Analysis of Ultrasonic Machining for Surface Characteristics of Reinforced Glass Material

Abstract

Introduction

Ultrasonic Machining (USM) is a non-traditional machining method where abrasive slurry flows between a vibrating tool and the workpiece, removing material through abrasion. Key characteristics:

• Tool vibrates at ultrasonic frequencies (>20 kHz)

• No direct tool contact with workpiece → minimal heat

• Ideal for hard and brittle materials: glass, ceramics, diamond, etc.

• Uses abrasive slurry (e.g., boron carbide, silicon carbide, aluminum oxide)

2. Working Principle & Components

• A magnetostrictive transducer converts electrical energy into mechanical vibration.

• Vibrations are amplified via a concentrator (waveguide).

• The vibrating tool transmits force through slurry, causing microscopic chipping of the workpiece.

• Tool does not cut directly but facilitates material removal via abrasive impact.

Important Design Considerations:

• Tool material: Must be tough and ductile (e.g., low-carbon/stainless steel)

• Tool length: Typically <25 mm; long tools reduce machining efficiency

• Slurry concentration: Typically 30–60%; adjusted for tool area

• Grit size: 200–400 for roughing, 800–1000 for finishing

• Abrasive: Boron carbide is most effective but costly

3. Key Machining Parameters

USM performance is influenced by:

• Amplitude of tool oscillation (a?)

• Frequency (f)

• Tool material

• Abrasive type and grit size

• Feed force (F)

• Contact area (A)

• Abrasive concentration (C)

• Hardness ratio (λ = σw/σt)

4. Experimental Methodology

Objective:

To study the effects of parameters on:

• Material Removal Rate (MRR)

• Surface Roughness

• Tool Wear Rate

Input Factors:

Design Approach:

• Taguchi L18 orthogonal array used for experimental design

• Allows efficient analysis with limited runs

• Response measured using Signal-to-Noise (S/N) ratio

5. Results & Analysis

Key Findings from ANOVA on MRR:

• Amplitude was the most impactful parameter on MRR.

• Tool material and slurry concentration also had significant effects.

• Frequency and its interaction with tool material were statistically insignificant.

Optimal Conditions for Maximum MRR:

• Tool: Titanium

• Amplitude: 50 µm

• Slurry Concentration: 40%

• Frequency (S/N-based): 25 kHz

Conclusion

The effect of parameters i.e. tool material, frequency, amplitude and concentration were evaluated using ANOVA design analysis and Regression analysis. The purpose of the ANOVA was to identify the important parameters in prediction of MRR. Some results consolidated from ANOVA and plots are given below The effect of parameters i.e. tool material, frequency, amplitude and conc. were evaluated using ANOVA and factorial design analysis. A confidence interval of 95% has been used for the analysis. Two repetitions for each 18 trails were completed to measure the Signal to Noise ratio (S/N Ratio). ANOVA table shows that tool material with F value 4.25, amplitude with F value 36.08 and concentration 5.74 are the factors that significantly affect the MRR, with % contribution of 2.9%, 67.27 % and 8.61% to MRR The other factor frequency was found to be insignificant. For S/N ratio frequency, amplitude and concentration are significant to reduce the variation of MRR. So the confidence interval around the MRR is given by 8.52 ± 1.43 mm3/min. The present study was carried out to study the effect of input parameters on the MRR and surface roughness and tool wear rate. The following conclusions have been drawn from the study:

References

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Copyright

Copyright © 2025 Raminder Singh, Rahul Agarwal, Rahul Saxena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.