Topology optimization designs parts easy to machine faster
Design for Machinability: Topology Optimization for Subtractive Machining Using Trimmed Neural Fields
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Summary
Parts designed by computers are often very detailed, making them hard to carve out with traditional tools. The authors created a new way to design parts so the surfaces are easier to machine with standard cutters, saving time and improving quality. They use a special math trick with neural fields to create shapes that have flat regions separated by edges where curves concentrate, making them fit well with common milling machines. This approach balances making strong parts while also cutting machining time.
What this means in practice
- •For manufacturing engineers: Design more efficient parts that reduce machining time and improve surface finish for subtractive manufacturing processes.
- •For mechanical design teams: Create lightweight and strong components optimized for both structural needs and ease of traditional machining.
Authors
Hongrui Chen, Levent Burak Kara
Abstract
Topology optimization has found significant utility in additive manufacturing, but the resulting geometries are often difficult to realize using subtractive methods. The complex, curved surfaces of optimized designs typically require time-consuming point milling operations. In contrast, geometries composed of developable surfaces are well suited to flank milling, enabling substantial improvements in machining efficiency and surface quality. However, globally minimizing Gaussian curvature leads to overly simplistic designs. We address this challenge with a novel method that represents geometry using three implicit neural fields, optimized to produce machinable components. The resulting designs consist of regions of developable surfaces separated by seamlines where curvature is concentrated. Seamlines are efficiently computed as intersections of the implicit fields, while the geometry emerges through their product. By constraining surfaces to align with the cutter's normal or tangential direction, our approach further accommodates practical constraints of 3-axis machining. We demonstrate concurrent optimization of topology and Gaussian curvature, enabling control over seamline feature scale. Compared to methods that enforce subtractive machinability without considering milling efficiency, our approach consistently reduces machining time while maintaining competitive structural performance.