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Abrasive Jet Drilling of Hard Alumina Flat: An Experimental Investigation and Predictive Modeling by ANN


Affiliations
1 College of Engineering and Management, Kolaghat, Purba Medinipur, West Bengal Kalyani Government Engineering College, Kalyani, West Bengal, India
2 Kalyani Government Engineering College, Kalyani, West Bengal, India
3 College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
     

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Abrasive jet machining (AJM) is often applied in drilling of hard and brittle ceramic materials, and is also used for other processes like surface preparation, deburring, shot-peening, polishing, etc. AJM process parameters need be appropriately selected to have optimized responses like MRR, nozzle wear, etc. Experimental investigation is performed in this work by precisely controlling abrasive flow rate. Along with system pressure, abrasive flow rate, stand-off distance (SOD) and grain size are considered during performing AJM with silicon carbide abrasive on commercially pure 4 mm thick alumina tiles using response surface methodology (RSM). Analysis of variance is done to detect the relative significance of each of the variables. Artificial neural network (ANN) is constructed to estimate the response in AJM based on input parameters. Estimation of machining performance is effectively carried out by ANN based on the training data with less than 8% estimation error, particularly for MRR and NWR.

Keywords

Abrasive Jet Machining (AJM), Alumina, Drilling, ANN, Estimation
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  • Abrasive Jet Drilling of Hard Alumina Flat: An Experimental Investigation and Predictive Modeling by ANN

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Authors

Deb Kumar Adak
College of Engineering and Management, Kolaghat, Purba Medinipur, West Bengal Kalyani Government Engineering College, Kalyani, West Bengal, India
Prosenjit Dutta
Kalyani Government Engineering College, Kalyani, West Bengal, India
Barun Haldar
College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Santanu Das
Kalyani Government Engineering College, Kalyani, West Bengal, India
Naser Abdulrahman Alsaleh
College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Sibsankar Dasmahapatra
Kalyani Government Engineering College, Kalyani, West Bengal, India

Abstract


Abrasive jet machining (AJM) is often applied in drilling of hard and brittle ceramic materials, and is also used for other processes like surface preparation, deburring, shot-peening, polishing, etc. AJM process parameters need be appropriately selected to have optimized responses like MRR, nozzle wear, etc. Experimental investigation is performed in this work by precisely controlling abrasive flow rate. Along with system pressure, abrasive flow rate, stand-off distance (SOD) and grain size are considered during performing AJM with silicon carbide abrasive on commercially pure 4 mm thick alumina tiles using response surface methodology (RSM). Analysis of variance is done to detect the relative significance of each of the variables. Artificial neural network (ANN) is constructed to estimate the response in AJM based on input parameters. Estimation of machining performance is effectively carried out by ANN based on the training data with less than 8% estimation error, particularly for MRR and NWR.

Keywords


Abrasive Jet Machining (AJM), Alumina, Drilling, ANN, Estimation

References