This paper presents an AI-assisted framework designed to monitor and enhance manual assembly processes in industrial environments, in line with Industry 4.0 principles. The system integrates an OAK-D Pro PoE RGB camera and the YOLOv11 object detection model to identify components, track assembly phases, detect errors, and calculate Key Performance Indicators (KPIs) in real time. A Human-Machine Interface (HMI) displays ongoing metrics such as total assembly time, cycle time, phase durations, and error counts, offering immediate feedback to operators. A synthetic dataset of 45,000 annotated images across six object classes was generated in Unity to train the object detection model, allowing adaptation to various assembly tasks.The system was evaluated through experiments with 10 participants performing 10 complete assembly cycles each, totaling 100 runs. The average cycle time was 64.21 seconds (±24.41), with durations ranging from 20.80 to 142.14 seconds. Errors led to a 20.6% increase in cycle time (73.34s with errors vs. 58.37s without, p = 0.0034). A 6.3% improvement in cycle time was observed from the first to last five runs (66.29s to 62.13s), though not statistically significant (p = 0.3975). The bolting phase was the most time-consuming, occupying 47% of the total cycle time and exhibiting the highest variability (StdDev = 16.14s).These findings support the use of AI and real-time feedback to improve quality, efficiency, and learning in manual assembly workflows.

The Digital Twin approach has increased in interest in recent years. Without well defined specifications, it is common for different researchers to use different approaches. Digital Twin concept emerged to support Industry 4.0, so it is of utmost importance to specify a best methodology, and tools, for its implementation for industrial use cases. This work proposes a Digital Twin architecture that follows manufacturing-centric standards of an industrial prototype testing station, for a new car infotainment system. Testing is performed by low-cost Software-Defined Radio equipment that aims to replace expensive metrological equipment. Moreover, an environment sensing device is also used to monitor the physical environment around prototype. We present the development of a solution that allows manage hardware processing, real-time monitoring of machine states in the virtual environment, and control of the overall system through a logical sequence supported by its Digital Twin. With the implementation of a standard like ISO23247, helps in the identified problem of having various Digital Twins and improves an interaction with test equipment. All steps made for the development of our architecture approach, as well as some results, are shown and explained for our use case.

The Radio Frequency Identification (RFID) technology has been used mainly to manage products and have stock control in real time. This technology is commonly used in the form of tags that are positioned outside of the object. However, the RFID insertion strategies are still sub-optimal, thus, there has been attempted to create methods to insert labels during the plastic injection process. However, must of the available insertion strategies do not satisfy the needs of large companies, since they are not standard. So, the objective of this study is to present a proof of concept of a new RFID tags insertion strategy adapted to plastic parts. The system uses a robot to pick up an RFID chip and insert it into a cavity of a mold. Then it will take some of the same material and with an ultrasound welder the plastic material will be melted to close the structure.For this project, we started by carrying out some experiments to understand the limitations of the available RFID chips, such as: maximum distance that can be detected, maximum temperature without damage when subjected to a welding process. With these experiments we will validate our proof of concept. Through these experiments we conclude that chips are detected at greater distances if they are centered with the reader’s antenna. Moreover, it was possible to confirm that they supported high temperatures.Overall, the current results corroborate the potential of this technique for the insertion of RFID in standard processes of the plastic industry.

Industry 4.0 is one of the biggest industrial revolutions with the creation of new technology and artificial intelligence solutions. In this context, Autonomous Mobile Robot (AMR) is one of the reasons to expand productivity and logistical flexibility due to its adaptability to new environments. However, only recently the AMR has had greater acceptance, much due to its complexity and cost. In this sense, this article presents a ROS-based mobile platform to evaluate the possibility of navigation only with monocular cameras. The hardware and software stack structure will be detailed evidencing the most critical components for the success of the implementation of omnidirectional AMR. To ensure a reduced cost while maintaining the advanced ability to detect and change the trajectory whenever an object is detected during its path, it is proposed to use an artificial intelligence system to create an estimated depth image from the 2D RGB images. Finally, to ensure its ability to generalize and navigate, the system accuracy and behavior were evaluated and validated in several environments. The main contribution of this work is the assessment and validation of an AMR navigation solution using only Ai-based depth estimation from 2D RGB cameras without performance impact in an embedded computational system.

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.