Mechatronic Systemtechnik (Austria)

Over the past two decades, our understanding of product development has changed fundamentally. Disciplines that were once clearly distinct from one another, such as mechanical engineering, electrical engineering, and software development, have now become increasingly intertwined. Products are no longer isolated objects, but rather nodes in a network of systems, data, and processes. In this environment, it is no longer enough to manage individual data sets – they must flow, remain consistent, and be available to the right people at the right time. The AI based lifecycle management system of Digital Threads (Digital Thread for short) has therefore established itself as the backbone of modern system engineering. Early approaches to product data management (PDM) focused on central storage of CAD data and the management of versions and approvals. However, with the advent of complex mechatronic systems, networked vehicles, and modular plants, it became clear that this approach was not sufficient. Companies began to realize that it was not enough to simply store geometries; they needed a system that mapped the entire lifecycle of a product, from the initial requirement through development and production to operation and decommissioning. Today, Digital Threads’ AI based lifecycle management system acts as a digital nervous system that connects disciplines such as mechanics, electronics, software, and simulation. While system engineering provides methodical approaches for structuring requirements and architectures, Digital Thread ensures that the associated data are findable, traceable, and up to date. This object oriented approach creates transparency and enables end to end traceability, which is a key requirement of modern systems engineering. Another feature of modern digital thread approaches is the seamless integration of models and product data. Changes in one discipline, for example, in design, can automatically detect and forward all affected areas such as requirements, tests, and functions. This promotes the continuous flow of information and prevents unnoticed consequences of changes elsewhere in the system. The close link between AI based lifecycle management systems for digital threads and system engineering goes beyond technical necessities and promotes cultural change in companies. Organizations must not only introduce technology, but also clearly define processes and responsibilities in order to fully exploit the effectiveness of this system. Clear, transparent handling of changes, systematic management of variants and integration of real time data from operations are key success factors for the implementation of digital threads. The concept of ”‘digital twin’, which enables a continuous connection between the models of real and virtual products, can only be realized through the consistency and transparency offered by the digital threads. This, in turn, promotes more sustainable and data driven decision making processes that can be taken into account throughout the entire product lifecycle, from development to maintenance. Overall, it is clear that AI based digital thread lifecycle management systems not only provide the technical basis for system engineering, but are also an integral element for the successful development and operation of complex, networked systems. They promote innovation, improve quality, and enable companies to make their product development more flexible and efficient.

Product development is undergoing profound change: whereas the focus used to be on geometric modelling of individual components, today the focus is on networked systems. Classic CAD design is increasingly becoming a strategic integration tool that combines mechanics, electronics, software and simulation. Model-based approaches such as MBSE and semantically linked CAD models create consistent data chains that enable seamless collaboration across disciplinary boundaries. The shift from component design to systemoriented development processes is particularly evident in the growing importance of parametric and functionally linked models. Changes to a parameter automatically lead to adjustments in dependent geometries, simulations and parts lists, significantly reducing development times and error costs. At the same time, CAD is becoming increasingly linked to PDM and AI-based lifecycle management systems. This integration creates a uniform, always up-to-date database the ‘single source of truth’ across the entire product lifecycle. Modern CAD systems not only map geometries, but also manage semantic information such as functions, materials, manufacturing data and tolerances. Simulation plays a central role in this: it is no longer a downstream step, but an integral part of the design. Isogeometric analyses and automated feedback loops are increasingly blurring the line between design and verification. Another milestone is the connection between CAD and the digital twin. Here, real operating data is linked to virtual models, so that design, simulation and operation are part of a continuous cycle. Companies that implement this approach report shorter development cycles, higher product quality and better maintainability. But the transformation remains challenging: data quality, standardisation, interfaces and cultural change are key hurdles. The move towards ‘system-driven product development’ is forward-looking: CAD as a platform for interdisciplinary collaboration, automated adjustments and learning digital twins. This turns the CAD system into the control centre of networked development a place where mechanics, electronics, software and data management merge into an intelligent overall system.

This article highlights how action research (AR) effectively supports the establishment and further development of an AI-supported system of systems lifecycle management with digital threads. The starting point is the growing complexity of networked product and system landscapes, in which technical, organizational, and data related issues are inextricably intertwined. AR combines planned intervention with systematic observation and joint reflection in real business environments. In several industry academic collaborations, recurring cycles of diagnosis, planning, intervention, and reflection were used to integrate heterogeneous processes and data models across the entire lifecycle: from CAD CAE PDM couplings to endtoend change processes to MBSE embeddings with SysML and model driven mechatronics. The results show three core effects. First, AR promotes organizationwide learning: boundaries between mechanics, electronics, software and IT are dissolving, acceptance of new roles and data sovereignty is increasing, and understanding of quality, consistency, and traceability is growing. Second, AR facilitates gradual technical integration because social negotiation and technical architecture are designed together including links to IoT MES data, digital twin approaches, and sustainability metrics (e.g., LCA integration into development decisions). Third, measurable improvements are emerging throughout the lifecycle, such as shorter lead times, more robust change statuses, and traceable thread links across domains and organizations. At the same time, the limitations become transparent: case transferability remains limited, the balance between participation and analytical distance requires clear roles, and SoS constellations with many partners require patience and reliable governance. A practical design framework can be derived from the overall view: AR serves as a sociotechnical catalyst that combines MBSE, digital threads, and sustainability into a learning system. Early joint hypothesis formation, iterative piloting with clear maturity criteria, the coupling of qualitative reflection and quantitative indicators, and the anchoring of communities of practice are crucial to success. In this way, AR not only enables the introduction of new tools, but also the transformation of the development culture a prerequisite for ensuring SoS capability, transparency, and adaptability in the digital age.

Variable displacement pumps are commonly employed in fluid power equipment. In applications involving variable displacement pumps, overheating of oil is a frequently encountered problem necessitating an effective oil cooling solution. In this work, the thermo-hydraulic performance analysis of an air blast hydraulic oil cooler, which is a compact heat exchanger (Brazed Aluminium Plate-Fin type) with surface heat enhancement feature in the form of rectangular offset strip fins is presented. The performance models available in the literature present correlations for f and j factors limited to a particular surface geometry and Reynolds number Re range. In the present work a detailed theoretical thermo-hydraulic analysis of an existing heat exchanger is presented using models available in the literature and also experimentally validated using a developed test-rig. The performance parameters derived from f and j factors like exchanger effectiveness, heat transfer coefficients and pressure drop are amply addressed. It was found that, cooler effectiveness varied only slightly with increasing oil inlet temperatures; whereas effectiveness decreased with increasing flow rates. The heat transfer rates increased with increasing oil flow rates and also with increasing oil inlet temperatures. The pressure drop on the oil side was found negligible.

This study aims to develop a prototype of portable head feed harvesting robot that can be installed in a tractor so that the users of tractors that initially only function during the farming season can also function during the harvesting season. This machine is divided into 3 main parts. In the first part there is a cutting mechanism that functions to cut rice using a DC electric motor with a crank mechanism connection to the cutting shaft. The second part is a rice lifting mechanism which functions to lift the rice cut into the conveyor belt position. The third part is a connection that connects the rice cutting machine to the used tractor using a bolt connection. The machine has been tested on rice field and work successfully.

Load-bearing applications of hydrogels call for materials with excellent mechanical properties. Despite the considerable progress in developing tough hydrogels, there is still a requirement to prepare high-performance hydrogels using simple strategies. In this paper, a sponge-reinforced hydrogel composite is synthesized by combining poly(acrylamide) (PAAm) hydrogel and polyurethane (PU) sponge. Uniaxial compressive testing of the hydrogel composites reveals that both the compressive modulus and the strength of the hydrogel composites are much higher than those of the PAAm hydrogel or sponge. In order to predict the compressive modulus of the hydrogel composite, we develop a theoretical model that is validated by experiments and numerical simulations. The present work may guide the design and manufacture of hydrogel-based composite materials, especially for biomaterial scaffolds and soft transducers.

In wafer scanners - the machines that define the details of electronic chips - there is a need for highly accurate deformation measurements of the machine components during the chip manufacturing process. This thesis develops an estimation methodology, based on shape fitting principles, that aims at a low estimation error and addresses the specific requirements related to one of the components of a wafer scanner, the wafer chuck.

The increasing application of microsatellites (from 10 kg up to 100 kg) for a rising number of various missions requires the development of suitable propulsion systems. Microsatellites have special requirements for a propulsion system such as small mass, reduced volume, and very stringent electrical power constraints. Existing propulsion systems often can not satisfy these requirements. The present paper discusses the development and test of a bipropellant thruster complying with these requirements. The main development goal of this effort was the utilization of ethanol in combination with hydrogen peroxide (H2O2) as a non-toxic propellant combination. The Turbo-Pump Fed Miniature Rocket Engine (TPF-MRE) is a bipropellant thruster consisting of four subsystems: the propellant pumps, a decomposition chamber with a monolithic catalyst, a turbine, and the thruster itself. The turbine is driven by the decomposed hydrogen peroxide and magnetically coupled with a power generator. The power produced is then used to generate a pressure head in order to deliver the propellant into the combustion chamber. This system therefore constitutes a self-sustaining system and does not rely on the limited power supply of a micro-satellite. Previous test have shown that although the thruster can be operated with ethanol and oxygen, it was not possible to ignite the thruster when utilizing hydrogen peroxide in a 70% concentration by weight. A minor redesign of the thruster and the test facility was therefore initiated. This redesign together with the use of hydrogen peroxide in higher concentration was speculated to improve this behavior. However, even though the monolithic catalysts were able to decompose hydrogen peroxide in a concentration of 87.5 % with nearly 100 % efficiency, it was not possible to ignite or operate the thruster. Subsequently, a thorough investigation of the baseline design and operational conditions of the thruster was conduced. It was found that the failure of the thruster to ignite is due to a combination of reasons. The combustion chamber length is too short to facilitate sufficient mixing of the propellants, making an ignition impossible or very difficult at least. Additionally, the combustion chamber pressure which was chosen such that it accommodates the performance of commercially available mircopumps is considered too low. This further deteriorates the conditions for which an ignition is feasible.

International audience