Research on the Multi-Objective Optimal Design of Adjusting Mechanisms Considering Force Transmission Performance

For the guide vane adjusting mechanism, precision represents the primary design requirement. Meanwhile, due to the presence of aerodynamic loads under actual operating conditions, stagnation forces emerge that affect the mechanism motion characteristics, including the response speed and precision. This paper establishes kinematic and static analysis models of the guide vane adjusting mechanism through analytical modeling methods, investigates analytical approaches for mechanism adjustment precision and stagnation force, and conducts error and sensitivity analyses of the mechanism parameters based on these analytical models. Building upon this foundation, an optimization design method integrating adjustment precision and force transmission performance is proposed using a multi-objective genetic algorithm. Optimizing the critical design parameters, such as the mechanism dimensions and positions, can enhance both the adjustment precision and force transmission performance. Through case studies, significant reductions in motion precision errors and the peak stagnation force and maximum differences in stagnation force were achieved, validating the feasibility of this optimization design approach.

Authors

Institutions

Publication Details

Journal
Machines
Published
2025-05-14
DOI
https://doi.org/10.3390/machines13050410
Citations
1
Primary Topic
Industrial Technology and Control Systems
Type
article
Field-Weighted Citation Impact
1.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the Multi-Objective Optimal Design of Adjusting Mechanisms Considering Force Transmission Performance

Mingxin Shan, Yebing Tian, Jingyu Zhai, Yang Qi et al.
1 citations
Machines
Industrial Technology and Control Systems
1.21
article

Research on the Multi-Objective Optimal Design of Adjusting Mechanisms Considering Force Transmission Performance

Mingxin Shan, Yebing Tian, Jingyu Zhai, Yang Qi, Guan Bo-ran, Wei Sun
article en
1 citations

Abstract

For the guide vane adjusting mechanism, precision represents the primary design requirement. Meanwhile, due to the presence of aerodynamic loads under actual operating conditions, stagnation forces emerge that affect the mechanism motion characteristics, including the response speed and precision. This paper establishes kinematic and static analysis models of the guide vane adjusting mechanism through analytical modeling methods, investigates analytical approaches for mechanism adjustment precision and stagnation force, and conducts error and sensitivity analyses of the mechanism parameters based on these analytical models. Building upon this foundation, an optimization design method integrating adjustment precision and force transmission performance is proposed using a multi-objective genetic algorithm. Optimizing the critical design parameters, such as the mechanism dimensions and positions, can enhance both the adjustment precision and force transmission performance. Through case studies, significant reductions in motion precision errors and the peak stagnation force and maximum differences in stagnation force were achieved, validating the feasibility of this optimization design approach.

MachinesVol. 13(5)
Dalian University of Technology (CN)
Openalex Percentile: Top 22%
Industrial Technology and Control Systems
1.21
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.

Research on the Multi-Objective Optimal Design of Adjusting Mechanisms Considering Force Transmission Performance — Mingxin Shan, Yebing Tian, et al. · Machines (2025) | TGRS Research Map | TGRS