An adaptive cooperative swarm optimization method for noise-robust cylindricity evaluation in signal processing

TY - JOUR

T1 - An adaptive cooperative swarm optimization method for noise-robust cylindricity evaluation in signal processing

AU - Yang, Peiyi

AU - Liu, Zhibing

AU - Li, Jia

AU - Qiu, Tianyang

AU - Shi, Dan

AU - Tu, Wenxuan

AU - Zhao, Bin

AU - Shen, Wenhua

AU - Wang, Xibin

N1 - Publisher Copyright: © 2026 Elsevier Ltd

PY - 2026/6/9

Y1 - 2026/6/9

N2 - Accurate cylindricity evaluation is imperative for ensuring dimensional integrity and process stability in precision manufacturing. However, high-density measurement signals are often affected by outliers, noise, and multi-source uncertainty, leading to biased assessments. The present study proposes an Adaptive Cooperative Particle Swarm Optimization (ACPSO) framework for robust Minimum-Zone Cylindricity (MZCY) evaluation. The method integrates local-region outlier suppression, adaptive parameter adjustment, and axial-direction constraint into a unified optimization scheme. The measurement data are segmented into angular subregions, and percentile-based thresholds are applied to remove abnormal signals. The refined data are then processed through a cooperative multi-swarm optimization, where adaptive learning parameters dynamically balance exploration and convergence. The findings, which are supported by simulation and experimental results, including comparisons with APSO, PSO, and RPM-APSO, and validation using a Taylor Hobson Talyrond-2000, demonstrate that ACPSO achieves higher precision, lower uncertainty, and better robustness. The proposed framework provides an efficient and reliable approach for high-fidelity cylindricity assessment in intelligent metrology.

AB - Accurate cylindricity evaluation is imperative for ensuring dimensional integrity and process stability in precision manufacturing. However, high-density measurement signals are often affected by outliers, noise, and multi-source uncertainty, leading to biased assessments. The present study proposes an Adaptive Cooperative Particle Swarm Optimization (ACPSO) framework for robust Minimum-Zone Cylindricity (MZCY) evaluation. The method integrates local-region outlier suppression, adaptive parameter adjustment, and axial-direction constraint into a unified optimization scheme. The measurement data are segmented into angular subregions, and percentile-based thresholds are applied to remove abnormal signals. The refined data are then processed through a cooperative multi-swarm optimization, where adaptive learning parameters dynamically balance exploration and convergence. The findings, which are supported by simulation and experimental results, including comparisons with APSO, PSO, and RPM-APSO, and validation using a Taylor Hobson Talyrond-2000, demonstrate that ACPSO achieves higher precision, lower uncertainty, and better robustness. The proposed framework provides an efficient and reliable approach for high-fidelity cylindricity assessment in intelligent metrology.

KW - Adaptive optimization

KW - Cylindricity error

KW - Manufacturing metrology

KW - Outlier suppression

KW - Signal processing

UR - https://www.scopus.com/pages/publications/105036581998

U2 - 10.1016/j.measurement.2026.121579

DO - 10.1016/j.measurement.2026.121579

M3 - Article

AN - SCOPUS:105036581998

SN - 0263-2241

VL - 277

JO - Measurement: Journal of the International Measurement Confederation

JF - Measurement: Journal of the International Measurement Confederation

M1 - 121579

ER -