Design Optimisation

Topology optimisation is a mathematical method that determines the optimal distribution of material within a defined design space, subject to loads, boundary conditions, and manufacturing constraints. Starting from a solid block of material, the algorithm (typically SIMP — Solid Isotropic Material with Penalisation) iteratively removes material from regions that contribute little to structural performance, converging on an organic, load-path-efficient geometry. The result typically reduces component mass by 20–40% while maintaining or improving stiffness and strength.

Design of Experiments is a structured approach to exploring how multiple input parameters (e.g. wall thickness, fillet radius, material grade) affect one or more output responses (e.g. mass, maximum stress, natural frequency). Rather than varying one parameter at a time — which is slow and misses interaction effects — DOE uses statistical sampling plans (Latin Hypercube, full factorial, Box-Behnken) to efficiently map the design space with the minimum number of simulations. The result is a Response Surface Model that can be interrogated in seconds to find optimal combinations.

Modern optimisation solvers support a range of manufacturing constraints that ensure the resulting geometry is producible. For casting and forging: draw direction and minimum member size constraints prevent undercuts and thin walls. For milling: tool accessibility constraints ensure all surfaces can be machined. For additive manufacturing (3D printing): overhang angle constraints prevent unsupported structures. Symmetry constraints can also be applied when mirrored or rotationally periodic designs are required. CHS Intl always discusses manufacturing intent before setting up the optimisation.

Many engineering problems have competing objectives — for example, minimising mass while also maximising fatigue life, or reducing drag while maintaining downforce. Multi-objective optimisation explores these trade-offs simultaneously, producing a Pareto front: a curve of optimal solutions where improving one objective necessarily worsens another. Engineers can then select the design point on the Pareto front that best matches their project priorities — for instance, accepting 5% more mass to gain 30% more fatigue life. CHS Intl presents Pareto front results visually to support informed design decisions.

Both. For new designs, topology optimisation is applied to the full design space from the outset. For existing designs, shape and size optimisation can improve a current geometry by morphing surfaces or adjusting parametric dimensions within defined bounds — without changing the overall architecture. This is particularly effective for reducing mass of established components, improving fatigue life at stress concentrations, or tuning natural frequencies away from excitation ranges.

Mass savings of 20–40% versus a conventionally designed baseline are typical for structural components with a well-defined design space. Aerospace brackets optimised for additive manufacturing have achieved reductions exceeding 60% in some published case studies. The actual saving depends strongly on how conservative the original design was, the number of load cases that must be satisfied simultaneously, and the manufacturing constraints applied. CHS Intl provides a conservative estimate at project scoping based on the component type and load environment.