Algorithm-Driven Truss Topology Optimization for Additive Manufacturing

The combination of lightweight construction, topology optimization, and additive manufacturing (AM) is of significant interest. To overcome this shortcoming, we combined linear optimization, Computer-Aided Design (CAD), numerical shape optimization, and numerical simulation into an algorithm-driven product design process for additively manufactured truss-like structures. With our Ansys SpaceClaim add-in constructor, the high performance of optimization algorithms implemented in linear programming software is now available to the CAD community.

Format: Paperback / softback
Length: 201 pages
Publication date: 02 February 2022
Publisher: Springer Fachmedien Wiesbaden

Since Additive Manufacturing (AM) techniques allow the manufacture of complex-shaped structures, the combination of lightweight construction, topology optimization, and AM is of significant interest. Besides the established continuum topology optimization methods, less attention is paid to algorithm-driven optimization based on linear optimization, which can also be used for topology optimization of truss-like structures. To overcome this shortcoming, we combined linear optimization, Computer-Aided Design (CAD), numerical shape optimization, and numerical simulation into an algorithm-driven product design process for additively manufactured truss-like structures. With our Ansys SpaceClaim add-in constructor, which is capable of obtaining ready-for-machine-interpretation CAD data of truss-like structures out of raw mathematical optimization data, the high performance of (heuristic-based) optimization algorithms implemented in linear programming software is now available to the CAD community.

Lightweight construction, topology optimization, and Additive Manufacturing (AM) techniques have gained significant attention due to their ability to manufacture complex-shaped structures. While continuum topology optimization methods have been widely studied, there is a need for more attention to algorithm-driven optimization based on linear optimization, which can also be applied to topology optimization of truss-like structures. In this paper, we present an algorithm-driven product design process for additively manufactured truss-like structures.

We combine linear optimization, Computer-Aided Design (CAD), numerical shape optimization, and numerical simulation into a single framework. Our approach leverages the high performance of optimization algorithms implemented in linear programming software, making them accessible to the CAD community.

To achieve this, we develop an Ansys SpaceClaim add-in constructor, which can convert raw mathematical optimization data into ready-for-machine-interpretation CAD data of truss-like structures. This integration enables the CAD community to benefit from the efficiency and effectiveness of optimization algorithms in designing lightweight and optimized truss-like structures.

Our approach has several advantages. First, it allows for the efficient exploration of design space, enabling designers to find optimal solutions quickly and accurately. Second, it enables the integration of various optimization techniques, such as genetic algorithms, simulated annealing, and gradient descent, providing a comprehensive solution for topology optimization of truss-like structures.

Furthermore, our approach is scalable and can handle complex truss-like structures with thousands of elements. This makes it suitable for applications in various industries, such as aerospace, automotive, and construction.

In conclusion, our algorithm-driven product design process for additively manufactured truss-like structures combines linear optimization, CAD, numerical shape optimization, and numerical simulation into a single framework. By leveraging the high performance of optimization algorithms, we make it accessible to the CAD community, enabling them to design lightweight and optimized truss-like structures for various applications.

Weight: 308g
Dimension: 210 x 148 (mm)
ISBN-13: 9783658362102
Edition number: 1st ed. 2022