Artificial intelligence-driven design : How AI will change design in the industry
Toivomäki, Santtu (2024-05-31)
Artificial intelligence-driven design : How AI will change design in the industry
(31.05.2024)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2024061049208
https://urn.fi/URN:NBN:fi-fe2024061049208
Tiivistelmä
This thesis explores the transformative potential of artificial intelligence in enhancing additive manufacturing design processes. By integrating AI in design for additive manufacturing, designers can overcome traditional constraints and innovate at an unprecedented pace. The study focuses on the use of generative design and topology optimization, which utilize AI to automate and optimize design parameters, thereby enhancing the creation of complex, functionally superior, and customized products in less time.
However, the integration of AI in design for additive manufacturing also presents challenges, including the dependency on high-quality data and the need for extensive training datasets to avoid biases. Future directions suggest further integration of AI to refine design processes, enhance the predictability of material properties, and reduce the iterative nature of traditional design methodologies. The research discusses how AI-driven methods not only streamline the design-to-production cycle but also improve material utilization, reduce waste, and increase the sustainability of manufacturing practices.
The thesis highlights AI's crucial role in revolutionizing design capabilities in the AM industry. AI-driven optimization is expected to become standard practice, enhancing both efficiency and sustainability of manufacturing processes.
However, the integration of AI in design for additive manufacturing also presents challenges, including the dependency on high-quality data and the need for extensive training datasets to avoid biases. Future directions suggest further integration of AI to refine design processes, enhance the predictability of material properties, and reduce the iterative nature of traditional design methodologies. The research discusses how AI-driven methods not only streamline the design-to-production cycle but also improve material utilization, reduce waste, and increase the sustainability of manufacturing practices.
The thesis highlights AI's crucial role in revolutionizing design capabilities in the AM industry. AI-driven optimization is expected to become standard practice, enhancing both efficiency and sustainability of manufacturing processes.