Data-driven CFRP machining performance prediction and optimization: Advances, challenges and future prospects

• The first comprehensive review on application of AI data-driven methods in CFRP machining process prediction/optimization. • Four key scenarios of application in CFRP machining are identified. • Knowledge gaps in data scarcity, model interpretability, close-loop controlled system and non-conventional machining of CFRP are identified. The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However, significant challenges including surface defects (delamination, burr, surface roughness), rapid tool wear and material transition issues in drilling CFRP/metal stack, underscore the need for effective, automated process prediction/optimization for improved machining performance. Conventional physics-based models often fall short due to their reliance on extensive computational resources and inability to capture CFRP’s complex machining dynamics arising from thermo-mechanical load coupling and process uncertainties. To address these limitations, recent advancements in artificial intelligence (AI) offer promising, data-driven solutions that reduce reliance on domain-specific knowledge while delivering fast, accurate predictions by uncovering patterns within dataset. This provides a promising solution towards intelligent CFRP machining process with improved quality and efficiency. To date, there is a lack of comprehensive, up-to-date review of data-driven methods in CFRP machining process prediction/optimization. This review fills this gap and provides a critical analysis of data-driven methods in four key application settings: (i) machining process characteristics and surface quality/defects prediction; (ii) tool wear prediction; (iii) material transition recognition in CFRP/metal stacks machining; (iv) vision-based surface defects recognition. By presenting a state-of-the-art overview of advances, challenges and future research directions, this review highlights the transformative potential of data-driven methods in advancing intelligent CFRP machining within the manufacturing value chain.