Parameter Extraction from Images Using Multilabel Supervised Learning

In this work, we propose an approach to predict multiple design parameters of products using 2D images and supervised learning techniques. Fully parametric 2D or 3D vector representations have a high degree of flexibility since they are generally capable of morphing between all designs in the design space. A reverse design method is applied to parametrically designed products to extract design knowledge from existing designs and apply it to future designs. The goal of this research is to develop an accurate and efficient predictive model for multiple design parameters. The paper introduces several novel aspects, including learning parametric patterns from a 2D model dataset and using them to predict design parameters for new engineering systems. Additionally, the paper proposes a finetuning multilabel approach for predicting multiple design parameters of different types simultaneously, including binary and continuous parameters. The results demonstrate that the proposed approach achieves high prediction accuracy and suggest that the proposed approach can be used as a reliable tool for predicting multiple design parameters of products in practical engineering design scenarios from only 2D images, potentially allowing designer to iterate through more existing engineering designs.