Using Machine Learning for Manufacturing Process Improvement

A key development goal of any machine-building project is to produce perfectly running, reliable machines that make high-quality products. By leveraging accurate virtual prototypes, seamless production can be ensured earlier in the development process to help assess and improve product profitability.

The advancements in the fields of AI and ML, combined with the increased availability of robust simulation, testing, and field data sets has made engineering data science a critical component of the modern product development lifecycle, but in order to extract maximum value from these exciting tools, companies need a plan to store, manage, and utilize their data efficiently. They need data discipline

Simulation-driven design changed heavy equipment product development forever, enabling engineers to reduce design iterations and prototype testing. Increasing scientific computing power expanded the opportunity to apply analysis, making large design studies possible within the timing constraints of a program. Now engineering data science is transforming product development again. Augmented simulation features inside Altair® HyperWorks® are accelerating the design decision process with machine learning (ML). The power of ML-based AI-powered design combined with physics-based simulation-driven design leveraging the latest in high-performance computing is just being realized.

Two- and three-wheeler vehicle manufacturers, whether they are existing OEMs, new EV start-ups, or suppliers serving this segment, all have the goal of shortening product development time and getting product to market faster. With Altair HyperWorks™, ride, durability, and vehicle dynamics simulations for two- and three-wheeled vehicles can now be seamlessly performed using an intuitive and easy to use GUI with built-in libraries for vehicle models, analyses, and predicting and optimizing vehicle behavior.

When applied to engineering, Machine Learning can be a powerful tool to aid in a range of applications, from faster finite-element (FE) model building to optimizing manufacturing processes and obtaining more accurate results from physics-based simulations. Although incorporating this collection of technology is relatively new in the field of engineering, Altair has made leaps forward in this space to provide users with the tools they need to make a difference.