As our first project we chose a commercially used subsea riser clamp to showcase the advantages of optimization AI-driven design-to-manufacturing methods with generative design and topology optimisation.
We chose this product to demonstrate how even a highly standardised machine can be further optimized through material reduction, increased efficiency, and a lower environmental footprint.
Original
Optimized
Topology optimization
We optimised the original design over multiple iterations using topology optimisation as our primary tool and comparing our results with generative designed models. We produced six prototypes categorized by geometry, standardisation, performance (FOS) and mass. To maintain coherency, some parts of the original design were left unmodified.
Employing these technologies enables multiple manufacturing methodsāfrom additive manufacturing to multi-axis millingāminimizes mass while maintaining performance, consolidates components into solid parts, and supports sustainability by reducing production waste.
We avoided imposing rigid structural constraints, preserving original geometry while still maintaining feasible structural integrity and factor of safety.
The final topology optimised design shows a 24.9% reduction in mass and an efficiency increment of 44.5% compared to the original design: this product is about 100kg lighter and can withstand 10,800kg more load, leading to an overall performance rise of 86.76%.
The generative design shows a 32.8% reduction in mass and an efficiency increment of 40.5% compared to the original design: this product is about 150kg lighter and can withstand 9,800kg more load, leading to an overall performance rise of 116.62%.