IDEeP – Institute for Digital Engineering and Production

Computational engineering is a multidisciplinary field that focuses on the application of computer-based modeling, simulation, and optimization technologies to a wide range of technical and scientific areas. Depending on the area of application, these may be established and mature methods and procedures, or, in other cases, relatively new approaches and models that require thorough validation and verification. The professional use of computer-aided tools requires an understanding of the relevant fields of engineering, mathematics, and computer science.

The goal of computational engineering is to improve the performance and efficiency of technical systems and processes by virtually modeling and simulating them before they are physically built. The ability to create and analyze complex models enables engineers and scientists to test new designs in order to evaluate the impact of design decisions and identify potential weaknesses at an early stage. This helps avoid costly errors and trial-and-error that would otherwise occur during the physical testing of prototypes.

The field encompasses a wide range of applications, including fluid mechanics, materials science, chemistry, electrical engineering, biomedicine, geology, and environmental science, to name just a few. The field of computational engineering is of critical importance to today’s industry and science, as it accelerates the development of new technologies and products while simultaneously reducing costs. The ongoing development of more powerful computers and algorithms, as well as the availability of large amounts of data, has further advanced the field and is opening up new possibilities for future applications.

Virtual production engineering refers to the use of virtual technologies and digital tools throughout the entire production process, from conception to manufacturing. Virtual environments are used to simulate, optimize, and control production processes. Typically, computer-aided models and simulations are used to design, test, and analyze production facilities, machinery, and workflows before they are implemented in the real world. This enables the early identification of problems, improves efficiency and quality, and reduces costs and time.

Applications of virtual production engineering include virtual factories where layouts and processes are visualized, digital twins of production facilities that replicate real-world operations, and simulations of manufacturing processes to optimize workflows and resource utilization. This innovative technology is used in various industries such as automotive, aerospace, Maschinenbau, and electronics to increase efficiency and agility in production. By integrating Virtual Production Engineering, companies can improve their competitiveness and respond more quickly to changing market demands.

The Virtual Engineering lab focuses on both teaching and applied research in digital factory and production planning, its simulation, and virtual 3D visualization. Furthermore, ergonomic studies for the simulation of workflows can be conducted.

Smart manufacturing, also known as intelligent manufacturing or Industry 4.0, refers to the integration of advanced technologies and digital systems throughout the entire production process to improve efficiency, flexibility, and quality. This strategy leverages the Internet of Things (IoT), artificial intelligence (AI), machine learning, big data analytics, and automation to create a connected and optimized manufacturing environment.

In smart manufacturing, production facilities and systems are interconnected to collect, analyze, and utilize real-time data. This enables proactive monitoring and control of manufacturing processes to minimize bottlenecks, optimize capacity utilization, and reduce downtime. A central aspect of smart manufacturing is the implementation of cyber-physical systems (CPS), which integrate physical and virtual components. This enables machines to make autonomous decisions and adapt to changing requirements.

The benefits of smart manufacturing are manifold: improved product quality, faster time-to-market, optimized resource utilization, and increased productivity. Companies can make informed decisions based on better data analysis and strengthen their competitiveness. Overall, smart manufacturing aims to make the manufacturing industry more agile, efficient, and future-proof by maximizing the potential of digital technologies.

Smart Factory Solutions refer to innovative technologies and concepts implemented in factories to improve the efficiency, flexibility, and quality of manufacturing processes. These solutions leverage advanced digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, big data analytics, and cloud computing to create a highly connected and automated production environment.

In Smart Factory Solutions, traditional manufacturing equipment is equipped with smart sensors that provide real-time data on the status of machines and processes. This data is analyzed to identify bottlenecks, predict maintenance needs, and optimize production workflows. This helps reduce production downtime, lower operating costs, improve adaptability, and accelerate the time-to-market for new products. Furthermore, they enable transparent and data-driven decision-making for management.

Overall, Smart Factory Solutions aim to revolutionize the manufacturing industry and lay the foundation for future processes and factories that are intelligent, adaptive, and competitive.

Predictive maintenance is an advanced approach to the maintenance of machinery and equipment. It relies on the use of real-time data and analytical methods to monitor the condition of equipment and predict potential failures. By integrating sensors, the Internet of Things (IoT), big data analytics, and machine learning, companies can determine the optimal time for maintenance before unplanned downtime occurs.

Predictive maintenance requires experts from all disciplines to collaborate in order to maximize the availability and reliability of processes, products, and machines. By analyzing historical and current data, as well as data from simulations and experiments, algorithms can identify patterns and anomalies that indicate future failures. This proactive approach enables companies to plan resources more effectively and carry out maintenance activities more efficiently.

Predictive maintenance is used in various industries, including manufacturing, the energy sector, the transportation sector, and many others. Overall, predictive maintenance helps optimize operational processes, increase cost efficiency, and improve companies’ competitiveness. Through the use of state-of-the-art technologies and analytical methods, traditional reactive maintenance is being replaced, setting a new standard in maintenance strategy.