Statistical Design of Experiments for the Parameterization of Functional Models

The Vision

In view of the global challenges in dealing with resources and sustainability, the world is at a decisive turning point. Traditional economic models based on the linear principle of "take, produce, use, dispose" are reaching their limits. The circular economy is a promising approach to overcoming these limits. The vision of the circular factory aims to refurbish used products in such a way that they are in no way inferior to linearly produced new products of the current generation.

An essential aspect of this vision is the forecast of functional fulfillment in the next product life cycle. The forecast serves as a basis for deciding how a used product should be processed in a circular way within the circular factory. The quantitative functional model links the relevant relationships between design and function in order to make a statement about the functional quality.

Your Contribution

The aim of the work is the design of a statistical experimental design for the parameterization of the functional model.

Tasks

1. Research and evaluation:
   The functional quality of the technical system is influenced by many design parameters. In addition to single-factor influences, there are also interactions between the individual parameters. Based on these requirements, existing methods including strengths and weaknesses are to be identified.
2. Conceptualization:
   A suitable method for the design of experiments is to be derived from the current state of research. The focus here is on the statistical validation of the interactions.
3. Implementation:
   The experimental design is formulated using a selected set of parameters. Experimental data from the X-in-the-Loop test bench is planned for evaluation.
4. Evaluation and optimization:
   Finally, the suitability of the statistical design of experiments is to be evaluated. On this basis, the limits and optimization possibilities of the method are to be derived.

Your statistical design of experiments lays the foundation for the efficient implementation of the experimental parameterization.