Abstract Additive Manufacturing (AM) is increasingly adopted in the aerospace industry, as benefits like resource efficiency are complemented by distributed manufacturing possibilities that enhance supply chain resilience. However, replacing conventional, established manufacturing methods with Laser Powder Bed Fusion in a highly regulated domain such as civil aviation comes at the price of increased requirements and thus costs for certification and quality assurance, which limit the attractiveness of AM. This paper presents a new data-based certification platform using Machine Learning (ML), a subdomain of artificial intelligence (AI), to enable faster and more cost-efficient design and manufacturing approval for additively manufactured aircraft components. The platform connects all relevant stakeholders and guides them through the certification process. As data sharing across different stakeholders and ML applications are central to the platform, a data governance concept aligned with European legislation, based on project-specific closed groups comprising direct supplier-customer relationships was developed. In addition, a compatible platform business model is described, presenting the roles of stakeholders and their respective value contributions. To this end, a deep dive into the landscape of current certification approaches and requirements was conducted and the impact of AM and the general use of AI on aircraft component certification was evaluated from technical, regulatory, legal, and economic perspectives.
Schwarz et al. (Thu,) studied this question.
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