Lithium-ion batteries represent a pivotal component within contemporary energy storage solutions, exhibiting a diverse range of applications spanning from consumer electronics to electric vehicles and renewable energy systems. Nevertheless, the progressive degradation of these batteries, resulting in a reduction in capacity and performance, poses significant challenges in terms of system safety and reliability. In this context, the evaluation of the Remaining Useful Life (RUL) plays a central role in assessing the health of lithium-ion batteries. Ensuring precise and reliable RUL prediction is critical for the proper operation of a system. In this work, to address these challenges, a novel lightweight deep learning approach has been proposed for battery RUL estimation, by using voltage and current data. The proposed model is an approach based on the Echo State Networks (ESNs), which is compared to conventional deep learning models, such as Long Short-Term Memory (LSTM) networks, which re-quire more complex architectures and substantial computational resources. The ESN-based model demonstrates a comparable predictive capacity, while substantially reducing training and inference times. The model was tested with the CALCE dataset, focused on data obtained during charge and discharge cycles of lithium-ions batteries. Specifically, under the test conditions of 1 C discharge, the ESN requires only 0.3 seconds for training and approximately 0.06 seconds for inference, thus offering a computational advantage over the LSTM model, which requires 384 seconds for training and approximately 0.19 seconds for inference with the same hardware.

In order to successfully integrate circular economy principles into current value chains, it is crucial to ensure the economic sustainability of disassembly processes. Recent scientific and technological innovations, including Large Language Models (LLMs) in artificial intelligence (AI), are greatly accelerating progress in enhancing robotic capabilities. Dismantling is the first step in the re-manufacturing, repair, and recycling processes of end-of-life (EoL) products. Traditionally, this operation is performed manually by operators or by expensive dedicated robotic cells. Although manual disassembly offers flexibility in handling complex situations, it is a time-consuming and labour-intensive process that can negatively affect the health of operators and the cost-effectiveness of the disassembly process. However, robot disassembly presents difficulties in handling complex parts in a flexible manner. Robot application emerges as an automated versatile solution, capable of handling uncertainties in the frequency, quantity, and quality of end-of-life products. A fully automated approach for the removal of clothing zips from garments is proposed. The approach detects the pixels corresponding to the zip using LLM-Based AI techniques to plan the path of the robotic arm. The solution reduces the effort of AI training in industrial applications. Simulations validate the approach and its performance.

Advances in machine learning (ML) have opened up new opportunities to include decision-making capabilities in Internet of Things (IoT) nodes. This opportunity is complex to address, since conventional ML implementations are computationally intensive, thus reducing the possibility of implementing them on resource-constrained systems. In addition, when nodes in a network grow significantly, centralized data processing creates new challenges such as latency, resource efficiency, privacy, bandwidth, etc. Aiming to simultaneously address the above challenges, this paper presents a novel efficient distributed learning strategy for ESN-Based model, that is conceived for training and inference to incorporate part of the model while reducing the sharing of private information. The experiment setup was conducted using Appliances Energy Prediction dataset to forecast energy consumption under different environmental and usage conditions, using simulated IoT devices. The numerical results show that the proposed solution performs well compared to a classical centralized ESN-based approach without sacrificing too much performance at very low computational costs.

In modern industrial manufacturing, simulation and accurate forecasting of energy consumption are critical to optimise resources and reduce waste meeting the significant energy demands and greenhouse gas emissions of this sector. Industry 5.0 (15.0), focused on sustainability and human-centered manufacturing, emphasises the use of Artificial Intelligence (AI), the Industrial Internet of Things (1IoT) and the Cyber Physical Systems (CPS) to monitor and optimise resource use in real time. This paper proposes a novel energy consumption simulation method using Neural Network (NN), specifically the Deep Echo State Network (DeepESN), integrated with a text embedding model, which allows to combine energy consumption data into production scheduling. Unlike existing approaches, our method considers both machine-level energy consumption and production workflows, enabling comprehensive optimisation of energy effi-ciency. Preliminary tests in a real production scenario show the potential of the approach, highlighting its ability to predict energy consumption simply by using smart meters without additional hardware. This work represents a significant advancement in in-tegrating energy consumption modeling into production planning and contributes to more sustainable industrial practices.

The advent of increasingly flexible and adaptive production processes will require equally flexible robotic collaboration. The forthcoming robotic applications must be able to self-adapt to different applications and scenarios and to handle frequent interaction with humans and dynamic environments. Environment perception, object recognition, and trajectory re-planning in a dynamic and changing environment, even with possible human interaction, are features that are not yet all simultaneously available in collaborative robots, and certainly not in industrial robots. This paper proposes some initial results of the authors’ ongoing research on the development of a ROS2-based framework for industrial applications due to which a robotic manipulator equipped with a depth camera is able to have simultaneously perception, recognition and re-planning capabilities in a dynamic and changing environment. This framework is potentially applicable to various industrial robots and depth cameras; here, the first results of the experimental implementation on an Omron TM5-900 collaborative robot equipped with a fixed depth camera will be shown.

The present paper proposes an architecture for a product process monitoring system suitable for SMEs (Small-Medium Enterprises). The monitoring system is the main means by which decision-making systems based on intelligent automation technologies are aware of the state of the system on which they will take decisions. Methods and tools from best-practice and best-effort approaches are proposed in the context of SMEs, where the requirements of low cost, low initial level of digitalization and high production flexibility often coexist and contribute to the complexity of management and control problems in these companies. The paper focuses on the design of the monitoring system using an OSGi framework to meet industry standards and Industry 4.0 requirements, taking into account the peculiarities of SMEs as design constraints. The proposed architecture was first tested using a simulation tool and then implemented on a full-scale production line used for data collection.

Simulators for Wireless Sensor Networks (WSNs) are one of the most important tools for systems development. They enable to study and evaluate new theories and hypotheses for sensors data gathering, testing new applications and protocols. Nowadays, there are a large number of open source WSN simulators and they can be divided into different categories according to their features and main applications. Due to the ability to increase the real WSN prototyping, the Cross Levels Simulator, like Cooja, has become an important class of simulators. Although they are open source, flexible and extensible in all levels, the test interface, the external connection at a physical level and the direct interaction with the process control via the WSN is very poor. In this work we present the Cooja Advanced Sky Interface which is an extension of the Contiki's Cooja network simulator for the Sky mote. Due to the absence of the analog output control in the Contiki OS for the Sky mote, as additional contribution, the Contiki Sky DAC driver has been developed and tested in the Cooja Simulator with the Advanced Sky GUI and GISOO plugin to give the ability to implement control over the wireless sensor network.