At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared to multiple independent Bayesian linear regression models to showcase the benefits of partial pooling in a machining setting with respect to prediction accuracy and uncertainty quantification.

翻译：目前，大多数表面质量预测方法仅能执行单任务预测，这导致数据集利用不充分、重复劳动以及实验成本增加。为应对这一问题，作者提出了一种贝叶斯层次模型，用于预测车削加工过程中的表面粗糙度测量值。该层次模型与多个独立的贝叶斯线性回归模型进行了对比，以展示在加工场景中部分池化对预测精度和不确定性量化方面的优势。