Driving performance engineering using artificial intelligence in automotive
Take advantage of the potential of AI and machine learning by using generative engineering, creating value from historical data, tapping into data sources, exploit experimental and simulation data, and taking preventive actions before a predicted failure occurs.
Vehicle manufacturers across the world implement artificial intelligence (AI) at a rapid pace into their processes. And with reason. Artificial intelligence in automotive industries can help improve design processes, increase accuracy, or speed up product development.
But how do you get started if you lack the data? Can AI take the guesswork out of your design optimization? How about predictive maintenance? Would it be straightforward to identify potential problems in advance?
Illustrated by examples throughout the design cycle, this webinar outlines the possibilities of AI and machine learning to improve vehicle performance.
Take advantage of the potential of AI and machine learning at every design stage
AI and machine learning will revolutionize how business is conducted. By implementing artificial intelligence in automotive development processes, manufacturers can make better use of data to recognize trends and make smarter decisions.
Take advantage of the potential of artificial intelligence in automotive engineering processes:
- Early concept: Evaluate the best mechatronic design architectures using generative engineering
- Target setting and benchmarking: Create value from historical data
- CAE optimization: Tap into data sources with unlimited scalability
- Product validation: Exploit experimental and simulation data for smarter scenario and product validation
- Predictive maintenance: Monitor component lifetime and take preventive actions before a predicted failure occurs
Stop spending time to debug models. Instead, use the time to implement AI and machine learning to improve vehicle performance.