RESEARCH PAPER
A surface quality analysis method for CNC milling based on an improved Z-Map algorithm
1
Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China, China
2
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, Jilin Province, China, China
3
Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun 130025, Jilin Province, China, China
4
College of Robotics, Beijing Union University, Beijing, 100027, China
5
Beijing University of Technology, Beijing 100124, China, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2025-12-09
Final revision date: 2026-05-25
Acceptance date: 2026-06-25
Online publication date: 2026-07-10
Corresponding author
Ziling Zhang
Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China, China
Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China, China
KEYWORDS
improved Z-Map algorithmmilled surface topographyIBOA-RBFNN-MC reliability modelroughness reliability
TOPICS
ABSTRACT
Demands for superior surface quality in high-end components make precise analysis and reliable assessment critical. This paper presents an integrated method combining improved morphology simulation with an intelligent evaluation framework. Specifically, the Z-Map algorithm is enhanced by particle swarm optimization for efficient, high-precision surface topography simulation. The high-fidelity simulation data provides an effective database for training the subsequent intelligent prediction model. The simulated data then trains a radial basis function neural network (RBFNN) optimized by an improved butterfly optimization algorithm (IBOA) for roughness prediction, achieving over 99% accuracy. This model serves as a surrogate in Monte Carlo simulations for reliability analysis (IBOA-RBFNN-MC). Results show this approach outperforms benchmarks and aligns closely with experimental measurements, validating its accuracy for process optimization and quality control.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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