The Industry 4.0 paradigm introduced several technologies in the industrial manufacturing landscape, highlighting the Cyber Physical Systems, Internet of Things and Visual Computing as some of the key enabling technologies. The fast paced evolution of the technology has to be reflected in the common ground between the machines and the operators. The introduction of advanced, flexible and adaptable Human Machine Interfaces (HMI) will empower the operators with information and custom tools to be more productive in a safer way. This work presents totally functional hardware and a novel software architecture to build flexible advanced HMI interfaces that will provide adaptable and useful information to the operator of the machines. Industrial protocols are used to receive real-time data. A USB camera can be plugged to enable the utilisation of Computer Vision algorithms. A touch display shows adaptable interfaces composed of (a) 3D interactive graphics, (b) texts and 2D images, (c) the camera feed and (d) user interface controls to enable the interaction with the operators. The results present three use cases: (a) an HMI customised for 3D printers, (b) real-time motor control from the HMI and (c) a digital twin of a robotic arm receiving real-time data from the actual robotic arm. This work present data regarding consumption of the hardware for each use case under different work-load.

Personalized production is moving the progress of industrial automation forward, and demanding new tools for improving the decision-making of the operators. This paper presents a new, projection-based augmented reality system for assisting operators during electronic component assembly processes. The paper describes both the hardware and software solutions, and depicts the results obtained during a usability test with the new system.

This work introduces the design and assessment of a voice-controlled elevator system aimed at facilitating touchless interaction between users and hardware, thereby minimising contact and improving accessibility for individuals with disabilities. The research distinguishes three distinct deployment scenarios – on cloud, on edge and embedded – with the ultimate goal of integrating the entire system into a low-resource environment on a custom carrier board. An objective evaluation measured acoustic conditions rigorously using a dataset of 2900 audio files recorded inside a laboratory elevator cabin featuring two internal coatings, five audio input devices, and under four distinct noise conditions. The study evaluated the performance of two Automatic Speech Recognition systems: Google's Speech-to-Text API and a Kaldi model adapted for this task, deployed using Vosk. Additionally, latency times for these transcribers and two communication protocols were measured to enhance efficiency. Finally, two subjective evaluations on clean and noisy conditions were conducted simulating a real world scenario. The results, yielding 84.7 and 77.2 points respectively in a System Usability Scale questionnaire, affirm the reliability of the presented prototype for industrial deployment.

In the recent years, due to the technological advancements brought by Industry 4.0, flexible in-line quality control processes that analyze 100% of produced parts targeting Zero Defect Manufacturing have become a reality. The electronics industry in general and the manufacturing of PCBAs in particular have also adopted this paradigm, where quality inspection applies especially to the most critical and error-prone feature: the solder joints between PCBs and components. When the literature related to AI-based solder joint quality inspection is analyzed, we find a lack of applications in real production lines, since most of them are limited to lab scenarios. Therefore, we present a deep learning-based method for solder joint defect detection, which is deployed in a real manufacturing line. The mentioned system is trained using the database we captured using an ad hoc robotic system with onboard front and lateral cameras designed also in this work, which we also describe in detail. The deployment of the defect detector in the real production line is done using that same robotic system too, ensuring the coherence and compatibility between the training data and the real inference scenario.

Purpose: The purpose of this study is to explore a methodology for connecting microelectromechanical system sensors – i.e. inertial measurement unit (IMU) – to an Arduino-based microcontroller, using graphene-based conductive filament and flexible thermoplastic polyurethane (FTPU) filament and low-cost dual material extrusion technology. Design/methodology/approach: A series of electrical tests were carried out to determine the maximum resistance the conductive paths may take to connect printed circuit boards (PCB). To select the most suitable printing material, three types of conductive filaments were examined. Then an experiment was carried out to find the best printing parameters in terms of printing speed, printing temperature and layer height to minimise resistivity. The size of the conductive path was also analysed. A final prototype was designed and printed according to optimised printing settings and maximum allowable resistances for each line and considering different geometries and printing strategies to reduce cross-contamination among paths. Findings: For the Black Magic 3D conductive filament, the printing speed and layer height played a significant role regarding resistivity, while the printing temperature was not very important. The infill pattern of the conductive paths had to be aligned with the expected current path, while using air gaps between two adjacent paths resulted in the best approach to reducing cross-contamination. Moreover, the cross-section size of the conductive path did not affect the volume resistivity. When combined with FTPU filament constraints, the prototype yielded suitable electrical performance and printing quality when printed at a temperature of 220°C, speed of 20 mm/s and layer height of 0.2 mm. Originality/value: This paper explores a systematic methodology for the additive manufacturing of commercial conductive material using low-cost extrusion technology to connect complex electronics when data transmission is a key feature.

In this work, a vision system oriented to the quality inspection of solder joints in electronic boards is presented. The proposed vision system is composed of two cameras (one frontal and one lateral), to achieve a general view of the joints; a light source, to ensure good and robust lighting conditions; and a mobile system (i.e. 3-axis cartesian robot), to automatically move to each image capture position and get the optimal focus. Moreover, a classifier based on Artificial Intelligence is fed with the captured images to perform an automatic inspection of the soldering joints. The output for each one of them is the belonging to either a correct or incorrect joint group. The tests carried out with real samples show the validity of the proposed system for its future deployment in the factory.