{"title":"AZ31合金电化学微加工及参数优化-神经网络和TOPSIS技术","authors":"N. Sivashankar, R. Thanigaivelan, K. G. Saravanan","doi":"10.4314/bcse.v37i5.17","DOIUrl":null,"url":null,"abstract":"ABSTRACT. Electrochemical micromachining (ECM) is a nontraditional method used for machining operations in hard and light materials with fixed or varying parameters. In this study, magnesium AZ31 alloy was micro machined using two types of electrolyte supply systems, namely electrolyte flooding and minimum quantity electrolyte (MQE). Experimental investigations were performed using TOPSIS and artificial neural network (ANN) techniques with types of electrolyte supply system, electrolyte concentration (EC), duty cycle (%), and machining voltage (V) as the input parameters, and material removal rate (MRR) and over cut (OC) as the outputs. Single and multi-objective parameter optimization was performed using Taguchi, TOPSIS, and ANN techniques. The machined microholes were analyzed using scanning electron microscopy. According to the TOPSIS results, under optimal conditions, a high MRR value and minimum OC of 1.282 μm/s and 66 μm, respectively, were obtained. The results of TOPSIS were verified using the developed ANN architecture. \n \nKEY WORDS: Magnesium, AZ31 alloy, Electrochemical micromachining, Optimization, TOPSIS, ANN \nBull. Chem. Soc. Ethiop. 2023, 37(5), 1263-1273. \nDOI: https://dx.doi.org/10.4314/bcse.v37i5.17 ","PeriodicalId":9501,"journal":{"name":"Bulletin of the Chemical Society of Ethiopia","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Electrochemical micromachining and parameter optimization on AZ31 alloy—ANN and TOPSIS techniques\",\"authors\":\"N. Sivashankar, R. Thanigaivelan, K. G. Saravanan\",\"doi\":\"10.4314/bcse.v37i5.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT. Electrochemical micromachining (ECM) is a nontraditional method used for machining operations in hard and light materials with fixed or varying parameters. In this study, magnesium AZ31 alloy was micro machined using two types of electrolyte supply systems, namely electrolyte flooding and minimum quantity electrolyte (MQE). Experimental investigations were performed using TOPSIS and artificial neural network (ANN) techniques with types of electrolyte supply system, electrolyte concentration (EC), duty cycle (%), and machining voltage (V) as the input parameters, and material removal rate (MRR) and over cut (OC) as the outputs. Single and multi-objective parameter optimization was performed using Taguchi, TOPSIS, and ANN techniques. The machined microholes were analyzed using scanning electron microscopy. According to the TOPSIS results, under optimal conditions, a high MRR value and minimum OC of 1.282 μm/s and 66 μm, respectively, were obtained. The results of TOPSIS were verified using the developed ANN architecture. \\n \\nKEY WORDS: Magnesium, AZ31 alloy, Electrochemical micromachining, Optimization, TOPSIS, ANN \\nBull. Chem. Soc. Ethiop. 2023, 37(5), 1263-1273. \\nDOI: https://dx.doi.org/10.4314/bcse.v37i5.17 \",\"PeriodicalId\":9501,\"journal\":{\"name\":\"Bulletin of the Chemical Society of Ethiopia\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bulletin of the Chemical Society of Ethiopia\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.4314/bcse.v37i5.17\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of the Chemical Society of Ethiopia","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.4314/bcse.v37i5.17","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Electrochemical micromachining and parameter optimization on AZ31 alloy—ANN and TOPSIS techniques
ABSTRACT. Electrochemical micromachining (ECM) is a nontraditional method used for machining operations in hard and light materials with fixed or varying parameters. In this study, magnesium AZ31 alloy was micro machined using two types of electrolyte supply systems, namely electrolyte flooding and minimum quantity electrolyte (MQE). Experimental investigations were performed using TOPSIS and artificial neural network (ANN) techniques with types of electrolyte supply system, electrolyte concentration (EC), duty cycle (%), and machining voltage (V) as the input parameters, and material removal rate (MRR) and over cut (OC) as the outputs. Single and multi-objective parameter optimization was performed using Taguchi, TOPSIS, and ANN techniques. The machined microholes were analyzed using scanning electron microscopy. According to the TOPSIS results, under optimal conditions, a high MRR value and minimum OC of 1.282 μm/s and 66 μm, respectively, were obtained. The results of TOPSIS were verified using the developed ANN architecture.
KEY WORDS: Magnesium, AZ31 alloy, Electrochemical micromachining, Optimization, TOPSIS, ANN
Bull. Chem. Soc. Ethiop. 2023, 37(5), 1263-1273.
DOI: https://dx.doi.org/10.4314/bcse.v37i5.17
期刊介绍:
The Bulletin of the Chemical Society of Ethiopia (BCSE) is a triannual publication of the Chemical Society of Ethiopia. The BCSE is an open access and peer reviewed journal. The BCSE invites contributions in any field of basic and applied chemistry.