Miura Nexus: Physics-Informed Machine Learning Technology: A Use Case in Optimal Process Design.

Physical simulation is a powerful but time-consuming tool for engineers. Both data generation and analysis are time intensive – the iterative process requires many cycles to define a viable solution.

The rgenerated results are difficult to evaluate, visualise and communicate. In addition, physical simulations in aerospace industrial processes, especially with complex engineering components and advanced materials, lead to significant variations that make it difficult to predict production quality prior to implementation.

Miura aims to overcome these challenges in development and high-end manufacturing processes by using PIML technology to develop a predictive quality analysis tool. This tool supports simulation engineers and factory operators in making informed decisions.

By using this technology, Miura provides an in-depth and physically based simulation of part quality that is particularly suited to manufacturing processes that involve complex physical transformations.

Benefits:

• Physics-informed machine learning (PIML) technology to help simulation engineers make improved decisions
• Proprietary technology to accelerate design exploration and improve process design
• Real-time tablet performance
• Engineers and operators can use the generated models as predictive components integrated into their workstations
• These models not only predict part quality, but also provide recommendations for optimal process parameters for each part