Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning

TL;DR

This paper introduces a deep learning-based retrieval model that uses text descriptions to efficiently find similar mechanical part shapes, aiding in redesign processes within case-based reasoning.

Contribution

It develops a novel text2shape deep retrieval model combining RNN and 3D CNN for shape retrieval based on structural feature descriptions.

Findings

Achieved a maximum retrieval accuracy of 0.98.

Constructed a dataset of 1000 text-shape samples.

Demonstrated effective retrieval for mechanical part redesign.

Abstract

Retrieving the similar solutions from the historical case base for new design requirements is the first step in mechanical part redesign under the context of case-based reasoning. However, the manual retrieving method has the problem of low efficiency when the case base is large. Additionally, it is difficult for simple reasoning algorithms (e.g., rule-based reasoning, decision tree) to cover all the features in complicated design solutions. In this regard, a text2shape deep retrieval model is established in order to support text description-based mechanical part shapes retrieval, where the texts are for describing the structural features of the target mechanical parts. More specifically, feature engineering is applied to identify the key structural features of the target mechanical parts. Based on the identified key structural features, a training set of 1000 samples was constructed, where each sample consisted of a paragraph of text description of a group of structural features and the corresponding 3D shape of the structural features. RNN and 3D CNN algorithms were customized to build the text2shape deep retrieval model. Orthogonal experiments were used for modeling turning. Eventually, the highest accuracy of the model was 0.98; therefore, the model can be effective for retrieving initial cases for mechanical part redesign.