Conference Agenda

Adaptive structures have the potential to play a significant role in saving resources in the construction industry in the future. For realisation, this requires actuators that meet the requirements of different buildings with their specific load-bearing structures. In the past, the actuators were mainly developed particularly for one exemplary load-bearing structure. This paper analyses the primary classifications for buildings, followed by challenges of adaptive structures, before outlining the draft of a framework for actuators for adaptive structures to speed up and simplify development.

Shorter product lifecycles and a shift from mechatronic to cyber-physical systems are leading to greater product complexity. This complexity can be addressed by more intensive cooperation between product and production system development. Despite intensive development in recent years, these process models have not been able to establish themselves in corporate practice. Therefore, this paper analyses the existing integrative product and production methods to identify the reasons for their lack of use in practice. The analysis has shown that there are nine barriers.

The complexity of the products/systems requires an in-depth understanding of variability and its impact on all phases, from design to maintenance. This study explores Variability Management (VM) emphasizing its challenges. Conducting semi-structured interviews with experts at Renault Group, the research examines variability aspects, semantics, methods, challenges, and possible solutions. The findings offer practical insights into industrial-scale variability management, addressing the use case of the automotive industry.

Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

As digitalization progresses, the development and integration of sensory functions in technical systems become increasingly important. Managing uncertainty, especially in the early phase of this process, is crucial to ensure the reliability of the data provided. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was presented in prior works. In this contribution, the effectivity of the framework is evaluated by applying it to a sensory function for rotational speed and offset measurement of a disk pack coupling using sensor integrating bolts.