Motivated by energy efficiency and decreasing the amount and size of components, recent studies have presented hydraulically actuated systems that include one or several fluid short circuit connections between actuator chambers. The main motivations for establishing short circuit connection have been to enable hydraulic power sharing directly between hydraulic actuators in terms of cylinders and motors, thereby reducing conversion losses and enabling reduced power installations in hydraulic drive networks. This paper expands the general theory of hydraulic short circuit connections by generically analyzing the consequences of short circuit connections. This analysis is used to define which short circuiting schemes are physically feasible and which inhibit the full functionality of a machine. Furthermore, a generic method is presented on how to identify every feasible short circuiting scheme for any number of double acting hydraulic actuators.
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.
For decades, the classic product development process (PDP) was the backbone of industrial development: clearly structured, linear, with fixed handover points between mechanics, electronics, and software. However, this structure is becoming less effective in an increasingly networked world. Modern products are no longer self contained objects, but part of complex system landscapes embedded in data flows, digital services, and feedback from operations. This means that the old logic of ‘define design produce’ is no longer sufficient. PDP must be thought of as a dynamic system that integrates interactions, dependencies, and learning loops from the outset. Systems thinking means recognizing connections rather than optimizing in isolation. Today, decisions in mechanics affect software, data architectures and service processes and vise versa. A modern PDP must structurally reflect this interconnectedness. This begins with a system architecture developed at an early stage that brings together requirements, functions, data flows, and operating conditions. It serves as a common language between disciplines and forms the basis for traceability, simulation, and adaptation throughout the entire AI based Lifecycle management. Instead of rigid phases, iterative cycles are needed: short architecture and integration sprints, early testing, virtual prototypes. In this way, learning becomes part of the process and not a corrective measure. In addition, end to end data chain digital threads are crucial for making changes transparent and keeping knowledge usable across disciplinary boundaries. In systemic thinking, quality is understood as an emerging property: safety, reliability, maintainability, and data integrity arise from the interaction of components. This perspective requires not only new methods (e.g. MBSE, DSM, AI supported lifecycle management), but also a rethinking of the organization. Roles such as system architects and lifecycle owners link disciplines, while management creates the framework for interdisciplinary decisions. Those who embed systems thinking early on in the PDP reduce late waves of change, improve integration capabilities, and create sustainable knowledge continuity. This makes the PDP itself an adaptive system capable of producing products that can hold their own in networked environments. The transition from product to system is not a trend, but a necessary evolution of technical development.
The development of technical products is undergoing radical change: instead of clearly defined projects with fixed start and end points, today's market is dominated by living systems whose value only unfolds during operation, through updates and over generations. This article traces the shift ‘from project to product’ and situates it in system system of Systems Lifecycle Management (SLM) as a further development of classic PLM approaches. Agility is not understood as a set of methods, but as an attitude: iterative learning, early and continuous feedback, responsibility in interdisciplinary, self-organised teams embedded in a sustainable, modular architecture. On this basis, changes can be made in a targeted and controlled manner instead of leaving them to chance. Standards such as ISO/IEC/IEEE 15288 and current guidelines on ‘systems engineering agility’ support a view of the entire life cycle and emphasise feedback from operation and use. Technologically, agility only becomes practicable through the digital thread, end-to-end data models, MBSE and digital twins; it shortens feedback loops, makes decisions traceable and increases the ability to change without compromising quality. At the same time, limitations remain apparent: safety-critical domains, insufficiently modular architectures, cultural inertia and regulatory compliance requirements call for a conscious translation of agile principles rather than a 1:1 transfer from the software world. Practical examples such as ASEL-CM, system-driven product development, or centralised vehicle architectures show how continuous validation, platform strategies, and data-based governance make the leap possible. The common thread: stability comes from architecture, dynamism from working methods the two belong together. Those who understand SLM as a learning system closely link development, operation and service, use data as a source of knowledge and organise responsibility along the product, not along projects. In this way, the life cycle is transformed from a cost factor into a competitive advantage.
In modern mechanical engineering, the understanding of success has changed fundamentally. It is no longer enough to simply deliver technically sophisticated products today, the ability to strategically support and shape the entire life cycle of a machine is crucial. System of Systems Lifecycle management (LCM) is thus becoming a key success factor that combines technology, economic efficiency and sustainability. At its core, LCM describes the holistic control of all phases of a product from the idea to development, use and maintenance to dismantling and recycling. Especially in mechanical engineering, where systems are often in use for decades, this approach opens up enormous potential for increasing efficiency, reducing costs and boosting innovation. Current research, for example by Salehi [32] and Salehi and Witte [33], shows that the integration of digital concepts such as modelbased systems engineering (MBSE), agile methods and blockchain technologies is crucial to taking LCM to a new level. By using the Munich Agile MBSE Concept (MAGIC), complex systems can be modelled virtually in early development phases and continuously monitored via digital twins. This creates a continuous data chain that seamlessly connects the flow of information between development, production and operation. Blockchain-based architectures ensure data integrity and traceability throughout the entire life cycle an essential factor for trust and efficiency in global value creation networks. For mechanical engineering, this means a profound change: processes become more transparent, feedback loops between operations and development accelerate innovation, and ecological key figures can be precisely recorded and optimised. LCM thus becomes not only a tool for sustainability, but also a driver for new business models such as ‘machine-as-a-service’ or data-based service contracts. Ultimately, System of Systems Lifecycle management is not purely an IT system, but a management philosophy. Those who succeed in combining technological tools with organisational learning and long-term thinking will position themselves as pioneers of a digital and sustainable industry. This makes LCM the key to competitiveness, innovative strength and entrepreneurial resilience in 21st-century mechanical engineering.
