Experimental investigations, empirical modeling and multi objective optimization of performance characteristics for ECDD with pressurized feeding method
[Display omitted] •Empirical models were developed to establish the relation between input process parameters and performance characteristics.•The outcomes manifested by the predictive models are in good agreement with experimental results as it exhibits an average error less than 5%.•The underlying...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 149; S. 107017 |
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| Sprache: | Englisch |
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01.01.2020
Elsevier Science Ltd |
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| ISSN: | 0263-2241, 1873-412X |
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| Abstract | [Display omitted]
•Empirical models were developed to establish the relation between input process parameters and performance characteristics.•The outcomes manifested by the predictive models are in good agreement with experimental results as it exhibits an average error less than 5%.•The underlying process mechanism for ECDD with the pressurized feeding system has also been explored.•Effect of process parameters on response characteristics has also discussed.•Genetic algorithm (GA) was used to perform the multi objective optimization.
Electrochemical discharge drilling (ECDD) is a hybrid micromachining process that can be used to drill micro holes on all kind of materials, irrespective of their mechanical and electrical properties. In ECDD, the continual penetration of tool electrode having electrochemical discharge (ECD) energy along the work material generates the holes. The relative movements between tool electrode and work material controls the energy penetration rate, and subsequently effects the performance characteristics. Therefore, the effect of process parameters associated with feeding motion and ECD energy are important to investigate simultaneously with respect to the performance characteristics. In the current study, a Buckingham’s dimensional method was used to determine the relation between input process parameters (that includes simultaneous interaction of ECD energy based parameters and feeding motion based parameters) and performance characteristics. The material removal rate (MRR) and hole overcut (HOC) were considered as the performance characteristics. MRR and HOC symbolize the productivity and accuracy of the process, respectively. During predictive modeling, the constant terms and coefficients were estimated from the non-linear experimental data. The outcomes manifested by the predictive models are in good agreement with the experimental results. The underlying process mechanism for ECDD with the pressurized feeding system has been explored by considering the simulation results as an evidences. Microscopic images of machined holes and discharge characteristics were used to develop the fundamental background of ECDD process mechanism with pressurized feeding method. Additionally, genetic algorithm (GA) was used to perform the multi objective optimization of ECDD process. |
|---|---|
| AbstractList | [Display omitted]
•Empirical models were developed to establish the relation between input process parameters and performance characteristics.•The outcomes manifested by the predictive models are in good agreement with experimental results as it exhibits an average error less than 5%.•The underlying process mechanism for ECDD with the pressurized feeding system has also been explored.•Effect of process parameters on response characteristics has also discussed.•Genetic algorithm (GA) was used to perform the multi objective optimization.
Electrochemical discharge drilling (ECDD) is a hybrid micromachining process that can be used to drill micro holes on all kind of materials, irrespective of their mechanical and electrical properties. In ECDD, the continual penetration of tool electrode having electrochemical discharge (ECD) energy along the work material generates the holes. The relative movements between tool electrode and work material controls the energy penetration rate, and subsequently effects the performance characteristics. Therefore, the effect of process parameters associated with feeding motion and ECD energy are important to investigate simultaneously with respect to the performance characteristics. In the current study, a Buckingham’s dimensional method was used to determine the relation between input process parameters (that includes simultaneous interaction of ECD energy based parameters and feeding motion based parameters) and performance characteristics. The material removal rate (MRR) and hole overcut (HOC) were considered as the performance characteristics. MRR and HOC symbolize the productivity and accuracy of the process, respectively. During predictive modeling, the constant terms and coefficients were estimated from the non-linear experimental data. The outcomes manifested by the predictive models are in good agreement with the experimental results. The underlying process mechanism for ECDD with the pressurized feeding system has been explored by considering the simulation results as an evidences. Microscopic images of machined holes and discharge characteristics were used to develop the fundamental background of ECDD process mechanism with pressurized feeding method. Additionally, genetic algorithm (GA) was used to perform the multi objective optimization of ECDD process. Electrochemical discharge drilling (ECDD) is a hybrid micromachining process that can be used to drill micro holes on all kind of materials, irrespective of their mechanical and electrical properties. In ECDD, the continual penetration of tool electrode having electrochemical discharge (ECD) energy along the work material generates the holes. The relative movements between tool electrode and work material controls the energy penetration rate, and subsequently effects the performance characteristics. Therefore, the effect of process parameters associated with feeding motion and ECD energy are important to investigate simultaneously with respect to the performance characteristics. In the current study, a Buckingham's dimensional method was used to determine the relation between input process parameters (that includes simultaneous interaction of ECD energy based parameters and feeding motion based parameters) and performance characteristics. The material removal rate (MRR) and hole overcut (HOC) were considered as the performance characteristics. MRR and HOC symbolize the productivity and accuracy of the process, respectively. During predictive modeling, the constant terms and coefficients were estimated from the non-linear experimental data. The outcomes manifested by the predictive models are in good agreement with the experimental results. The underlying process mechanism for ECDD with the pressurized feeding system has been explored by considering the simulation results as an evidences. Microscopic images of machined holes and discharge characteristics were used to develop the fundamental background of ECDD process mechanism with pressurized feeding method. Additionally, genetic algorithm (GA) was used to perform the multi objective optimization of ECDD process. |
| ArticleNumber | 107017 |
| Author | Singh, Tarlochan Rathore, Ranjeet Singh Dvivedi, Akshay |
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| CitedBy_id | crossref_primary_10_1016_j_jmapro_2023_03_044 crossref_primary_10_1177_09544054221093302 crossref_primary_10_1177_09544062231179988 crossref_primary_10_1016_j_measurement_2022_111079 crossref_primary_10_1016_j_measurement_2024_114440 crossref_primary_10_1088_1361_6439_ad2089 crossref_primary_10_1007_s00170_021_07155_7 crossref_primary_10_1007_s40430_024_04891_1 crossref_primary_10_1007_s10800_023_01956_2 |
| Cites_doi | 10.1080/10426914.2017.1339319 10.1016/j.measurement.2016.05.018 10.1088/0960-1317/17/5/016 10.1016/j.measurement.2018.02.016 10.1016/S0924-0136(99)00318-0 10.1016/S0032-3861(02)00241-0 10.1016/j.ijmachtools.2005.12.005 10.1016/j.measurement.2016.02.008 10.1080/10940340008945710 10.1016/j.measurement.2016.09.033 10.15623/ijret.2016.0525023 10.1177/0954405412441885 10.1016/S0890-6955(98)00010-8 10.4028/www.scientific.net/KEM.196.149 10.1016/j.procir.2016.02.146 10.1177/0954408915593875 10.1016/j.ijmachtools.2016.03.004 10.1080/10426914.2015.1103857 10.1016/j.precisioneng.2016.01.007 10.1007/s00170-010-3000-0 10.1088/0960-1317/16/11/N03 10.1007/s00170-017-0224-2 10.1016/j.jmapro.2015.05.006 |
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| References | Gupta, Dvivedi, Kumar (b0060) 2015; 31 Cardaropoli, Alfieri, Caiazzo, Sergi (b0090) 2012; 226 Jain, Choudhury, Ramesh (b0025) 2002; 42 Yang, Ho, Yan (b0110) 2001; 196 Zheng, Cheng, Huang, Yan (b0015) 2007; 17 Jain, Chak (b0105) 2000; 4 Ziki, Wuthrich (b0035) 2015; 19 Selvarajan, Sathiya Narayanan, Jeyapaul, Manohar (b0075) 2016; 92 Kumar, Singh, Batish, Singh (b0085) 2015; 231 Bhondwe, Yadava, Kathiresan (b0050) 2006; 46 Singh, Dvivedi (b0070) 2018; 33 Wuthrich, Despont, Maillard, Bleuler (b0040) 2006; 16 Gupta, Dvivedi, Kumar (b0005) 2014 Wei, Xu, Ni, Brzezinski, Hu (b0055) 2011; 54 Tamiloli, Venkatesan, Vijaya Ramnath (b0100) 2016; 84 Bhattacharyya, Doloi, Sorkhel (b0115) 1999; 95 Jain, Dixit, Pandey (b0045) 1999; 39 Elhami, Razfar (b0065) 2017; 92 Mia, Dey, Hossain, Arafat, Asaduzzaman, Shoriat Ullah, Tareq Zobaer (b0095) 2018; 122 Čarnogurská, Příhoda, Puškár, Fabian, Dobáková, Kubík (b0080) 2016; 94 Kolhekar, Sundaram (b0120) 2016; 45 Mudimallana, Sharma, Jawalkar (b0010) 2016; 45 Singh, Dvivedi (b0020) 2016; 5 Singh, Dvivedi (b0030) 2016; 105 Singh (10.1016/j.measurement.2019.107017_b0030) 2016; 105 Bhondwe (10.1016/j.measurement.2019.107017_b0050) 2006; 46 Čarnogurská (10.1016/j.measurement.2019.107017_b0080) 2016; 94 Mia (10.1016/j.measurement.2019.107017_b0095) 2018; 122 Bhattacharyya (10.1016/j.measurement.2019.107017_b0115) 1999; 95 Cardaropoli (10.1016/j.measurement.2019.107017_b0090) 2012; 226 Gupta (10.1016/j.measurement.2019.107017_b0060) 2015; 31 Selvarajan (10.1016/j.measurement.2019.107017_b0075) 2016; 92 Mudimallana (10.1016/j.measurement.2019.107017_b0010) 2016; 45 Yang (10.1016/j.measurement.2019.107017_b0110) 2001; 196 Zheng (10.1016/j.measurement.2019.107017_b0015) 2007; 17 Kumar (10.1016/j.measurement.2019.107017_b0085) 2015; 231 Gupta (10.1016/j.measurement.2019.107017_b0005) 2014 Ziki (10.1016/j.measurement.2019.107017_b0035) 2015; 19 Jain (10.1016/j.measurement.2019.107017_b0105) 2000; 4 Wuthrich (10.1016/j.measurement.2019.107017_b0040) 2006; 16 Kolhekar (10.1016/j.measurement.2019.107017_b0120) 2016; 45 Jain (10.1016/j.measurement.2019.107017_b0045) 1999; 39 Elhami (10.1016/j.measurement.2019.107017_b0065) 2017; 92 Singh (10.1016/j.measurement.2019.107017_b0070) 2018; 33 Tamiloli (10.1016/j.measurement.2019.107017_b0100) 2016; 84 Wei (10.1016/j.measurement.2019.107017_b0055) 2011; 54 Singh (10.1016/j.measurement.2019.107017_b0020) 2016; 5 Jain (10.1016/j.measurement.2019.107017_b0025) 2002; 42 |
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Technol. doi: 10.1007/s00170-010-3000-0 – volume: 16 start-page: N28 year: 2006 ident: 10.1016/j.measurement.2019.107017_b0040 article-title: Improving the material removal rate in spark-assisted chemical engraving (SACE) gravity-feed micro-hole drilling by tool vibration publication-title: J. Micromech. Microeng. doi: 10.1088/0960-1317/16/11/N03 – volume: 92 start-page: 1591 year: 2017 ident: 10.1016/j.measurement.2019.107017_b0065 article-title: Study of the current signal and material removal during ultrasonic-assisted electrochemical discharge machining publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-017-0224-2 – volume: 19 start-page: 87 year: 2015 ident: 10.1016/j.measurement.2019.107017_b0035 article-title: The machining gap during constant velocity-feed glass micro-drilling by spark assisted chemical engraving publication-title: J. Manuf. Processes doi: 10.1016/j.jmapro.2015.05.006 |
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•Empirical models were developed to establish the relation between input process parameters and performance characteristics.•The outcomes... Electrochemical discharge drilling (ECDD) is a hybrid micromachining process that can be used to drill micro holes on all kind of materials, irrespective of... |
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| SubjectTerms | Computer simulation Drilling ECDD Electrical properties Electrodes Empirical modelling Feeding Genetic algorithms Interaction parameters Material removal rate (machining) Mathematical models Mechanical properties Micromachining Modelling Optimization Penetration Performance characteristics Process parameters |
| Title | Experimental investigations, empirical modeling and multi objective optimization of performance characteristics for ECDD with pressurized feeding method |
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