In today's rapidly evolving industry, new challenges constantly arise that must be addressed quickly to keep up with the growing demand. Industry 5.0 aims to eliminate ob-stacles in production lines and integrate intelligent systems into manufacturing without excluding human operators from their daily tasks. In this context, developing software for controlling and managing data-driven evidence in the production cells becomes essential when considering performance, reliability, and practical aspects, as well as the financial returns it can bring to organizations. This work presents a brief overview of such an approach, proposing the development of a software architecture that manages industrial quality inspection in automotive parts using an Internet of Things publisher/subscriber model for controlling and synchronizing sub-processes. The results obtained demonstrate the benefits of implementing a control and evidence management system, offering production operators online monitoring of failures in manufactured parts and effective visualization of evidence of failures. This helps in decision-making and integrates the operator (user) with the system, which aligns with Industry 5.0 principles. This contribution is supported by creating a database of quality inspection evidence that can be used to train machine learning models to detect failures more accurately in the future.

Demand for ocean-based high-quality and sustainable fish protein soared in the last decade. Unlike precision agriculture, aquaculture remains an under-equipped farming activity. The aquaculture industry has provided remarkable contributions to the Sustainable Development Goal of zero hunger based on providing animal-based protein for human consumption worldwide. The success of the aquaculture industry hinges on appropriate monitoring of key water quality indicators to ensure both animal health and optimal productivity. In this context, the present work presents a cloud-based LoRaWAN system for quasi-real-time tracking of essential water quality parameters by integrating Internet of Things (IoT) sensor devices. The proposed approach harnesses the power of Long Range (LoRa) technology - especially the LoRa Wide Area Network (LoRaWAN) protocol - to facilitate efficient, large-scale monitoring focusing on data security and scalability. With practical insights drawn from IoT system deployment at an industrially relevant aquaculture farm in Brazil, this research provides a comprehensive look into the system's capabilities, drawbacks, and end-user feedback, offering a blueprint for future aquaculture innovations.

This paper describes a digital-twin-based approach to dimensional control designed to assist quality inspection of automotive air-conditioning rotors in the vehicle manufacturing industry. It focuses on dimensional quality control within the field of dimensional metrology, aiming to accurately measure the dimensions of parts, subassemblies, and complete equipment systems. The proposed approach has created a prototype, collecting measurement data from a test sample to generate validation results for error profiling. The main goal is to develop a method for improving the precision of sensors used for this type of quality inspection.

Fisheries and aquaculture industries notably contribute to animal-source protein production worldwide. Climate change is creating environmental conditions suitable for harmful algal blooms (HAB) on a global scale. Some phytoplankton species can also release toxins, which may cause large-scale marine mortality with knock-on effects on coastal economies. Reliable phytoplankton monitoring and early HAB detection are also essential in climate-resilient solutions for aquaculture applications. Currently, phytoplankton monitoring is primarily based on traditional microscopy. However, it is time-consuming and requires an experienced taxonomist. There is a need to expedite and automate phytoplankton monitoring to support aquaculture industries. Analytical instruments based on microscopy coupled with artificial intelligence (AI) models may be vital to monitoring applications. Digital plankton data sets are usually imbalanced and reflect natural environmental differences. The lack of data to represent minority species/genera prevents AI models from understanding some taxa completely. It compromises system reliability for HAB monitoring applications. The present study investigates state-of-the-art models for class imbalance problems tailored for HAB monitoring within multi-trophic aquaculture farms from Brazil, South Africa, and Scotland. A unified benchmark database covering publicly available microscopic image-based datasets supported phytoplankton modelling. AI deep collaborative models and threshold moving techniques provided the best results compared to standard architectures. It prevailed, especially for low-abundant yet toxic organisms.

The need for convergence between technology and shipbuilding processes is identified in the Brazilian shipbuilding industry so that it has a greater competitiveness against the shipyards belonging to the Tiger Cub Economies. In this perspective, wireless sensor networks and identification technologies have gained space regarding industrial solutions. This paper proposes an Internet of Things (IoT) based architecture using RFID and GPS for identification and tracking of blocks in shipyards. A hardware architecture is presented with the objective of identifying, mapping and tracking blocks and consequently the vehicles used in their transport, in a shipyard and offshore.

Welding is an important process in the industrial scenario, especially in the shipbuilding industry. This process is recognized by the laborious work and the hazardous work environment. The use of robots to automate the welding process can reduce the human interference and improve the productivity. This paper proposes a system for automated seam tracking based on passive monocular vision. The vision provides a data feedback to the automated robotic welding system allowing quality and productivity gains. A trajectory controller is developed to correct the robot's movement over the seam reference. The controller and a visual algorithm to find the seam reference in a real Gas Metal Arc Welding (GMAW) process are presented. The proposed system allows the automated seam tracking, the trajectory control of the welding torch, and a higher automation level in linear robotic welding. The capabilities of the method is evaluated using a commercial linear welding robot showing its viability.

This study intends to consider the operator skill and the environment conditions as variables of Intelligent Maintenance Systems (IMS). In this sense maintenance operations associated with the technical skills and environment factors through sensory perception can represent important strategy to improve the IMS. This approach considers the Cyber-Physical Systems (or CPS) paradigm to acquire human and context factors once that CPS system combines coordinates physical and computational elements. Thus this study proposes the CPS use for taking account the operator's skill and environment conditions for maintenance strategies. This proposal aims the developing of Advanced Human Computer Interface for Intelligent Maintenance Systems that consider the operator's skill and environment. The validation will be accomplished by interface named Toogle where two case studies will be tested.

Intelligent Maintenance Systems (IMS) are data acquisition/analysis systems for predictive maintenance. The IMS provide autonomously (or semi-autonomously) diagnoses, prognostics and health assessment of components. In these systems, factors such as data acquisition and visualization from equipments are very important. In this context, this study has used a Cyber-Physical Systems (or CPS) approach to consider the various aspects present into a maintenance environment. The CPS is a new paradigm that seeks to combine and coordinate physical and computational elements. In this paper, we propose a 2D/3D visualization tool for cyber-physical maintenance environments. This tool works under HTTP protocol and makes possible the real-time visualization and remotely access, allowing users to view, to edit and to access the maintenance data via web browser.

In this paper an automated platform for immersive multiprojection visualization of manufacturing processes is proposed. It admits scenarios with dynamic components and allows Virtual Reality collaborative visualization among geographically distributed users, through multi-CAVE devices. Modules for modeling, converting, visualizing and interacting composes the platform. The proposed system can be applied to CAD projects, models and simulations used in industry. The ideas discussed are then validated through the study of a real case related to the Shipbuilding and Offshore Industries.