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.

Digital twins are an important technology for advancing mobile communications, specially in use cases that require simultaneously simulating the wireless channel, 3-D scenes and machine learning (ML). Aiming at contributing towards a solution to this demand, this work describes a modular co-simulation methodology called CAVIAR, for implementing the virtual counterpart of a digital twin (DT) system. Here, CAVIAR is upgraded to support a message passing library and facilitate using different 6G-related simulators. The main contributions of this work are the detailed description of different CAVIAR architectures, the implementation of this methodology to assess a 6G use case of unmanned aerial vehicle (UAV)-based search and rescue (SAR), and the generation of benchmarking data about the computational resource usage. For executing the SAR co-simulation we adopt five open-source solutions: 1) the physical and link level network simulator Sionna; 2) the simulator for autonomous vehicles AirSim; 3) scikit-learn for training a decision tree for multiple input–multiple output (MIMO) beam selection; 4) Yolov8 for the detection of rescue targets; and 5) neural autonomic transport system (NATS) for message passing. Results for the implemented SAR use case suggest that the methodology can run in a single machine, with the main demanded resources being the CPU processing and the GPU memory.

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.

Flexibility and speed in the development of new industrial machines are essential factors for the success of capital goods industries. When assembling a printed circuit board (PCB), since all the components are surface mounted devices (SMD), the whole process is automatic. However, in many PCBs, it is necessary to place components that are not SMDs, called pin through hole components (PTH), having to be inserted manually, which leads to delays in the production line. This work proposes and validates a prototype work cell based on a collaborative robot and vision systems whose objective is to insert these components in a completely autonomous or semi-autonomous way. Different tests were made to validate this work cell, showing the correct implementation and the possibility of replacing the human worker on this PCB assembly task.

Shared autonomous vehicles (SAV) numbers are going to increase over the next years. The absence of human driver will create a new paradigm for in-car safety. This paper addresses the problem, presenting a monitoring system capable of estimating the state of the car interior, namely the presence of damage, dirt and stains. We propose the use of Semantic Segmentation methods to perform appropriate pixel-wise classification of certain textures found in the car's cabin as defect classes. Two methods, U-Net and DeepLabV3+, were trained and tested for different hiper-parameter and ablation scenarios, using RGB images. To be able to test and validate these approaches an In-car dataset was created, comprised by 1861 samples from 78 cars, and than splitted in 1303 train, 186 validation and 372 test RGB images. DeepLabV3+ showed promissing results, achieving an average accuracy for good, damage, stain and dirt of 77.17%, 58.60%, 65.81% and 68.82%, respectively.