Toward Smart and Autonomous Surface Quality Prediction: A Review of AI-Driven Approaches
DOI:
https://doi.org/10.65904/3083-3604.2025.01.08Keywords:
Surface Quality Prediction, Precision Manufacturing, Artificial Intelligence, Transfer Learning, Smart ManufacturingAbstract
As precision manufacturing evolves toward Industry 4.0, the transition from retrospective inspection to real-time Surface Quality Prediction (SQP) has become a critical requirement for autonomous process control. This study presents a systematic review and critical synthesis of Artificial Intelligence (AI)-driven SQP methodologies across machining, grinding, and finishing operations. Using a mixed-method framework combining bibliometric mapping with qualitative content analysis, 38 seminal studies were evaluated to classify current approaches into parameter-based, signal-based, and hybrid architectures. The comparative analysis reveals that hybrid models, which fuse static machining parameters with dynamic multi-sensor feedback, consistently outperform single-source techniques, achieving determination coefficients (R²) exceeding 0.90 and reducing root-mean-squared error (RMSE) to below 10%. Furthermore, the integration of physics-aware features was found to improve prediction accuracy by 25–30%, while transfer learning strategies demonstrated the capacity to reduce training data requirements by approximately 60%, effectively mitigating data scarcity in high-mix production environments. Despite these advances, barriers regarding model explainability, computational latency, and data heterogeneity persist. Consequently, this paper proposes a unified framework that converges process physics, data-driven modeling, and digital twin technologies, establishing a theoretical basis for interpretable, scalable, and sustainable precision manufacturing ecosystems.
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