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.

翻译：从历史案例库中检索与新的设计需求相似的解决方案，是基于案例推理背景下机械零件再设计的首要步骤。然而，当案例库规模较大时，人工检索方法存在效率低下的问题。此外，简单的推理算法（例如基于规则的推理、决策树）难以涵盖复杂设计解决方案中的所有特征。为此，本文建立了一种文本到形状深度检索模型，以支持基于文本描述的机械零件形状检索，其中文本用于描述目标机械零件的结构特征。具体而言，采用特征工程识别目标机械零件的关键结构特征。基于识别的关键结构特征，构建了包含1000个样本的训练集，每个样本由一段描述一组结构特征的文本以及对应结构特征的三维形状组成。定制了循环神经网络和三维卷积神经网络算法来构建文本到形状深度检索模型。采用正交实验进行模型调优。最终，该模型的最高准确率达到0.98；因此，该模型能有效检索机械零件再设计的初始案例。