dynamic mechanical analyzer) tests on virgin and 100% recovered samples of polyurethane (PU) revealed that the polymer at the end of the shape memory tests regains 100% of its initial form without regaining some of its physical properties like glass transition temperature, tensile modulus, heat expansion coefficient and free volume fraction. Shape memory (with and without stress-relaxation) tests were performed on the samples in order to show the role of residual stresses during recovery tests. On the basis of the results we have tried to show the origin of the driving force responsible for shape memory effect.

Hydrogen-powered vehicles are set to become a viable alternative for many of the cars currently on the roads. However, even if hydrogen offers a promising eco-friendly solution for the energy transition, several issues related to its storage and delivery need to be resolved in order to predict its wide use in both stationary and automotive applications. Hydrogen has the lowest volumetric energy density of all commonly used fuels (0.01079 MJ/L at atmospheric pressure). However, compression emerges as a direct and effective solution to this issue, with high pressures capable of significantly enhancing hydrogen's energy density, thereby augmenting its practicality. The energy densities achievable under high pressure are indeed impressive, making hydrogen highly practical. In mobile applications, hydrogen is typically stored as a gas in high-pressure composite overwrapped pressure vessels (COPVs). To achieve optimal functionality for high-pressure applications, two fundamental objectives must be met: ensuring exceptional structural integrity and maximizing gas impermeability. The commercialization of these vessels therefore presents a range of engineering challenges, including the development of advanced manufacturing techniques, the enhancement of structural properties, and the selection of appropriate materials, among others. The trend towards high-pressure hydrogen storage tanks is characterized by low cost, lightweight, and favorable safety performance. Consequently, the development of an efficient, sustainable, and safe high-pressure hydrogen storage method is a crucial focus of recent research, aiming to optimize hydrogen's utility in various applications. This review summarizes the latest developments in the most established hydrogen compression technologies.