Measuring functional independence in design with deep-learning language representation models

Abstract

Measuring functional coupling in complex systems is an important task for good design practice, though historically it has been an art of subjective judgement. With the recent advancements in Deep Learning and Natural Language Processing, functional requirements (FRs) and design parameters (DPs), which are expressed as words and sentences, can be represented in a vector space. The sentence embedding model, BERT, was used in this paper to vectorize FRs and DPs, to calculate functional independence and to study how metrics for functional coupling measurement can be enhanced. It was found that semantic similarity among FRs and DPs, represented in vector space, could be used to compute quantitative values for metrics of functional independence. It was also found that design cases where coupling was unambiguous yielded the best results, while cases where laws of physics needed to define the FR-DP relationship did not transliterate well to the natural language used to express the FR-DP highlighted the limitations of the model in its current state. This study, however, demonstrates a great opportunity to develop a robust, fine-tuned design language representation model for accurately measuring functional independence as a part of our effort to enhance design intelligence.