Abstract
The digital twin has been gaining significant attention from the academia and industry sectors in the last few years. The digital twin concept enables monitoring, diagnosis, optimisation, and decision support tasks to improve industrial systems operation. One of the identified challenges in this field is the need to improve the decision support cycle by decreasing decision-making time and improving the accuracy of recommendations by considering human intervention in the cycle. Bearing this in mind, the paper explores the use of trust models to improve the recommendation cycle in the digital twin. For this purpose, a literature overview on trust applied in recommendation systems was performed, focusing on the concept, its properties and previous models. Considering this analysis, a trust-based model is specified in a digital twin artificial intelligence-based recommendation system. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV's route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*-Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.

Abstract
Welding physics is complex, and therefore the welding parametrization is time-consuming. In manual welding, the "hand", the experience, and the best sensor of all (the eyes) can compensate for the difficulties in finding the right settings (welding parameters, robot posture, speed,...) for a specific weld seam. In robotic welding the robotic arm and the sensors are limited, and the parametrization time escalates. This work aims to develop a flexible welding robotized system, through the introduction of (knowledge-based) decision support for welding parametrization in an advanced robotic work cell, in combination with advanced (collision-free) offline programming and advanced sensing. By selecting a specific application area, structural steel, this work will reduce the degree of complexity during the development, paving the way for the introduction of knowledge-based welding in the robotic arc welding sector.

Abstract
In this paper it is presented an educational experiment, that consists of a mechatronic system applied to demonstrate concepts such as prototyping and control. The described mechatronic system is based on a conveyor belt, that was integrated with a manipulator, being physical devices commonly used in the industry. The conveyor Belt was prototyped from scratch, using 3d print technology. Its movement is based on the closed loop control of a DC Motor, based on a PID. The Conveyor Belt was integrated with a Braccio Manipulator from Arduino, using the ZMQ communication library, which is a high-performance asynchronous messaging library. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
The materials used in the transport industry have been changing in the last decades. The traditional and heavy steel have been switching by the light alloys like aluminum alloys. However, despite their advantages as low density and high corrosion resistance, the manufacturing process, especially fusion welding, is very demanding and challenging. In the transport industry, most of the hyperstatic components made in aluminum alloys are welded manually with the associate financial costs as well as the lack of quality and repeatability. For these reasons, it is urgent to develop new methodologies to automate this process. The present work intends to show a scientific method to automate the welding process of hyperstatic frames, very common in bicycles, made in aluminum alloy. This methodology involves two steps, the first one in which is performed numerical simulations to determine the optimal welding parameters to minimize the distortion and residual stresses. The second step is experimental one, and it is created an automated welding cell with a robot to weld the frames. It has been proved that it is possible to obtain welding aluminum frames with acceptable quality in agreement with the ASME IX standard. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Distributed power sources will become increasingly ubiquitous in the near future. In this power production paradigm, photovoltaic conversion systems will play a fundamental role due to the growing tendency of energy price, and an opposed trend for the photovoltaic panels. This will lead to increased pressure for the installation of this particular renewable energy source in home buildings. In particular, on-grid photovoltaic systems where the generated power can be injected directly to the main power grid. This strategy requires the use of DC-AC inverters whose output is synchronized, in phase, with the main grid voltage. In order to provide steady output in the presence of load disturbances, the inverter must work in closed-loop. This work presents a new way to design an inverter controller by resorting to the CDM design technique. The obtained results suggest that the controller achieved with this method, although simpler than other methods, leads to an acceptable and robust closed-loop response. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.