An efficient open-source framework for high-fidelity 3D surface topography and roughness prediction in milling

TL;DR

This paper introduces an efficient open-source framework that accurately predicts 3D surface topography and roughness in milling, significantly reducing computation time and facilitating large-scale data generation for data-driven modeling.

Contribution

It develops an optimized computational algorithm for the forward solution method, enabling fast and accurate synthetic data generation for manufacturing surface analysis.

Findings

Achieves an average computational speedup of 42.2x.

Demonstrates acceptable prediction errors against experimental data.

Provides a generalizable open-source framework for large-scale surface data generation.

Abstract

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.