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

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.

Figures (11)

• Figure 1: (a) Indexable face milling cutter with two inserts. The inserts have a radius $R$, and the interest point $P$ is located on the engaged cutting-edge. (b) Four coordinate systems are defined: the cutting-edge, tool, spindle, and workpiece coordinate systems. The point $P$ on the cutting-edge moves during the milling operation, and its trajectory is obtained in the workpiece coordinate system using the kinematic transformation equations between the coordinate systems. (c) Axial and radial run-outs, with the axial and radial rake angles of the tool. (d) Discretization of the workpiece. The workpiece is discretized into grids with sampling points located within each grid and along the sampling boundaries. The $z$ represents the depth of cut in the milling process. Additionally, the topography generation process within each sampling boundary is presented.
• Figure 2: Flowchart of the FSM algorithm.
• Figure 3: Architecture of the EFSM.
• Figure 4: Overview of the surf-topo framework workflow. Simulation cases are specified using JSON configuration files that define individual experiments and dataset settings. These configurations are executed by the C++ simulation interface (surftopo.cpp), which invokes the core solver implemented in simulation.hpp and simulation.cpp. The solver is compiled via CMake into a Python-accessible module ( surftopo.so). Python utilities are then used for dataset generation, inspection, and visualization of simulation results.
• Figure 5: Experimental setup. (a) Additively manufactured workpiece and its dimensions. (b) Face milling process. (c) Two passes of the tool on the workpiece during the milling operation. (d) Face mill cutter used in the operation, equipped with four inserts, along with the tool and insert specifications.