Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling

Abstract Regardless of the materials’ intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The present article optimizes the ultrasonic assisted rotary ECDD (UR-ECDD) process for maximizing the material removal rate (MRR), while minimizin...

Full description

Saved in:
Bibliographic Details
Main Authors: Sahil Grover, Viveksheel Rajput, Sanjay Kumar Mangal, Sarbjit Singh, Sandeep Singh, Shubham Sharma, Ehab El Sayed Massoud, Dražan Kozak, Jasmina Lozanovic
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-025-92574-9
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849311976516222976
author Sahil Grover
Viveksheel Rajput
Sanjay Kumar Mangal
Sarbjit Singh
Sandeep Singh
Shubham Sharma
Ehab El Sayed Massoud
Dražan Kozak
Jasmina Lozanovic
author_facet Sahil Grover
Viveksheel Rajput
Sanjay Kumar Mangal
Sarbjit Singh
Sandeep Singh
Shubham Sharma
Ehab El Sayed Massoud
Dražan Kozak
Jasmina Lozanovic
author_sort Sahil Grover
collection DOAJ
description Abstract Regardless of the materials’ intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The present article optimizes the ultrasonic assisted rotary ECDD (UR-ECDD) process for maximizing the material removal rate (MRR), while minimizing the hole overcut (HOC) and circularity error (CE). The micro-holes are produced using a Taguchi’s L16 array and multi-criteria optimization is carried out using grey relational based analysis (GRA). MRR, HOC and CE serve as a response parameter while tool vibration, tool feed rate, working material rotation, applied voltage and electrolyte concentration are control variables. UR-ECDD results in the improvement of 14.8% in MRR, 15.4% in HOC and 17.2% in CE when compared to the ECDD process. The optimized control variables based on GRA are derived as A4C3B4D1E4 (6 µm, 80 rpm, 0.9 mm/min, 35 V, 25 wt%). Tool vibration emerged as the most significant control variable. The GRG’s predicted results at optimum conditions provide a satisfactory alignment with the experimental results. Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. The validation of the dataset exhibits a similar trend confirming the efficacy of prediction.
format Article
id doaj-art-ae7d7fd109c64aeeb6eab9143e700212
institution Kabale University
issn 2045-2322
language English
publishDate 2025-05-01
publisher Nature Portfolio
record_format Article
series Scientific Reports
spelling doaj-art-ae7d7fd109c64aeeb6eab9143e7002122025-08-20T03:53:13ZengNature PortfolioScientific Reports2045-23222025-05-0115112110.1038/s41598-025-92574-9Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drillingSahil Grover0Viveksheel Rajput1Sanjay Kumar Mangal2Sarbjit Singh3Sandeep Singh4Shubham Sharma5Ehab El Sayed Massoud6Dražan Kozak7Jasmina Lozanovic8Mechanical Engineering Department, Punjab Engineering CollegeMechanical Engineering Department, Punjab Engineering CollegeMechanical Engineering Department, Punjab Engineering CollegeMechanical Engineering Department, Punjab Engineering CollegeSchool of Engineering, Bhara UniversityDepartment of Technical Sciences, Western Caspian UniversityCollege of Applied Sciences, King Khalid UniversityMechanical Engineering Faculty in Slavonski Brod, University of Slavonski BrodDepartment of Engineering, University of Applied Sciences - FH Campus WienAbstract Regardless of the materials’ intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The present article optimizes the ultrasonic assisted rotary ECDD (UR-ECDD) process for maximizing the material removal rate (MRR), while minimizing the hole overcut (HOC) and circularity error (CE). The micro-holes are produced using a Taguchi’s L16 array and multi-criteria optimization is carried out using grey relational based analysis (GRA). MRR, HOC and CE serve as a response parameter while tool vibration, tool feed rate, working material rotation, applied voltage and electrolyte concentration are control variables. UR-ECDD results in the improvement of 14.8% in MRR, 15.4% in HOC and 17.2% in CE when compared to the ECDD process. The optimized control variables based on GRA are derived as A4C3B4D1E4 (6 µm, 80 rpm, 0.9 mm/min, 35 V, 25 wt%). Tool vibration emerged as the most significant control variable. The GRG’s predicted results at optimum conditions provide a satisfactory alignment with the experimental results. Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. The validation of the dataset exhibits a similar trend confirming the efficacy of prediction.https://doi.org/10.1038/s41598-025-92574-9Ultrasonic assisted rotaryMachine learningOptimizationOvercutSparksMulti-criteria
spellingShingle Sahil Grover
Viveksheel Rajput
Sanjay Kumar Mangal
Sarbjit Singh
Sandeep Singh
Shubham Sharma
Ehab El Sayed Massoud
Dražan Kozak
Jasmina Lozanovic
Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
Scientific Reports
Ultrasonic assisted rotary
Machine learning
Optimization
Overcut
Sparks
Multi-criteria
title Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
title_full Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
title_fullStr Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
title_full_unstemmed Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
title_short Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
title_sort micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling
topic Ultrasonic assisted rotary
Machine learning
Optimization
Overcut
Sparks
Multi-criteria
url https://doi.org/10.1038/s41598-025-92574-9
work_keys_str_mv AT sahilgrover microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT viveksheelrajput microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT sanjaykumarmangal microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT sarbjitsingh microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT sandeepsingh microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT shubhamsharma microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT ehabelsayedmassoud microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT drazankozak microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling
AT jasminalozanovic microholedrillingandmulticriteriaoptimizationofsodalimeglassviaultrasonicassistedrotaryelectrochemicaldischargedrilling