AI-Guided Human-In-the-Loop Inverse Design of High Performance Engineering Structures

TL;DR

This paper introduces an AI co-pilot that predicts user-preferred regions in inverse design of structures, significantly reducing iteration time and enhancing design performance with minimal additional effort.

Contribution

It presents a machine learning-based AI co-pilot using U-Net for predicting user preferences, improving human-in-the-loop topology optimization efficiency and effectiveness.

Findings

Predicts plausible modification regions with high accuracy

Generalizes across diverse topology optimization problems

Achieves 39% improvement in buckling load with minimal time increase

Abstract

Inverse design tools such as Topology Optimization (TO) can achieve new levels of improvement for high-performance engineered structures. However, widespread use is hindered by high computational times and a black-box nature that inhibits user interaction. Human-in-the-loop TO approaches are emerging that integrate human intuition into the design generation process. However, these rely on the time-consuming bottleneck of iterative region selection for design modifications. To reduce the number of iterative trials, this contribution presents an AI co-pilot that uses machine learning to predict the user's preferred regions. The prediction model is configured as an image segmentation task with a U-Net architecture. It is trained on synthetic datasets where human preferences either identify the longest topological member or the most complex structural connection. The model successfully predicts plausible regions for modification and presents them to the user as AI recommendations. The human preference model demonstrates generalization across diverse and non-standard TO problems and exhibits emergent behavior outside the single-region selection training data. Demonstration examples show that the new human-in-the-loop TO approach that integrates the AI co-pilot can improve manufacturability or improve the linear buckling load by 39% while only increasing the total design time by 15 sec compared to conventional simplistic TO.