Digital Lifecycle Engineering for Drone Systems

Within the Digital Lifecycle Lab (DLL) at the Institute of Machine Components and Methods of Development, ongoing theses and research activities focus on establishing lifecycle-oriented digital engineering approaches that connect education, applied research, and industrial practice.

Central to these efforts is the development of integrated engineering frameworks that combine system modeling, data-driven workflows, and traceability considerations within a coherent digital ecosystem. Through structured data availability and reusable engineering artifacts, the DLL provides the perfect foundation for researching aspects such as modern systems engineering methods, consistency, interdisciplinary collaboration, and continuous development across all life cycle phases.

Drone systems represent a particularly suitable reference domain for this research. Due to their high interdisciplinary complexity – integrating mechanics, electronics, software, communication systems, and mission-specific payloads – they serve as ideal demonstrators for lifecycle-oriented digital engineering approaches.

Engineering Stories: From Digital Lifecycle Concepts to Operational Systems

A key objective of these activities is to translate theoretical engineering concepts into tangible development stories in order to provide students and researchers with a better understanding of aspects such as digital infrastructures, engineering methods, and human-centered processes in real-world application scenarios.

Within this broader context, different drone systems are used as reference platforms to study:

  • AI-enhanced engineering automation
  • Seamless data integration across tools and disciplines
  • Lifecycle-spanning engineering workflows
  • Human-system interaction in mission-critical environments

These systems range from research demonstrators and educational platforms to mission-oriented operational drones developed in collaboration with external partners. Future projects within the DLL will continuously expand this portfolio, enabling comparative studies across different system architectures, mission profiles, and lifecycle strategies.

Use Case: Alpine Rescue Drone System

One central demonstration scenario emerging from these activities is the development of a multirotor drone system in collaboration with an Austrian mountain rescue team.

What began as an exploration of how digital engineering methods could support real-world missions evolved into a lifecycle-spanning reference implementation that combines technical innovation with methodological and educational objectives.

The alpine rescue drone supports rapid-response solutions for the localization and assistance of injured hikers in mountainous environments. At the same time, it serves as a tangible example of interdisciplinary system development, illustrating how modern engineering workflows interact across disciplines, tools, and development stages.

From an engineering perspective, a lifecycle-oriented development framework integrates:

  • System modeling
  • Method modeling
  • Traceability concepts
  • Optimization strategies

These elements are embedded into a coherent digital representation. Engineering artifacts, data models, and interdisciplinary processes are systematically linked to demonstrate how structured digital engineering enables transparency, reuse, and collaboration across development phases.

Expanding the Drone Systems Portfolio

While the alpine rescue drone represents a flagship scenario, the DLL’s Drone Systems activities extend beyond a single application case.

Current and future drone system developments include:

  • Modular drone architectures for rapid prototyping and educational use
  • Mission-specific drones

Each system contributes to a growing ecosystem of interoperable engineering artifacts, models, and datasets. By maintaining a unified digital backbone, insights gained in one project can be systematically transferred and reused in others.

AI-Driven Engineering and Process Optimization

Building on structured data foundations, current research focuses on identifying disruptions and inefficiencies along the development process and translating them into AI-supported, data-driven solutions.

The objective is to:

  • Enhance engineering decision-making
  • Accelerate iteration cycles
  • Improve traceability and compliance
  • Support future AI-augmented engineering workflows

Drone systems provide a highly dynamic and data-rich environment for evaluating these approaches under realistic conditions.

Education, Research, and Industrial Collaboration

Drone Systems at the DLL serve as:

  • Educational platforms for project-based learning
  • Research demonstrators for digital lifecycle innovation
  • Collaboration interfaces with industrial and public partners

Through thesis projects, interdisciplinary workshops, and applied research collaborations, students and researchers actively contribute to evolving system architectures and digital engineering methods.

Interested in becoming part of this evolving story?

Join the movement and explore how digital engineering and AI-driven systems development are shaping the future of engineering practice.

Bachelor’s and Master’s Theses at IME

Current Research Activities

  • AI-enhanced engineering automation
  • Seamless data integration and availability within a unified data repository
  • Engineering education and human-enablement for a future-oriented engineering workforce
  • Strategic approaches to connect development with manufacturing efforts and lifecycle considerations
  • Method modeling and process analysis