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Devesh Ojha,
Rajendra Kumar Srivastava,
- Assistant Professor, Department of Civil Engineering, Amity University, Lucknow, India
- Professor, Department of Civil Engineering, Motilal Nehru National Institute of Technology, Allahabad, India
Abstract
This study proposes a new integration of the experimental analysis, multi-physics finite element
modelling (FEM) and machine learning (ML) optimisation of contact temperature (CT) in ultrasonic
vibration-assisted single pole magnetic abrasive finishing (UV-SPMAF) of zinc alloy. The three gaps of
the research are addressed: (i) The absence of a multi-physics FEM model that can couple
electromagnetic, thermal and structural fields for UV-SPMAF of zinc; (ii) No quantified contribution of
the ultrasonic frequency and amplitude to CT; and (iii) No ML-driven predictive model for CT in this
hybrid process. The L-18 Taguchi orthogonal array was used, which contained six parameters: tool
speed (250–1250 RPM), working gap (4–6 mm), feed rate (1.5–5.5 mm/s), pulse on time (1–5 min),
ultrasonic frequency (20–40 kHz), and amplitude (5–15 µm). ANOVA revealed that working gap
(44.21%) and tool speed (34.33%) were dominant factors, while UV frequency (4.16%) and amplitude
(2.84%) are statistically significant (p < 0.05). UV assistance gives a decrease in CT of 6.5–9.1% and an
increase in the surface roughness of up to 14.2%. The periodic shear-stress relief occurs under UV
vibrations as confirmed by FEM (R² = 0.961, RMSE = 0.31°C). For the prediction of CT, R² = 0.9814
was obtained by ANN. Optimal parameters yield CT = 25.71 ± 0.48°C with 51.72% SR improvement.
The resultant surface is found to have uniform micro-strains by SEM and XRD.
Keywords: UV-SPMAF; contact temperature; FEM; ANN; zinc alloy; ANOVA; Taguchi; surface roughness; thermal management.
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| Volume | 04 | |
| 02 | ||
| Received | 12/06/2026 | |
| Accepted | 14/07/2026 | |
| Published | 19/08/2026 | |
| Publication Time | 68 Days |