  Accurate surface roughness prediction is critical for ensuring high product
quality, especially in areas like manufacturing and aerospace, where the
smallest imperfections can compromise performance or safety. However, this is
challenging due to complex, non-linear interactions among variables, which is
further exacerbated with limited and imbalanced datasets. Existing methods
using traditional machine learning algorithms require extensive domain
knowledge for feature engineering and substantial human intervention for model
selection. To address these issues, we propose NASPrecision, a Neural
Architecture Search (NAS)-Driven Multi-Stage Learning Framework. This
innovative approach autonomously identifies the most suitable features and
models for various surface roughness prediction tasks and significantly
enhances the performance by multi-stage learning. Our framework operates in
three stages: 1) architecture search stage, employing NAS to automatically
identify the most effective model architecture; 2) initial training stage,
where we train the neural network for initial predictions; 3) refinement stage,
where a subsequent model is appended to refine and capture subtle variations
overlooked by the initial training stage. In light of limited and imbalanced
datasets, we adopt a generative data augmentation technique to balance and
generate new data by learning the underlying data distribution. We conducted
experiments on three distinct real-world datasets linked to different machining
techniques. Results show improvements in Mean Absolute Percentage Error (MAPE),
Root Mean Square Error (RMSE), and Standard Deviation (STD) by 18%, 31%, and
22%, respectively. This establishes it as a robust and general solution for
precise surface roughness prediction, potentially boosting production
efficiency and product quality in key industries while minimizing domain
expertise and human intervention.
