Artificial Intelligence (AI) and Machine Learning (ML): Pioneering the Future of Product Engineering
From a kindergarten child to a retired grandfather, everyone knows that Artificial Intelligence is the future of technology and AI would influence each and every aspect of life, including the Product engineering. At ESPL, we always have an eye on what future is bringing. We adapt ourselves in advance to welcome the future. In 2023, we realized that Machine Learning would play a vital role in the future of engineering services. And as we continue the journey through the third decade of this century, we only see these technologies grow, flourish and prosper! We hired the first PhD employee with the background in Machine Learning applied for CFD topics.
At ESPL, we acknowledge the enormous potential AI and ML in product engineering, encouraging us to strengthen our expertise in these technologies. Accordingly, we’ve launched new offerings this year to help our clients leverage the power of AI/ML.
Data Engineering Services for Product Engineering
We have received an overwhelming response from our customers who are eager to learn how AI/ML can help them save time, costs and efforts and enhance precision in product development with effective data engineering. So, if you're an OEM or a Tier-1 company, we have exciting solutions for you.
What You Need to Implement AI/ML in Your Product Development
If you are an OEM or Tier-1 or a product company with
- An understanding of the product/system requirements (inputs, variables, outputs, target/threshold values).
- A reliable dataset of simulation resulted, CAD models from the historical variants of your products, or testing data.
- A solid endeavor to be in the frontrunner in technological innovation.
Thus, we are eager to be your partner in driving product development with AI/ML.
Fact checks @AI/ML
- AI/ML has enormous potential but one needs trustworthy and high-quality data to provide meaningful and predictive outcomes.
- Setting realistic expectations is vital - AI/ML cannot solve challenges for fully new products or address field failure issues. Instead, one must look at them as complementary technologies that enhance traditional engineering tools.
- Data training for AI/ML implementations takes time to achieve accuracy. The training duration can range from a few weeks to a few months based upon complexity, and the number of input/ output parameters.
- To integrate AI/ML into product development, companies need a strategic approach, ensuring the effective application of AI/ML for optimal outcomes.
- Every implementation of AI/ ML could be unique and needs data training every time. However, businesses can reduce data training time significantly in the next phases of implementation.
Customer Benefits
- Businesses can achieve predictive results for future variants of designs in a few hours once they complete the data training with satisfactory prediction levels.
- They will eliminate the need to build FE models, multi-CPU licenses. Besides, no HPC will be required.
- Companies can accelerate Design of Experiments (DoE).
- Businesses can analyze predictive results precisely like simulation results (Deformation, flow contours, animations).
ESPL’s Offerings
- Comprehensive understanding of simulation process and client expectations
- Feasibility analysis of AI/ ML implementation
- Extensive support in data training
- Training customer’s engineers to begin using AI/ ML
- AI/ ML Implementation Catalyst