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Updated: Oct 22, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
Published on: April 20, 2016
Research on the dynamic hammering method for identifying weak parts in cantilever structures
Tieneng Guo1,2, Lingjun Meng1,2, Cheng Zhou1,3
1Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, China.
This study introduces a new method for identifying structural stiffness weaknesses in machine tools using hammer testing. The Flexibility Matrix Diagonal element method offers higher accuracy in detecting vulnerabilities, even with noise interference.
Area of Science:
- Mechanical Engineering
- Structural Analysis
- Vibration Analysis
Background:
- Improving structural stiffness is critical for machine tool performance.
- Identifying stiffness vulnerabilities is a key challenge in machine tool design.
Purpose of the Study:
- To propose and validate the Flexibility Matrix Diagonal element method for identifying stiffness weaknesses in cantilever structures.
- To develop a novel method for quantifying weakness levels and measuring accuracy under noisy conditions.
Main Methods:
- Hammer testing to obtain dynamic characteristics.
- Finite element simulations to model stiffness weakening scenarios.
- Singular value decomposition to establish the flexibility matrix from experimental data.
Main Results:
- The Flexibility Matrix Diagonal element method effectively identifies single and multiple stiffness weaknesses.
- The proposed quantification method accurately assesses weakness levels under noise.
- Experimental validation on a cantilever beam with elastic support confirmed the method's efficacy.
Conclusions:
- The Flexibility Matrix Diagonal element method is a valid and practical approach for identifying structural stiffness vulnerabilities.
- This method demonstrates superior accuracy compared to the Flexibility Difference Method, especially under noisy conditions.
- The study highlights the method's significant practical engineering value for machine tool optimization.
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