Sergio Illana Rico

This work presents a Digital Twin aimed at incorporating proactive quality control capabilities into industrial vision systems applied to the manufacturing of automotive components. The proposed approach combines an existing industrial inspectionsystem with a multi-agent platform developed using SPADE and deep learning-based models for the automatic identification of surface defects. The Digital Twin enables the synchronization of information generated by the physical inspection system witha digital representation oriented toward defect analysis and production-process traceability. For automatic defect identification, models based on the YOLO architecture are trained using images acquired from the industrial environment. In addition, atraceability analysis mechanism is proposed to associate specific defects with different stages of the manufacturing process. The proposed architecture has been defined and functionally validated in an industrial environment for headlamp lens inspection,providing a basis for future supervision and proactive quality control strategies in manufacturing.

This paper introduces GrafcetWeb, an innovative web-based platform tailored for the design, simulation, and execution of GRAFCET diagrams in industrial automation education. Unlike traditional isolated simulators, GrafcetWeb functions as a Control Digital Twin, enabling real-time bidirectional interaction with physical Programmable Logic Controllers (PLCs) via the IoT MQTT protocol. The system emulates the Operative Part of a PLC, allowing engineering students to validate logical automation sequences risk-free before deploying them synchronously with a physical plant. Built on modern web technologies like React and React Flow, the tool removes local installation dependencies while integrating advanced functionalities such as counters, timers, and real-time visual supervision via IP cameras. The article details the system architecture, the technologies involved, and a practical case study, concluding with perspectives on its upcoming implementation in automation courses at the University of Jaén.

The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This work makes use of this technology to develop a methodology for the detection of the threshold by which an extra-virgin olive oil (EVOO) drops into the virgin olive oil (VOO) category. With this aim, two features were studied: the level of fruitiness level and the type of defect. The results showed a greater influence of the level of fruitiness than the type of defect in the determination of the detection threshold. Furthermore, three of the sensors (S2, S7 and S9) of the commercial e-nose PEN3 were identified as the most discriminating in the classification between EVOO and VOO oils.

This paper presents a machine vision system that performs the automatic positioning of optical components in LED modules of automotive headlamps. The automatic adjustment of the module is a process of great interest at the industrial level, as it allows us to reduce reworks, increasing the company profits. We propose a machine vision system with a flexible hardware–software structure that allows it to adapt to a wide range of LED modules. Its hardware is composed of image-capturing devices, which enable us to obtain the LED module light pattern, and mechanisms for manipulating and holding the module to be adjusted. Its software design follows a component-based approach which allows us to increase the reusage of the code, decreasing the time required for configuring any type of LED module. To assess the efficiency and robustness of the industrial system, a series of tests, using three commercial models of LED modules, have been performed. In all cases, the automatically adjusted LED modules followed the ECE R112 regulation for automotive lighting.

Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the ground truth obtained by measurements made with other types of sensors. Marking these canopies manually is an arduous and tedious process that is replaced by automatic methods that rarely work well for groves with a thick plant cover on the ground. This paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera, regardless of the plant cover density between canopies. The method is based on the relative spatial information between canopies.The planting pattern used by the grower is computed and extrapolated using Delaunay triangulation in order to fuse this knowledge with that previously obtained from spectral information. It is shown that the minimisation of a certain function provides an optimal fit of the parameters that define the marking of the trees, yielding promising results of 77.5% recall and 70.9% precision.

This paper provides a zero defect manufacturing (ZDM) approach designed for the virgin olive oil (VOO) industry, with the objective of producing the best possible product using sustainable methods. A deep analysis of related work for ZDM and the current state-of-the-art technology in the VOO elaboration process is presented, along with the implications of the well-known trade-off between quality and extraction yield and the importance of having the right information on the state of the fruits and the main technological variables of the process. Currently available new technologies, such as smart devices with cloud connectivity, enable having the required amount of data and information in real-time, thus making the concept of ZDM possible. Together with the proposed ZDM approach and strategies, the basic requirements and the first steps towards the implementation of ZDM in this productive sector are identified.