Reliability Prediction Model for Ball Screws Considering Full-Life Fatigue Damage

This paper addresses the challenges of life prediction and reliability assessment for ball screws under complex operating conditions by proposing a reliability prediction model that incorporates full-life fatigue damage. First, a full-life fatigue life prediction model encompassing the three stages of crack initiation, propagation, and fatigue cumulative spalling is developed. This model comprehensively considers the effects of material properties, geometric parameters, and loading history, enabling a systematic description of the fatigue process of ball screws from initial use to final failure. Based on this life prediction model, an enhanced adaptive Kriging–Monte Carlo simulation (E-AK-MCS) method is introduced to construct a surrogate model, which efficiently solves the high-dimensional nonlinear limit state function, thereby enabling accurate reliability assessment and parameter sensitivity analysis. Experimental results demonstrate that the proposed model achieves an average life prediction accuracy of 94.15% for the 8020 and 5005 specification ball screws, indicating its preliminary engineering applicability under the tested conditions. Reliability analysis indicates that ball diameter fracture toughness, and initial crack size are key factors influencing service reliability. This research provides systematic theoretical methods and technical support for the accurate life prediction, reliability design, and process optimization of ball screws.

Authors

Institutions

Publication Details

Journal
Lubricants
Published
2026-06-30
DOI
https://doi.org/10.3390/lubricants14070257
Primary Topic
Fatigue and fracture mechanics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reliability Prediction Model for Ball Screws Considering Full-Life Fatigue Damage

Chang-Guang Zhou, Bohao Meng, Chao Luo, Maocheng Jiang et al.
Lubricants
Fatigue and fracture mechanics
article

Reliability Prediction Model for Ball Screws Considering Full-Life Fatigue Damage

Chang-Guang Zhou, Bohao Meng, Chao Luo, Maocheng Jiang, Jun Xu, Hutian Feng
article en

Abstract

This paper addresses the challenges of life prediction and reliability assessment for ball screws under complex operating conditions by proposing a reliability prediction model that incorporates full-life fatigue damage. First, a full-life fatigue life prediction model encompassing the three stages of crack initiation, propagation, and fatigue cumulative spalling is developed. This model comprehensively considers the effects of material properties, geometric parameters, and loading history, enabling a systematic description of the fatigue process of ball screws from initial use to final failure. Based on this life prediction model, an enhanced adaptive Kriging–Monte Carlo simulation (E-AK-MCS) method is introduced to construct a surrogate model, which efficiently solves the high-dimensional nonlinear limit state function, thereby enabling accurate reliability assessment and parameter sensitivity analysis. Experimental results demonstrate that the proposed model achieves an average life prediction accuracy of 94.15% for the 8020 and 5005 specification ball screws, indicating its preliminary engineering applicability under the tested conditions. Reliability analysis indicates that ball diameter fracture toughness, and initial crack size are key factors influencing service reliability. This research provides systematic theoretical methods and technical support for the accurate life prediction, reliability design, and process optimization of ball screws.

LubricantsVol. 14(7)
Nanjing University of Science and Technology (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Fundamental Research Funds for the Central Universities
Responsible consumption and production
Openalex Percentile: Top 13%
Fatigue and fracture mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Reliability Prediction Model for Ball Screws Considering Full-Life Fatigue Damage — Chang-Guang Zhou, Bohao Meng, et al. · Lubricants (2026) | TGRS Research Map | TGRS