The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC’s weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB’s ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs’ ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing.

Automated Optical Inspection (AOI), based on machine vision, has been widely used in the industry to perform quality control in various manufacturing segments. In addition to electrical testing, optical analysis is an important step for quality control in Printed Circuit Board (PCB) manufacturing. Two approaches are reported in the literature for the inspection of printed circuit boards: the referential and the non-referential approach. Recently the DeepPCB database, composed of PCB images for identification and classification of defects, was published. This work assess the application of transfer learning strategies to detect defective PCBs in a non-referential method. Since the DeepPCB dataset is too small to train a deep model from scratch, we evaluated some strategies of transfer learning from pre-trained models VGG16 and ResNet50. Given that the PCB images from DeepPCB does not present the same complexity of the ImageNet dataset, feature extraction from a less deep model as VGG16 presented better results. The best-evaluated model obtained an accuracy of 89%.

Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios.

Monitoring rotating machines is essential for evaluating operations, preventing failures, and improving efficiency. Traditional rotational measurement methods, like magnetic induction or optical sensors, typically require dedicated hardware for each rotating part, making them costly and complex to implement, especially in industrial scenarios. The challenge intensifies when multiple rotations must be measured simultaneously. This study introduces a novel method using a single event-based vision sensor, or dynamic vision sensor (DVS), which offers asynchronous pixel activation, high temporal resolution, and a wide dynamic range. Unlike traditional frame-based sensors, which require high frame rates and generate large data volumes, the DVS analyzes event streams and processes signals to identify crossing edges and time intervals. This enables simultaneous measurement of multiple rotating parts. The method was tested on a fan propeller, a CNC lathe, and drone propellers, demonstrating a relative mean absolute error of around 0.5%. This method allows safe distance measurements without altering the original production line due to its non-contact nature.

Waste Printed Circuit Boards (WPCBs) contain many valuable and rare metals found in electronic waste, and recycling these boards can help recover these metals and prevent hazardous elements from harming the environment. However, the diverse composition of PCBs makes it challenging to automate the recycling process, which should ideally be tailored to each PCB's composition. Computer vision is a possible solution to evaluate WPCBs, but most state-of-the-art models depend on labeled datasets unavailable in the WPCB domain. Building a large and fully labeled WPCB dataset is expensive and time-consuming. In addition, the presence of long-tailed class imbalance, where specific electronic components are significantly more prevalent than others, further complicates the development of accurate detection and classification models. To address this, we propose a new method called GEN Self-Labeling Electronic Component Detector, which utilizes a domain adaptation strategy to train semi-supervised teacher-student models that can handle the lack of fully labeled datasets while mitigating the effects of class imbalance. We also introduce a new version of the Waste Printed Circuit Board Economic Feasibility Assessment (WPCB-EFAv2), which characterizes the PCB's composition by identifying hazardous components, calculating the density of each component type, and estimating the metals that could be recovered from recycling electrolytic capacitors and integrated circuits. Finally, we present a case study involving six PCBs with different characteristics, from which we estimated that 121g of metals could be recovered. The most recovered metal (108g) was aluminum from electrolytic capacitors. This information can help reduce the PCB's composition uncertainty, leading to more efficient dismantling and cost-effective recycling processes.