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.

翻译：精确的表面粗糙度预测对于确保高产品质量至关重要，尤其在制造和航空航天等领域，微小的缺陷都可能影响性能或安全性。然而，由于变量间复杂的非线性相互作用，以及有限且不平衡的数据集，这一任务极具挑战性。现有基于传统机器学习算法的方法需要大量领域知识进行特征工程，并在模型选择上依赖大量人工干预。为解决这些问题，我们提出了NASPrecision，一种神经架构搜索（NAS）驱动的多阶段学习框架。这一创新方法能自主识别适用于不同表面粗糙度预测任务的最优特征与模型，并通过多阶段学习显著提升性能。我们的框架包含三个阶段：1）架构搜索阶段，运用NAS自动识别最有效的模型架构；2）初始训练阶段，训练神经网络以获取初步预测；3）精炼阶段，附加后续模型以修正并捕捉初始训练阶段忽略的细微变化。针对有限且不平衡的数据集，我们采用生成式数据增强技术，通过学习底层数据分布来平衡并生成新数据。我们在与不同加工技术相关的三个真实数据集上进行了实验。结果表明，在平均绝对百分比误差（MAPE）、均方根误差（RMSE）和标准差（STD）上分别实现了18%、31%和22%的提升。这确立了其作为一种鲁棒且通用的精确表面粗糙度预测解决方案的地位，有望在关键行业中提升生产效率和产品质量，同时最大限度地减少对领域专业知识和人工干预的依赖。