In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.

This paper presents a framework to develop the automated design of fixtures using the combination ofdesign automation (DA), multidisciplinary optimization and robotic simulation. MDO necessitates the useof concurrent and parametric designs which are created by DA and knowledge-based engineering tools. Thisapproach is designed to decrease the time and cost of the fixture design process by increasing the degree ofautomation. AutoFix provides methods and tools for automatically optimizing resource-intensive fixturedesign utilizing digital tools from different disciplines.

Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.

In response to the growing complexity of industrial automation requirements, this paper introduces a comprehensive framework tailored for the automation of industrial robots within unstructured production environments. The framework, emphasizing on adaptability and flexibility, seamlessly merges cutting-edge GPU-based physics engine, the Isaac Sim from Omniverse NVIDIA, with industrial robots, thereby laying the foundation for the development of a robust and versatile digital twin. This digital shadow serves as a main step towards the realization of digital twin technologies in dynamically evolving production environments, facilitating dynamic decision-making processes powered by real-time virtual environmental data.

Furthermore, this paper show a compelling application scenario to underscore the practical relevance of the proposed framework. Specifically, the application case centers around a hospital test lab, an onsite facility charged with the preparation of tissue samples for subsequent evaluation by medical professionals. Presently, many of the lab’s tasks are performed manually, underscoring the urgent need for increased automation to enhance efficiency and the working environment. The specific task targeted by this paper involves the re-stacking of microscope slides from a slider fixture to a holder in preparation for subsequent operations. The motivation behind the integration of more dynamic behavior into the robotic system stems from the unstructured nature of incoming samples, coupled with deficiencies in the digital information chain, all within the constraints of a cost-sensitive, non-expert setting.

Proving the applicability of this framework in the current test case, it not only enhances efficiency in the hospital test lab scenario but also demonstrates its potential in more advanced applications within the manufacturing field, especially in environments with similar levels of complexity. By removing technical barriers and streamlining the exploration of digital twin applications, this paper contributes to the advancement of automation technologies and sets the stage for future developments in dynamic production environments.

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation.