Yapay zeka destekli shot peening prosesinin optimizasyonu ve ikincil proseslerin SLM ile üretilen AlSi10Mg alaşımının hidrojen kırılganlık direnci ve mekanik performansı üzerindeki etkilerinin incelenmesi

This thesis investigates the optimization of shot peening processes and the mitigation of hydrogen embrittlement in AlSi10Mg alloys produced via Selective Laser Melting (SLM). Chapter one reviews process optimization techniques (e.g., Taguchi, Box-Behnken), additive manufacturing (AM) challenges like residual stress and porosity, and introduces hydrogen embrittlement mechanisms and testing methods. Chapters two and three focus on optimizing shot peening intensity using AI-based approaches validated by Almen tests and analyze real-world aviation failures, such as Bell 412EP and Piper PA-32R, to highlight hydrogen embrittlement's impact on component degradation. Chapters four and five explore the effects of strain rates and post-processing treatments, including shot peening and heat treatment, on mechanical performance, demonstrating significant improvements in fatigue resistance. Advanced strategies for mitigating hydrogen embrittlement are also proposed. The thesis concludes by emphasizing the societal benefits of enhanced material reliability and sustainability, suggesting future research into AI-assisted methods and real-time monitoring systems in manufacturing.