Generative design

[15] It integrates environmental principles with algorithms, enabling exploration of countless design alternatives to enhance energy performance, reduce carbon footprints, and minimize waste.

[20] The study of PV and shading systems can maximize on-site electricity, improve visual quality and daylight performance.

[23] Other popular AI tools were also integrated, including deep reinforcement learning (DRL) and computer vision (CV) to generate an urban block according to direct sunlight hours and solar heat gains.

It is used in industries to produce a variety of end-use parts, which are final components designed for direct application in products or systems.

AM provides design flexibility and enables material reduction in lightweight applications, such as aerospace, automotive, medical, and portable electronic devices, where minimizing weight is critical for performance.

[25] Generative design can help create optimized solutions that balance multiple objectives, such as enhancing performance while minimizing cost.

[27] To overcome these difficulties, researchers proposed a generative design method with manufacturing validation to improve decision-making efficiency.

This method starts with a constructive solid geometry (CSG)-based technique to create smooth topology shapes with precise geometric control.

Then, a genetic algorithm is used to optimize these shapes, and the method offers designers a set of top non-dominated solutions on the Pareto front for further evaluation and final decision-making.

[27] By combining multiple techniques, this method can generate many high-quality solutions with smooth boundaries at lower computational costs, making it a practical approach for designing lightweight structures in AM.