With the emergence of data-driven approaches in science, there is growing interest in their application to manufacturing, particularly in surface precision engineering. However, generating large datasets required for model training is often impractical experimentally due to high costs and the time-intensive nature of measurements. High-fidelity synthetic datasets offer a viable alternative if they can be generated both efficiently and accurately. To address this challenge, this paper presents an efficient framework for generating accurate 3D surface topographies and roughness indicators in milling operations using numerical methods. First, a conventional topography prediction model is developed based on the forward solution method (FSM). Building on this, an optimized computational algorithm is proposed to establish an efficient FSM with significantly improved performance. The model is validated against two independent sets of experimental results, assessing both prediction accuracy and computational efficiency. The results demonstrate acceptable prediction errors and an average computational speedup of 42.2x. The proposed open-source model provides a generalizable framework for large-scale analysis, enabling the generation of extensive datasets for data-driven surrogate modeling.

翻译：随着数据驱动方法在科学领域的兴起，其应用于制造业（尤其是表面精密工程领域）的研究日益受到关注。然而，由于实验测量成本高昂且耗时，为模型训练生成大规模数据集往往不切实际。若能高效且精确地生成高保真合成数据集，将为此提供可行的替代方案。针对这一挑战，本文提出了一种基于数值方法高效生成铣削加工精确三维表面形貌与粗糙度指标的框架。首先，基于正向求解法（FSM）构建了传统形貌预测模型。在此基础上，提出一种优化计算算法以建立性能显著提升的高效FSM模型。通过两组独立实验结果对模型进行验证，评估了其预测精度与计算效率。结果表明，该模型预测误差可接受，平均计算加速比达42.2倍。所提出的开源模型为大规模分析提供了通用框架，能够生成用于数据驱动代理建模的广泛数据集。