Background:

Additive manufacturing (AM) is fast becoming one of the most important modern industries. The digital and flexible nature of AM is shifting traditional global supply chains (SCs) into more decentralized and flexible SCs. Many companies have already embraced AM technologies, while others are still exploring the potential. AM continues to offer new solutions to increase value and adaptability. AM's ability to reduce supply chain disruption makes it an attractive option for more flexible and efficient production. At the same time, understanding the economies of scale is critical to the competitive use of AM. To increase market share, it is important to analyze AM costs.

Aim of the thesis:

This master thesis focuses on a localized cost estimation study for monolithic catalyst prototypes 3D-printed and internally coated with catalytic materials at the IMVT Institute. Consequently, the cost estimation models and workflows developed in this study are specifically tailored to this context and are not intended for broader industrial applications. The primary objectives are to compile a comprehensive database of all relevant costs associated with additive manufacturing, catalytic coating and material preparation. While some of these costs can be measured directly with available equipment, others are extracted from online datasheets or estimated through mathematical calculations.

Tasks:
- Familiarization with cost estimation methods with a focus on parametric cost models
- Data search and data mining to support the estimation methods (can be automated, but is not mandatory)
- Announcement of a final price for each prototype (catalysts), taking into account risk premiums (ideal accuracy is not expected).
- Comparison of the prototypes in terms of price
- Creative suggestions for saving energy and unnecessary costs (electricity, gas, etc.).

Prerequisites:
- Degree in process engineering, chemical engineering, chemistry, materials science or related field
- Confident handling of Excel
- Familiarity with a programming language (Python, MATLAB, etc.)
- Good knowledge of mathematics and statistics
- Interest in automation and product development in process engineering

Tasksetter: Prof. Dr. Christoph Klahn
Advisor: M.Sc. Sima Mehdipour