AI-Driven Predictive Analytics for Intelligent Decision-Making in Next-Generation Engineering Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P101Keywords:
Predictive analytics, Artificial intelligence, Machine learning, Engineering design, System simulation, Data analysisAbstract
AI-driven predictive engineering analytics integrates data analytics with predictive models to optimize engineering design and manufacturing processes. This approach leverages historical data, physical simulation models, and data from testing to enable engineers to make informed decisions, automate processes, and explore design possibilities more efficiently. By using machine learning and AI, engineers can create accurate and rapid predictions about future outcomes, enhancing system simulation and streamlining processes in industries such as automotive, aerospace, and electronics. Predictive engineering analytics involves data collection, model building, and deployment. Data is gathered from various sources, including computeraided engineering, physical testing, and manufacturing. AI algorithms, like Transformers, analyze this data to identify patterns and trends, enabling proactive actions to prevent problems and capitalize on opportunities. The deployment phase involves integrating predictive analytics into actionable software, providing end-users with tools that emulate traditional system simulation software but with greater ease of use and faster response times. This leads to better decision-making, optimized performance, and accelerated innovation in complex engineering systems
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