Ideko (Spain)

Abstract Thermal deformation is a critical factor affecting the precision of machine tools, requiring accurate thermal modeling to predict temperature fields and thermal parameters. Traditional approaches, such as the Finite Element Method (FEM), require well-defined boundary conditions, which are often unknown or difficult to measure in real machining environments. This paper explores the use of Physics-informed neural networks (PINNs) as an alternative method for solving steady-state heat conduction problems in two dimensions. PINNs integrate sparse sensor data with physical laws, enabling temperature field prediction and convection coefficient estimation without the need for fully specified boundary conditions. We evaluate five different PINN models, varying the balance between data-driven and physics-informed constraints. Results show that enforcing the heat equation alone yields high accuracy in temperature prediction, but accurate convection coefficient estimation requires explicit enforcement of convection conditions. While PINNs successfully infer missing parameters, their sensitivity to temperature gradients can impact accuracy. Additionally, the need for retraining PINNs when conditions change limits real-time applicability, making them more suitable for offline thermal analysis rather than adaptive modeling in dynamic environments.

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

Стаття присвячена дослідженню практичних аспектів розвитку малого підприємництва у сфері послуг. На основі дослідження сучасного стану розвитку малих готелів України виявлено проблеми та перспективи розвитку підприємств туристичного бізнесу. Дана характеристика сутності та особливостей функціонування готельних підприємств малого бізнесу, визначено специфіку ведення підприємницької діяльності малих готелів в Україні. Аналітичне дослідження стану та тенденцій розвитку ринку готельних послуг підкреслило роль малих підприємств у сфері послуг України. Виявлені характерні особливості, що забезпечують високу привабливість суб’єктів малого готельного бізнесу. Визначено основні чинники, які перешкоджають розвитку малого туристичного підприємництва. Запропоновані основні напрямами реалізації державної політики щодо вирішення проблем розвитку малого підприємництва у сфері послуг.

Grinding is a widely used process in many industries due to the precise geometric tolerances and excellent surface finishes it can offer, which makes it very useful when high quality parts have to be manufactured. Due to the wear that the grinding wheel suffers, a process known as dressing is periodically carried out to recover the cutting ability of the wheel. Dressing is an extremely important part of the grinding process, as it can alter and improve the cutting conditions of the wheel.During the dressing of vitrified bonded CBN wheels, the real dressing depth of cut is significantly lower than the theoretical depth of cut, leading to dimensional errors in the dressed grinding wheel, which consequently translate into the ground part. However, while deformations during grinding have been widely studied, this phenomenon has not been yet analyzed during dressing. Therefore, the main objective of this work is to determine the influence of the dressing parameters on the real depth of cut. Also, the effect of dresser wear and the deformations has been determined separately.To this end, a new methodology has been developed to measure the real depth of cut using a laser displacement sensor. Moreover, experimental tests have been carried out to characterize the influence that the dressing speed ratio (qd) and set depth of cut (ad_set) have on the actual depth of cut (ad_real). It has been seen that the use of negative qd results in a more accurate real depth of cut, and therefore a more efficient dressing process. While the error using negative qd is about 10 %, using positive qd is about 25 %.

Robotic arms are widely used in sectors such as automotive or assembly logistics due to their flexibility and cost. Other manufacturing sectors would like to take advantage of this technology, however, higher accuracy is required for their purposes. This paper integrated a multi-camera system to achieve the requirements for milling and drilling tasks in aeronautic parts. A closed-loop framework allows the position of the robot’s end-effector to be corrected with respect to a static reference. This is due to the multi-camera system tracking the position of both elements due to the passive targets on their surface. The challenge is to find an auxiliary system to measure these targets with an uncertainty that allows the desired accuracy to be achieved in high volumes (>3 m3). Firstly, in a reduced scenario, a coordinate measuring machine (CMM), a laser tracker (LT), and portable photogrammetry (PP) have been compared following the guidelines from VDI/VDE 2634-part 1. The conclusions allowed us to jump into an industrial scenario and run a similar test with a higher payload than in the laboratory. The article ends with an application example demonstrating the suitability of the solution.

Abstract The volumetric accuracy of machine tools is significantly impacted by thermal errors, making the evaluation of volumetric accuracy a significant challenge for the manufacturing industry. Several technical solutions have been implemented, both at the industrial and research level, to address this issue. However, considering thermal errors in the volumetric error characterization adds complexity to the process and remains an open topic of research in the field.In this study, a novel volumetric error characterization technique based on simulated Thermal Modal Analysis is proposed for a three-axis machine tool. This approach involves analyzing the impact of thermal modes of the machine tool's structural elements on the volumetric error, providing a characterization that incorporates the physical meaning of the structure. This simulated characterization will serve as a foundation for future experiments that combine it with other techniques to achieve a comprehensive understanding of volumetric errors.

This paper presents an industrial scenario that simulates a Manufacturing as a Service system for the execution of remote production orders built upon the implementation of emerging Asset Administration Shell (AAS) capabilities and International Data Space connectors. Static and dynamic information from industrial assets (presses and laser cutting machines) are modelled with new AAS submodels and the result is stored in an AAS manager/registration system. A manufacturing orchestrator discovers assets through the registry and completes production orders. The AAS registry allows the selection of assets with capabilities to perform tasks and also shares the AAS catalogue available in the system. The catalogue is shared with external parties through Data Space Connectors. Third party companies can launch manufacturing orders remotely using the same connectors. The paper validates the implementation of AAS components and IDS connectors in a manufacturing context where remote production orders can be securely activated.