Uglanov, AlexeyCampean, FelicianAbdullatiff, Amr R.A.Neagu, Ciprian DanielDoikin, AlexandrDelaux, DavidBonnaud, P.2024-11-222024-11-222024-10-15Uglanov A, Campean F, Abdullatiff ARA et al (2024) An NLP-based framework for early identification of design reliability issues from heterogeneous automotive lifecycle data. Procedia CIRP. 128: 728-733.http://hdl.handle.net/10454/20129YesNatural Language Processing is increasingly used in different areas of design and product development with varied objectives, from enhancing productivity to embedding resilience into systems. In this paper, we introduce a framework that draws on NLP algorithms and expert knowledge for the automotive engineering domain, to extract actionable insight for system reliability improvement from data available from the operational phase of the system. Specifically, we are looking at the systematic exploration and exploitation of automotive heterogeneous data sources, including both closed-source (such as warranty records) and open-source (e.g., social networks, chatrooms, recall systems) data, to extract and classify information about faults, with predictive capability for early detection of issues. We present a preliminary NLP-based framework for enhancing system knowledge representation to increase the effectiveness and robustness of information extraction from data, and discuss the temporal alignment of data sources and insight to improve prediction ability. We demonstrate the effectiveness of the proposed framework using real-world automotive data in a recall study for a vehicle lighting system and a particular manufacturer: four recall campaigns were identified leading to corrective actions by the warranty experts.en(c) 2024 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Automotive warranty dataNatural language processingDesign informaticsArticlehttps://doi.org/10.1016/j.procir.2024.05.098CC-BY-NC-ND