Artificial Intelligence (AI) in automobile crash simulations is one of the important steps toward making cars safer and engineering more impeccable. The paper focuses on applying AI techniques, Machine Learning (ML) and Artificial Neural Networks (ANN), toward rendering the crash simulation process more efficient, accurate, and predictive. Traditional methods of crash simulation are heavily dependent on Finite Element Analysis (FEA), which, though effective, could turn time-consuming and, at the same time, very computationally expensive. AI-driven approaches offer a very promising avenue of research into large datasets for predicting the outcomes of crashes, material property optimization, and vehicle design improvement. Some of the critical methodologies focused on the present research relate to the development and training of AI models based on real-world crash data and high-fidelity simulation results. Further, the reduction cycle time and associated cost of development testing for crashworthiness purposes with the aid of AI has been discussed. Results show that AI has immense potential for enhancing the strength and reliability of crash simulations and therefore bringing about safe, efficient automobile designs. Future research directions will also see a combination of AI and real-time data from sensor-equipped vehicles in a bid to provide adaptive safety systems, further improving autonomous vehicle technology.


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    Titel :

    Artificial Intelligence (AI) for Crash Risk Forecasting During Automotive Crash Simulations


    Beteiligte:


    Erscheinungsdatum :

    16.01.2025


    Format / Umfang :

    929935 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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