In situations requiring high levels of customization and limited production volumes, additive manufacturing (AM) is a frequently utilized technique with several benefits. To properly configure all the parameters required to produce final goods of the utmost quality, AM calls for qualified designers and experienced operators. This research demonstrates how, in this scenario, artificial intelligence (AI) could significantly enable designers and operators to enhance additive manufacturing. Thus, 48 papers have been selected from the comprehensive collection of research using a systematic literature review to assess the possibilities that AI may bring to AM. This review aims to better understand the current state of AI methodologies that can be applied to optimize AM technologies and the potential future developments and applications of AI algorithms in AM. Through a detailed discussion, it emerges that AI might increase the efficiency of the procedures associated with AM, from simulation optimization to in-process monitoring.

Augmented Reality (AR) is worldwide recognized as one of the leading technologies of the 21st century and one of the pillars of the new industrial revolution envisaged by the Industry 4.0 international program. Several papers describe, in detail, specific applications of Augmented Reality developed to test its potentiality in a variety of fields. However, there is a lack of sources detailing the current limits of this technology in the event of its introduction in a real working environment where everyday tasks could be carried out by operators using an AR-based approach. A literature analysis to detect AR strength and weakness has been carried out, and a set of case studies has been implemented by authors to find the limits of current AR technologies in industrial applications outside the laboratory-protected environment. The outcome of this paper is that, even though Augmented Reality is a well-consolidated computer graphic technique in research applications, several improvements both from a software and hardware point of view should be introduced before its introduction in industrial operations. The originality of this paper lies in the detection of guidelines to improve the Augmented Reality potentialities in factories and industries.

This study deals with a mixed static and dynamic optimization of four-parameter functionally graded material (FGM) doubly curved shells and panels. The two constituent functionally graded shell consists of ceramic and metal, and the volume fraction profile of each lamina varies through the thickness of the shell according to a generalized power-law distribution. The Generalized Differential Quadrature (GDQ) method is applied to determine the static and dynamic responses for various FGM shell and panel structures. The mechanical model is based on the so-called First-order Shear Deformation Theory (FSDT). Three different optimization schemes and methodologies are implemented. The Particle Swarm Optimization, Monte Carlo and Genetic Algorithm approaches have been applied to define the optimum volume fraction profile for optimizing the first natural frequency and the maximum static deflection of the considered shell structure. The optimization aim is in fact to reach the frequency and the static deflection targets defined by the designer of the structure: the complete four-dimensional search space is considered for the optimization process. The optimized material profile obtained with the three methodologies is presented as a result of the optimization problem solved for each shell or panel structure.

Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially when managed by civil agencies. In this work, we present a comprehensive methodology for optimizing YOLO-based object detection models for real-time Search and Rescue scenarios. A two-stage transfer learning strategy was employed using VisDrone for general aerial object detection and Heridal for Search and Rescue-specific fine-tuning. We explored various architectural modifications, including enhanced feature fusion (FPN, BiFPN, PB-FPN), additional detection heads (P2), and modules such as CBAM, Transformers, and deconvolution, analyzing their impact on performance and computational efficiency. The best-performing configuration (YOLOv5s-PBfpn-Deconv) achieved a mAP@50 of 0.802 on the Heridal dataset while maintaining real-time inference on embedded hardware (Jetson Nano). Further tests at different flight altitudes and explainability analyses using EigenCAM confirmed the robustness and interpretability of the model in real-world conditions. The proposed solution offers a viable framework for deploying lightweight, interpretable AI systems for UAV-based Search and Rescue operations managed by civil protection authorities. Limitations and future directions include the integration of multimodal sensors and adaptation to broader environmental conditions.

This paper presents a novel design methodology to be used in the evaluation of the main features of advanced unconventional airship configuration. Similar to the process used in aircraft design, the concept of volume fractions (VF) is introduced to estimate airship weights, dimensions, and performances, in an early design phase. The paper presents the complete methodology, with tables and constants to help unconventional airship designers with preliminary design considerations. Volumes and weights of candidate solutions are obtained through an iterative method within a user-friendly tool requiring graphical and straightforward mathematical operations. The solutions are ranked based on procedures aiming at satisfying customer needs and expectations provided as inputs. A case study highlighting a step-by-step methodology process is presented, and the approach followed to select the final solution is documented. The method is easy to use and implement, rapidly providing a significant amount of data. ...