Knowledge-empowered multitask learning to address the semantic gap between customer needs and design specifications

Smart manufacturing attempts to build a collaborative and integrated platform to enable flexibility in product design and manufacturing processes so as to better address customer needs. This makes a smooth information flow a prerequisite for smart manufacturing. However, firms usually struggle with a lack of consistency and coherence in communication and information exchange in design and manufacturing practices, a phenomenon called "semantic gap." This article presents a multitask learning framework to close the semantic gap between customers and designers/engineers to facilitate efficient product codevelopment. We elicited domain knowledge from a product review text corpus and integrated the knowledge into a bidirectional long short-Term memory-based multitask learning network. Transfer learning was then applied to adapt the network so it could bridge the semantic gap between customer needs and product specifications. Experiment results indicate that the proposed method unites customer needs and product specifications in smart manufacturing by effectively mapping across these domains. Copyright © 2021 IEEE.