In the last decades, machine vision and Machine Learning (ML) techniques have seen significant improvements in developing new algorithms thanks to the increment of hardware performance. Exploiting machine vision for specific technological applications became an essential opportunity to introduce significant improvements in the manufacturing context. This paper proposes a study to analyze the ML capabilities to perform Automated Optical Inspection (AOI) for quality control in the manufacturing of Printed Circuit Boards (PCBs). The study has been performed by testing Mask R-CNN and YOLOv8 algorithms and an open-source PCB dataset designed to evaluate other ML techniques. The chosen open-source dataset (i.e. PCB defect dataset released by Open Lab on Human–Robot Interaction of Peking University, HRIPCB) individuates appropriate classes of products and related defects for the context of interest, resulting in a suitable dataset for the performance evaluation of tested algorithms. The challenge of this specific application is the recognition of the component boundaries that have submillimetric dimensions and are not clearly identifiable. The comparison between Mask R-CNN and YOLOv8 highlights that the Mask R-CNN performs better in defect detection (i.e., Missing Holes and Shorts). In particular, for the missing hole defects, for example, the mAP50-95 is 0.798 for Mask R-CNN and 0.261 for YOLOv8. Instead, for the short defects, mAP50-95 is 0.519 for Mask R-CNN and 0.399 for YOLOv8. This work has been carried out to gather know-how for further activity related to AOI for quality control in the PCB assembly employed in the aerospace field.

Purpose: – The purpose of this paper was the design, development, and test of a flexible and reconfigurable experimental setup for the automatic manipulation of microcomponents, enhanced by an accurately developed vision-based control. Design/methodology/approach: – To achieve a flexible and reconfigurable system, an experimental setup based on 4 degrees of freedom robot and a two-camera vision system was designed. Vision-based strategies were adopted to suitably support the motion system in easily performing precise manipulation operations. A portable and flexible program, incorporating the machine vision module and the control module of the task operation, was developed. Non-conventional calibration strategies were also conceived for the complete calibration of the work-cell. The developed setup was tested and exploited in the execution of repetitive tests of the grasping and releasing of microcomponents, testing also different grasping and releasing strategies. Findings: – The system showed its ability in automatically manipulating microcomponents with two different types of vacuum grippers. The performed tests evaluated the success and precision of the part grasping and release, which is a crucial aspect of micromanipulation. The results confirm reliability in grasping and that the release is precluded by adhesive effects. Thus, different strategies were adopted to improve the efficiency in the release of stuck components without negatively affecting the accuracy nor the repeatability of the positioning. Originality/value: – This work provided a flexible and reconfigurable architecture devoted to the automatic manipulation of microcomponents, methodologies for the characterization of different vacuum microgrippers, and quantitative information about their performance, to date missing in literature.

In fused deposition modeling (FDM) process, several uncertainty sources contribute to low accuracy and bad surface finishing. Besides intrinsic uncertainties, other factors, related to the machine structure and actuation, participate to increase complexity and reduce the controllability of the process. On-board vision systems can be successfully exploited to monitor the deposition process and to acquire quantitative information on geometry and transients. This information can be used to calibrate the process parameters where a compensation strategy is implemented. In this paper, a low-cost vision system capable of microscopic acquisitions is integrated into an FDM machine and image-processing techniques are exploited to define calibration procedures to improve the process accuracy, thus extending the use of this equipment to micromanufacturing applications. The effectiveness of the integration is evaluated through the fabrication of a microfluidic device.

The soldering process for aerospace applications follows stringent requirements and standards to ensure the reliability and safety of electronic connections in aerospace systems. For this reason, the quality control phase plays an important role to guarantee requirements compliance. This process often requires manual control since technicians’ knowledge is fundamental to obtain effective quality check results. In this context, the authors have developed a new open source dataset (SolDef_AI) to implement an innovative methodology for printed circuit board (PCB) defect detection exploiting the Mask R-CNN algorithm. The presented open source dataset aims to overcome the challenges associated with the availability of datasets for model training in this specific research and electronics industrial field. The dataset is open source and available online.