Realigning CAE with AI/ML
Nagendra Kumar – Director, Engineering
In today’s competitive business environment, product development is a complex process that often demands significant time to achieve the best possible design, mitigating the rising costs of materials and, in turn, risking delays in the design process. Additionally, one must not overlook other challenges such as skill gaps, ensuring simulation accuracy, data management, and more. Consequently, CAE design for vehicle development faces challenges in terms of the time required for various CAE analyses, including the need to achieve an optimal design that balances high performance with minimal cost. By leveraging advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) and embracing data-driven CAE, organizations can enhance their capabilities and significantly reduce product life cycle times, thereby accelerating time-to-market.
This is where EinNel’s MDOX comes into play by facilitating the prediction of outcomes, behaviors, and trends that traditionally required extensive time and resources. EinNel MDOX utilizes AI and ML algorithms to harness the vast datasets generated during the product development phases. On the other hand, EinNel MDOX’s Intelligent CAE ensures the effective application of these insights to aid in engineering and design.
EinNel MDOX integrates AI and ML with data-driven CAE to create comprehensive solutions that are not only robust and reliable but are also capable of self-learning and continuous improvement over time. EinNel’s MDOX, with its multidimensional approach, takes into account various parameters and constraints, resulting in optimized and holistic solutions that reduce your design time and enable seamless collaboration among different design and engineering disciplines. This, in turn, fosters a harmonious and synergized product development process.
The combined capabilities of AI, ML, and data-driven CAE in MDOX are strikingly evident in the significant reduction of product life cycle times. AI and ML expedite data processing, providing actionable insights in real-time, which, in turn, reduces the time required for analysis and decisionmaking. MDOX ensures that the design is optimized, taking into consideration various factors, including cost, performance, and manufacturability.
In the pursuit of excellence and innovation, EinNel’s MDOX, which blends AI and ML with datadriven CAE, emerges as the future of CAE. It not only promises to reduce product life cycle times but also ensures that designs are optimal, delivering value that meets the ever-evolving demands of the global market and ensuring a sustainable and profitable future in an increasingly complex global landscape.