Exploring topic modelling for generalising design requirements in complex design

Abstract

As the redesign process progresses in product lifecycle management, effectively managing engineering changes becomes increasingly challenging, often leading to catastrophic and costly project failures. In response, the study provides a framework for generalising design requirements documents into topics that engineers can use to understand complex designs. Based on previous work, this study employs and compares four different models, including latent Dirichlet allocation (LDA), the collapsed Gibbs sampling algorithm for the Dirichlet multinomial mixtures model (GSDMM), LDA-BERT, and GSDMM-BERT to determine the appropriate representation of requirements documents. Both heatmaps and UMAPs are used to illustrate the correlation between topics and words. The results indicate that the combined vector representation of topic modelling and the sentence-BERT model outperforms single topic modelling. This combined model leverages the additional knowledge from a pre-trained sentence-BERT model, thereby improving model performance and word distribution in all three industrial projects. Through this proposed framework, engineers can potentially generalise high-quality requirements topics for large requirements documents.