Modal-Validated Topology Optimization of a Vertical Grinding Machine and Predictive Diagnostics

Finite element analysis (FEA) is used to investigate the modal characteristics of a vertical grinding machine, with the aim of aligning simulation conditions with real-world phenomena. Experiments were conducted using a percussion hammer, accelerometer, and spectrum analyzer to validate the findings. The analysis covered a frequency range of 1Hz to 300Hz, identifying structural frequencies of 64.3Hz, 93.4Hz, and 214.2Hz through simulation, and 76.8Hz, 87.0Hz, and 248.0Hz from experiments. Experimental data, processed using NOVIAN spectrum analysis software for fast Fourier transforms (FFT), provided detailed insights into frequency response functions, spectra, and correlations. An impact hammer with an ultra-soft rubber tip generated excitation, and the accelerometer captured the structural response. Simulation matched experiment within 16.2%, 7.3%, and 13.6% for the first three modes, and within 7.7%, 1.7%, and 0.3% against ODS measurements. Component-level topology optimization under a 90% volume constraint then reduced machine mass by 39.5 kg (2.4%), lowered spindle-to-worktable deformation from 19.2 μm to 18.6 μm (3.1%), and raised the first three natural frequencies by 8.7%, 19.5%, and 5.7%; the base alone contributed 33.5 kg (3.9%) while gaining 38.4% in first-mode frequency. Shifting the second mode from 93.4 Hz to 111.6 Hz widens the margin against 6000 rpm spindle excitation from 6.6% to 11.6%, and a PCA-GMM diagnostic system distinguished healthy from abnormal spindle states.

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Publication Details

Journal
Journal of Advanced Manufacturing Systems
Published
2026-10-06
DOI
https://doi.org/10.1142/s0219686728500394
Primary Topic
Mechanical Engineering Research and Applications
Type
article
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article

Modal-Validated Topology Optimization of a Vertical Grinding Machine and Predictive Diagnostics

Umar Farooq, Sheng-Lun Huang, Li-Yuan Chang, Tzu-Chi Chan
Journal of Advanced Manufacturing Systems
Mechanical Engineering Research and Applications
article

Modal-Validated Topology Optimization of a Vertical Grinding Machine and Predictive Diagnostics

Umar Farooq, Sheng-Lun Huang, Li-Yuan Chang, Tzu-Chi Chan
article en

Abstract

Finite element analysis (FEA) is used to investigate the modal characteristics of a vertical grinding machine, with the aim of aligning simulation conditions with real-world phenomena. Experiments were conducted using a percussion hammer, accelerometer, and spectrum analyzer to validate the findings. The analysis covered a frequency range of 1Hz to 300Hz, identifying structural frequencies of 64.3Hz, 93.4Hz, and 214.2Hz through simulation, and 76.8Hz, 87.0Hz, and 248.0Hz from experiments. Experimental data, processed using NOVIAN spectrum analysis software for fast Fourier transforms (FFT), provided detailed insights into frequency response functions, spectra, and correlations. An impact hammer with an ultra-soft rubber tip generated excitation, and the accelerometer captured the structural response. Simulation matched experiment within 16.2%, 7.3%, and 13.6% for the first three modes, and within 7.7%, 1.7%, and 0.3% against ODS measurements. Component-level topology optimization under a 90% volume constraint then reduced machine mass by 39.5 kg (2.4%), lowered spindle-to-worktable deformation from 19.2 μm to 18.6 μm (3.1%), and raised the first three natural frequencies by 8.7%, 19.5%, and 5.7%; the base alone contributed 33.5 kg (3.9%) while gaining 38.4% in first-mode frequency. Shifting the second mode from 93.4 Hz to 111.6 Hz widens the margin against 6000 rpm spindle excitation from 6.6% to 11.6%, and a PCA-GMM diagnostic system distinguished healthy from abnormal spindle states.

Journal of Advanced Manufacturing Systems
Openalex Percentile: Top 21%
Mechanical Engineering Research and Applications
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Modal-Validated Topology Optimization of a Vertical Grinding Machine and Predictive Diagnostics — Umar Farooq, Sheng-Lun Huang, et al. · Journal of Advanced Manufacturing Systems (2026) | TGRS Research Map | TGRS