In product development, a critical component is engineering data science that makes available robust simulation, testing and field data sets. Simultaneously, there are rapid strides in the fields of Artificial Intelligence (AI) and Machine Learning (ML).

In product design and engineering, the application of AI and ML lagged behind. The three-dimensional products require complex 3D CAD models, FEA meshes consisting of millions of elements, simulation of multiple physics and optimisation runs exploring multiple variants of a design. It generates a lot of data.

CAE or computer-aided engineering is now augmented by AI. It enables the producers to discover insights and explore new solutions to design problems. It also achieves greater product innovation.

Thus AI works in design generation, design exploration and design optimisation. There are automative repetitive tasks. ML here generates direct models for geometry creation and editing, mid-surface extraction, surface and mid-meshing, mesh quality correction. Simultaneously there is efficient assembly management and process guidance.

Thus simulation technology is combined with design exploration and ML. It enables engineers to consider more design dimensions throughout the product development process. ML enables faster design convergence. It rejects low-potential designs early in the development process.

Data analytics help manufacturers perform preventive and corrective actions on their equipment.