Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities

• URL: http://arxiv.org/abs/2511.20730v1
• Date: Tue, 25 Nov 2025 11:16:38 GMT
• Title: Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities
• Authors: Nehal Afifi, Christoph Wittig, Lukas Paehler, Andreas Lindenmann, Kai Wolter, Felix Leitenberger, Melih Dogru, Patric Grauberger, Tobias Düser, Albert Albers, Sven Matthiesen,
• Abstract summary: Machine learning and statistical methods dominate current practice, whereas deep learning exhibits a clear upward trend in adoption.<n>Key challenges in existing applications include limited model interpretability, poor cross-stage traceability, and insufficient validation under real-world conditions.<n>This review is a first step toward design-stage guidelines; a follow-up synthesis should map computer science algorithms to engineering design problems and activities.
• Score: 0.2545763876632975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented. This fragmentation stems from uncertainty, particularly the lack of clarity on what types of DDMs to use and when to employ them across the product development lifecycle. To address this, a necessary first step is to investigate the usage of DDM in engineering design by identifying which methods are being used, at which development stages, and for what application. This paper presents a PRISMA systematic literature review. The V-model as a product development framework was adopted and simplified into four stages: system design, system implementation, system integration, and validation. A structured search across Scopus, Web of Science, and IEEE Xplore (2014--2024) retrieved 1{,}689 records. After screening, 114 publications underwent full-text analysis. Findings show that machine learning (ML) and statistical methods dominate current practice, whereas deep learning (DL), though still less common, exhibits a clear upward trend in adoption. Additionally, supervised learning, clustering, regression analysis, and surrogate modeling are prevalent in design, implementation, and integration system stages but contributions to validation remain limited. Key challenges in existing applications include limited model interpretability, poor cross-stage traceability, and insufficient validation under real-world conditions. Additionally, it highlights key limitations and opportunities such as the need for interpretable hybrid models. This review is a first step toward design-stage guidelines; a follow-up synthesis should map computer science algorithms to engineering design problems and activities.

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