A new workspace estimation method for heavy-load parallel kinematic machine considering mechanism deformation and motor loading performance

Abstract The estimation of workspace for parallel kinematic machines (PKMs) typically relies on geometric considerations, which is suitable for PKMs operating under light load conditions. However, when subjected to heavy load, PKMs may experience significant deformation in certain postures, potentially compromising their stiffness. Additionally, heavy load conditions can impact motor loading performance, leading to inadequate motor loading in specific postures. Consequently, in addition to geometric constraints, the workspace of PKMs under heavy load is also constrained by mechanism deformation and motor loading performance. This paper aims at developing a new heavy load 6-PSS PKM for multi-degree of freedom forming process. Additionally, it proposes a new method for estimating the workspace, which takes into account both mechanism deformation and motor loading performance. Initially, the geometric workspace of the machine is predicted based on its geometric configuration. Subsequently, the workspace is predicted while considering the effects of mechanism deformation and motor loading performance separately. Finally, the workspace is synthesized by simultaneously accounting for both mechanism deformation and motor loading performance, and a new index of workspace utilization rate is proposed. The results indicate that the synthesized workspace of the machine diminishes as the load magnitude and load arm increase. Specifically, under a heavy load magnitude of 6000 kN and a load arm of 200 mm, the utilization rate of the synthesized workspace is only 9.9% of the geometric workspace.

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Publication Details

Journal
Robotica
Published
2024-12-26
DOI
https://doi.org/10.1017/s0263574724001814
Primary Topic
Robotic Mechanisms and Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

A new workspace estimation method for heavy-load parallel kinematic machine considering mechanism deformation and motor loading performance

Fangyan Zheng, Wuhao Zhuang, Lin Hua, Jingyu Liu et al.
Robotica
Robotic Mechanisms and Dynamics
article

A new workspace estimation method for heavy-load parallel kinematic machine considering mechanism deformation and motor loading performance

Fangyan Zheng, Wuhao Zhuang, Lin Hua, Jingyu Liu, Xinghui Han, Shuai Xin
article en

Abstract

Abstract The estimation of workspace for parallel kinematic machines (PKMs) typically relies on geometric considerations, which is suitable for PKMs operating under light load conditions. However, when subjected to heavy load, PKMs may experience significant deformation in certain postures, potentially compromising their stiffness. Additionally, heavy load conditions can impact motor loading performance, leading to inadequate motor loading in specific postures. Consequently, in addition to geometric constraints, the workspace of PKMs under heavy load is also constrained by mechanism deformation and motor loading performance. This paper aims at developing a new heavy load 6-PSS PKM for multi-degree of freedom forming process. Additionally, it proposes a new method for estimating the workspace, which takes into account both mechanism deformation and motor loading performance. Initially, the geometric workspace of the machine is predicted based on its geometric configuration. Subsequently, the workspace is predicted while considering the effects of mechanism deformation and motor loading performance separately. Finally, the workspace is synthesized by simultaneously accounting for both mechanism deformation and motor loading performance, and a new index of workspace utilization rate is proposed. The results indicate that the synthesized workspace of the machine diminishes as the load magnitude and load arm increase. Specifically, under a heavy load magnitude of 6000 kN and a load arm of 200 mm, the utilization rate of the synthesized workspace is only 9.9% of the geometric workspace.

RoboticaVol. 43(1)
Wuhan University of Technology (CN)
National Natural Science Foundation of China
Peace, Justice and strong institutions
Openalex Percentile: Top 71%
Robotic Mechanisms and Dynamics
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