Interest in applying data-driven approaches in manufacturing has grown significantly, particularly for mapping complex, high-dimensional relationships. The milling process is one area where predictive models can link influential parameters to surface roughness metrics prior to in situ operations. While this approach offers clear advantages, it faces challenges due to limited datasets and robustness issues in inverse design paradigms. To address these challenges, this paper proposes a machine learning (ML)-based framework for the inverse design of the surface milling process, with a focus on surface roughness as the design objective. The framework employs forward training of two ML models, a deep neural network (DNN) and a random forest (RF) ensemble, both developed using a high-fidelity synthetic dataset generated from a computational simulation framework. These trained models are integrated into a Bayesian optimization (BO) procedure to overcome the multiplicity problem arising from the many-to-one mapping inherent in the dataset. The approach identifies top-performing milling process configurations, considering both process and tool parameters, and presents them from the full solution space. The models achieve average relative errors below 5% when compared to reference results, thereby demonstrating the robustness and reliability of the proposed methodology.

翻译：将数据驱动方法应用于制造业的兴趣显著增长，特别是在映射复杂高维关系方面。铣削过程是其中预测模型可在实际加工前将影响参数与表面粗糙度指标关联起来的领域之一。虽然这种方法具有明显优势，但由于数据集有限和逆向设计范式中鲁棒性问题，其面临挑战。为解决这些问题，本文提出一种基于机器学习（ML）的框架，用于表面铣削过程的逆向设计，重点关注表面粗糙度作为设计目标。该框架采用两个ML模型的前向训练，即深度神经网络（DNN）和随机森林（RF）集成，两者均基于计算仿真框架生成的高保真合成数据集进行开发。将这些训练好的模型整合到贝叶斯优化（BO）流程中，以克服数据集中固有的多对一映射所带来的多解性问题。该方法在考虑工艺参数和刀具参数的情况下，从完整解空间中识别出性能最优的铣削过程配置。与参考结果相比，模型平均相对误差低于5%，从而证明了所提方法的鲁棒性和可靠性。