A novel approach to vibratory finishing: Double vibro-polishing

Ahluwalia, K. S.; Mediratta, Rijul; Yeo, Song Huat

doi:10.1080/10426914.2016.1232812

Cited by 20 publications

(15 citation statements)

References 20 publications

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“…Following the model trends in the generated curves shown in Figure 9, it can be inferred that the surface roughness reduction time decreases if the bowl frequency is increased from 3000 rpm to 4500 rpm, and also if the secondary vibratory fixture is turned on. This is consistent with the observation of Ahluwalia et al [7] where the vibrating fixture helps in finishing the workpieces in a faster rate, thus it can be used a valid model.…”

Section: Family Of Curves Generationsupporting

confidence: 92%

“…In most conventional vibratory machining processes, components are either freely flowing together with the vibratory media particles or held into place in a stationary fixture. Ahluwalia et al [7] introduced a new vibropolishing method, coined double-vibropolishing. Instead of a stationary fixture, vibration was introduced to the fixture to increase the relative motion of particles to the workpiece.…”

Section: Double Vibro-polishingmentioning

confidence: 99%

“…Similar to the experiments done by Ahluwalia et al [7], Ti-6Al-4V workpieces were subjected to double-vibropolishing in a vibratory bowl to study several factors that affect the average surface roughness. Specifically, the independent variables studied were:…”

Section: Acquisition and Pre-processing Of Datamentioning

confidence: 99%

“…This study aims to derive an empirical model to predict surface roughness of a workpiece subjected to a vibratory finishing process. Moreover, a relatively new class of fixture vibratory finishing process called double vibro-polishing [7], has reported to produce significant improvements with cycle times. The abundance of experimental data has potential to be used in numerical modelling to produce a versatile model that could be used by industry to save time on experiments as well as catalogue various components against the parameters for vibratory finishing.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Models of Double-Vibropolishing in Bowl System Using Artificial Intelligence Methods

Alcaraz

Ahluwalia²,

Yeo

2019

JMMP

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Vibratory finishing is a versatile and efficient surface finishing process widely used to finish components of various functionalities. Research efforts were focused in fundamental understanding of the process through analytical solutions and simulations. On the other hand, predictive modelling of surface roughness using computational intelligence (CI) methods are emerging in recent years, though CI methods have not been extensively applied yet to a new vibratory finishing method called double-vibropolishing. In this study, multi-variable regression, artificial neural networks, and genetic programming models were designed and trained with experimental data obtained from subjecting rectangular Ti-6Al-4V test coupons to double vibropolishing in a bowl system configuration. Model selection was done by comparing the mean-absolute percentage error and r-squared values from both training and testing datasets. Exponential regression was determined as the best model for the bowl double-vibropolishing system studied with a Test MAPE score of 6.1% and a R-squared score of 0.99. A family of curves was generated using the exponential regression model as a potential tool in predicting surface roughness with time.

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Section: Family Of Curves Generationsupporting

confidence: 92%

Section: Double Vibro-polishingmentioning

confidence: 99%

Section: Acquisition and Pre-processing Of Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Models of Double-Vibropolishing in Bowl System Using Artificial Intelligence Methods

Alcaraz

Ahluwalia²,

Yeo

2019

JMMP

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“…5.The variance analysis of MRR was shown in Table 7. (9,13), FL<F0.05 (5,8). Therefore, the regression model was significant and the lack of fit model was not significant.…”

Section: Fig 3 Proportion Of the Influence Of Parameters On Mrrmentioning

confidence: 89%

Material Removal Rate of Double-faced Mechanical Polishing of 4H-SiC Substrate

Zhang

Yang

Qiu

2021

Preprint

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Silicon carbide (SiC) has been a promising the-third-generation semiconductor power device material for high-power, high-temperature, substrate applications. It aims to improve the material removal rate (MRR), on the premise of ensuring the surface roughness requirements of the double-faced mechanical polishing of 6-inch SiC substrate. To obtain the relationship between any point on SiC substrate and polishing pads, the model about double-faced mechanical polishing has been built and the kinematics equations were created. Best optimized material removal rate parameters were obtained. MRR reached the maximum when speed rate of the outside ring gear to the inside sun gear m=-1, speed rate of lower plate to the inside sun gear n=5, SiC substrate distribution radius RB=75. The primary and secondary order of MRR (n>m>RB) was obtained. An accurate mathematical model of orthogonal rotary regression test of Tri-factor quadratic of MRR was established and the regression model was significant. Surface quality of SiC substrate was observed and characterized with SEM and AFM. It greatly provides a key guarantee for the next process of CMP, confirmed the importance of MRR to ultra-smooth polishing, and provides a guarantee for its application in semiconductor equipment and technology.

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