Mechanistic Modelling & Problem Solving- A new Data-Driven CAE ProcessGoodson
The automobile industry is constantly evolving and adapting to the fast-paced market demands. To stay ahead of the competition, OEMs are focused on reducing their product development cycle and bringing new vehicles to market in record time. With the help of cutting-edge PLM solutions, the once lengthy 36-month process has now been trimmed down to just 24 months. However, the quest for accelerating product development does not stop there, Auto OEMs are continually exploring new approaches and techniques to achieve the ultimate goal of 12-month development cycle.
This is where EinNel Technologies comes into play, offering a new-age design approach called Data-Driven CAE. The combined expertise of traditional CAE and Data Science inspired the team to leverage the large volumes of data available across the functions right from the concept stage to final production to make predictions about product performance, resulting in a more informed and optimized design process.
Data-Driven CAE prioritizes a reality-driven design rather than a simulation-driven design that takes tooling and manufacturing constraints into account from the very beginning, with the goal of reducing the vehicle design and development cycle by 50%. This eliminates the need for multiple iterations for each design release and provides the most accurate results at the early stage of the design phase by incorporating machine learning and big data solutions.
Based on this approach, we are currently developing a cloud-based platform called EinNel MDOX that connects engineers, data, and resources to effectively model and optimize vehicle designs using real-time data from simulations, physical tests, and field tests. By harnessing the full potential of the cloud, organizations no longer need to rely on high-end computing hardware and validate complex designs with less computing power, allowing engineers to work from anywhere and anytime.
We believe that EinNel MDOX has the potential to revolutionize the way vehicles are designed and pave the way to reducing the vehicle design and development cycle to 12 months.
Mechanistic Problem Solving:
The Data-Driven CAE can be further scaled and redefined as the Mechanistic Solution Approach for solving complex engineering problems by integrating numerical, analytical, and statistical methods. Our technical team has been closely working with various Auto OEMs to identify and resolve vehicle manufacturing challenges using mechanistic solutions.
The conventional method of problem-solving is often time-consuming and resource intensive as it requires extensive domain expertise and experience. In contrast, the Mechanistic Solution Approach provides a faster and more efficient method of problemsolving, delivering results that are accurate and reliable by leveraging our advanced analytics engines.
One of the primary challenges faced by the automotive industry is developing solutions that are compatible with their ecosystems. With a deep understanding of the PLM tools, CAD/CAE software, and ERP products used by the industry, EinNel Technologies is well-equipped to develop solutions that are perfectly suited to the needs of the OEMs’ existing global ecosystems. Also, we understand the importance of delivering solutions that are both effective and easy to use. That is why we have taken the extra step to package our mechanistic solutions into user-friendly applications with interactive dashboards powered by ML algorithms. The dashboards can be tailored to fit the unique requirements of every stakeholder, allowing them to effectively utilize the data and insights we offer. Whether they are looking to improve their manufacturing processes, optimize their designs, or gain a deeper understanding of their product performance, our dashboards give them the tools they need to succeed.