Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities
🤖 AI Summary
Data-driven methods in engineering design are applied in a fragmented manner, primarily due to unclear methodological suitability across the full lifecycle stages and poor cross-stage traceability—particularly weak adoption during verification.
Method: Following the PRISMA framework, this study systematically reviews 114 peer-reviewed publications (2014–2024) from Scopus, Web of Science, and IEEE Xplore, and—novelty—structures analysis along the four V-model phases: system design, implementation, integration, and verification.
Contribution/Results: Machine learning and statistical methods dominate current practice; deep learning is rising but suffers from limited interpretability and insufficient real-world validation. The study proposes “interpretable hybrid models” as a new research direction and establishes a foundational AI method selection and deployment framework tailored to engineering design. This framework provides both theoretical grounding and actionable pathways for developing standardized AI application guidelines in engineering contexts.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Investigating data-driven methods usage across product development lifecycle stages
Identifying which methods are used when and for what applications in engineering
Addressing fragmentation in adopting AI methods due to uncertainty about application
Innovation
Methods, ideas, or system contributions that make the work stand out.
Systematic review maps machine learning in design stages
Identifies supervised learning and clustering dominance
Proposes interpretable hybrid models for future integration
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