Surface-Roughness Prediction Based on Small-Batch Workpieces for Smart Manufacturing: An Aerospace Robotic Grinding Case Study

Abstract

Small-batch workpieces in smart manufacturing demand process parameter modeling, but existing models lack analysis across varying sample sizes and runtime conditions. This study proposes a novel surface-roughness prediction method, Response Surface Methodology-BP Neural Network (RSM-BPNN), designed for experimental data from single small-batch workpieces with varying sample sizes. First, polynomial feature transformation and selection are performed based on the proposed process parameters to improve the feature quality of input data. Second, a Dynamic Central Composite Design-Response Surface Methodology (DCCD-RSM) determines the optimal experimental region and fits surface roughness, while a BPNN trains a deep learning model for prediction. The BPNN fusion method combines both approaches to create a general, adaptive predictive model for surface roughness. Finally, the accuracy and practicality of the BPNN model were verified through reverse calculation and parameter optimization in actual robot grinding experiments. The model demonstrated good predictive performance for surface roughness in aluminum alloy grinding, providing reliable guidance for surface quality prediction and process parameter optimization in small-batch workpieces within the context of smart manufacturing.