Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes

Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum sh...

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Main Authors: Shih-Ming Wang, Li-Jen Hsu, Hariyanto Gunawan, Ren-Qi Tu
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Machines
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Online Access:https://www.mdpi.com/2075-1702/12/12/908
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author Shih-Ming Wang
Li-Jen Hsu
Hariyanto Gunawan
Ren-Qi Tu
author_facet Shih-Ming Wang
Li-Jen Hsu
Hariyanto Gunawan
Ren-Qi Tu
author_sort Shih-Ming Wang
collection DOAJ
description Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%.
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spelling doaj-art-9c4110995dba40e9831de4ba5db6e2ac2025-08-20T02:00:24ZengMDPI AGMachines2075-17022024-12-01121290810.3390/machines12120908Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining ProcessesShih-Ming Wang0Li-Jen Hsu1Hariyanto Gunawan2Ren-Qi Tu3Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Taoyuan 320314, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Taoyuan 320314, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Taoyuan 320314, TaiwanMachining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%.https://www.mdpi.com/2075-1702/12/12/908WEDMdrum errorregressionsurface processing speed
spellingShingle Shih-Ming Wang
Li-Jen Hsu
Hariyanto Gunawan
Ren-Qi Tu
Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
Machines
WEDM
drum error
regression
surface processing speed
title Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
title_full Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
title_fullStr Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
title_full_unstemmed Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
title_short Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
title_sort optimization of machining parameters for reducing drum shape error phenomenon in wire electrical discharge machining processes
topic WEDM
drum error
regression
surface processing speed
url https://www.mdpi.com/2075-1702/12/12/908
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AT lijenhsu optimizationofmachiningparametersforreducingdrumshapeerrorphenomenoninwireelectricaldischargemachiningprocesses
AT hariyantogunawan optimizationofmachiningparametersforreducingdrumshapeerrorphenomenoninwireelectricaldischargemachiningprocesses
AT renqitu optimizationofmachiningparametersforreducingdrumshapeerrorphenomenoninwireelectricaldischargemachiningprocesses