All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a vessel’s operation and safety and, consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This study presents the economic and technical benefits of predictive maintenance over traditional preventive maintenance and repair by replacement approaches in the maritime domain. By leveraging modern technology and artificial intelligence, we can analyze the operating conditions of machinery by obtaining measurements either from sensors permanently installed on the machinery or by utilizing portable measuring instruments. This facilitates the early identification of potential damage, thereby enabling efficient strategizing for future maintenance and repair endeavors. In this paper, we propose and develop a convolutional neural network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.

The integration of 3D printing with smart infrastructure presents a transformative opportunity in urban planning, construction, and engineering, enhancing efficiency, flexibility, and sustainability. By leveraging additive manufacturing alongside digitalization, artificial intelligence (AI), and the Internet of Things (IoT), this technology enables the creation of customized, lightweight, and sensor-embedded structures. This work analyzes both the advantages and challenges of applying 3D printing in smart infrastructure, focusing on material optimization, rapid prototyping, and automated fabrication, which significantly reduce construction time, labor costs, and material waste. Applications such as 3D-printed bridges, modular housing, and IoT-integrated urban furniture exhibit its potential in contributing towards resilient and resource-efficient cities. However, despite these benefits, significant challenges hinder large-scale adoption. Issues of scalability, particularly in the fabrication of large and load-bearing structures, remain unresolved, requiring advancements in high-speed printing techniques, material reinforcement strategies, and hybrid construction methods. Furthermore, regulatory uncertainties and the absence of standardized guidelines create barriers to implementation. The lack of comprehensive building codes, certification protocols, and quality assurance measures for 3D-printed structures limits their widespread acceptance in mainstream construction. Overcoming these limitations necessitates research into AI-driven process optimization, multi-material printing, and international standardization efforts. By assisting towards overcoming these challenges, 3D printing has the potential to redefine urban development, making infrastructure more adaptive, cost-effective, and environmentally sustainable. This work provides a critical evaluation of the current capabilities and limitations of 3D printing in smart infrastructure towards achieving full-scale implementation and regulatory compliance.

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.

SUMMARY: This paper presents a new solution approach for managing the motion of a fleet of autonomous vehicles (AVs) in indoor factory environments. AVs are requested to serve a number of workstations (WS) (following a specified desired production plan for materials requirements) while taking into account the safe movement (collisions avoidance) in the shop floor as well as time duration and energy resources. The proposed approach is based on the Bump-Surface concept to represent the 2D environment through a single mathematical entity. The solution of the combined problem of path planning and task scheduling is searched on a higher-dimension B-surface (in our case 3D) in such a way that its inverse image into the robot environment satisfies the given objectives and constraints. Then, a modified Genetic Algorithm (GA) is used to search for a near-optimum solution. The objective of the fleet coordination consists of determining the best feasible paths for the AVs so that all the WS are served at the lowest possible cost. The efficiency of the developed method is investigated and discussed through characteristic simulated experiments concerning a variety of operating environments.