CD Laboratory for Model-Integrated Smart Production

Industry 4.0 promises nothing less than the provision of optimal solutions for every desired product. This CD Laboratory develops novel models to explore virtual solutions to deal with the increasing complexity.

The physical and virtual worlds are increasingly merging. More and more physical objects are equipped with intelligent sensor and actuator technologies and are being networked through the development of the Internet of Things. This also applies to production systems. The intensive use of software is turning these into cyber-physical production systems (CPPS). This development, known as Industry 4.0, is regarded as the next industrial revolution. For example, it promises to find optimal solutions by exploring virtual variants or making dynamic runtime decisions. However, this also increases the complexity of CPPS.

Modelling is a promising way of dealing with increasing complexity. However, current modelling principles and practices are lagging behind the requirements of Industry 4.0. For example, models are still defined and used in isolation in one discipline, although it is known that CPPS are inherently multidisciplinary systems and their optimisation must take this into account. Furthermore, the reuse of models is still carried out on an ad-hoc basis, which makes the systematic definition and utilisation of production systems extremely difficult. Thirdly, models are usually seen solely as planning tools for the early design phases. After that, they are usually ignored, which means that their potential for optimising the ongoing operation of the systems and for feeding back knowledge gained during operation into the area of product and process development is lost.

This CD Laboratory carries out necessary research for the further development of modelling principles and practices. The main goal is a new genre of models, the so-called "liquid models". This term refers to the transition from isolated and monolithic models to co-operative and learning models. This leads to a paradigm shift in the generation of modelling knowledge, its use and its evaluation.

With the help of the knowledge gained, the complexity of Industry 4.0 should become manageable in practice.