Issue |
Matériaux & Techniques
Volume 107, Number 6, 2019
|
|
---|---|---|
Article Number | 603 | |
Number of page(s) | 9 | |
Section | Sélection des matériaux et des procédés / Materials and processes selection | |
DOI | https://doi.org/10.1051/mattech/2020008 | |
Published online | 24 March 2020 |
Regular Article
Modelling and optimization of technological parameters in hot abrasive jet machining of alumina ceramic
Department of Production Engineering, Veer Surendra Sai University of Technology,
Burla
768018, India
Received:
26
September
2018
Accepted:
20
January
2020
The present work focuses on the experimental investigation of hot abrasive jet machining (HAJM) and precision drilling operation on flat surfaces of K-60 alumina ceramic material using different grades of silicon carbide abrasives. The machining AJM setup is designed based on fluidized bed mixing chamber along with pressurized powder feed chamber. The experiments are performed as per Box-Behnken design of experiments (BBDOEs) with four process parameters (pressure, stand of distance, abrasive temperature and grain size) for parametric optimization in order to control the two technological response characteristics (material removal rate, flaring diameter) of the precision holes on K-60 alumina. Analysis of variance (ANOVA), response surface methodology (RSM) and genetic algorithm (GA) are subsequently proposed for predictive modelling and process optimization. Result shows that application of hot abrasives in AJM process has excellent performance in terms of improved material removal rate, and minimum dimensional deviation of drilled hole. Multi-response optimization GA technique presented the optimal setting of machining variables in HAJM process at air pressure of 6.682 kgf/cm2, abrasive temperature of 60.6 °C, stand-off-distance of 7.1124 mm, abrasive grain size of 275.755 µm, with estimated maximal material removal rate of 0.005 gm/s and minimal flaring diameter of 6.382 mm. The methodology described here is expected to be highly beneficial to manufacturing industries.
Key words: HAJM / K-60 alumina / RSM / ANOVA / genetic algorithm
© SCF, 2020
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