{"title":"电沉积 Ni-PTFE 复合涂层的工艺参数预测：使用 ANFIS 模型的多重响应分析","authors":"S. Jeyaraj, P. S. Sivasakthivel","doi":"10.1007/s12666-024-03456-z","DOIUrl":null,"url":null,"abstract":"<p>Nickel-PolyTetraFluoroEthylene (Ni-PTFE) composite coatings were prepared from a watts-type nickel plating bath by varying process parameters in this study. The considered input process variables were current density, potential of hydrogen range, bath temperature, PTFE bath concentration, and stirrer speed. Experiments systematically analyzed their effects on outcomes. The responses measured in the experiments included the surface roughness, mass of deposit, and coating thickness of the coated samples. Scanning electron microscope and microstructure examinations analyzed Ni-PTFE deposition in specimens from mild steel plates. Additionally, an adaptive neural fuzzy inference system model was developed to predict the surface roughness, mass of deposit, and coating thickness of the coated samples. The model showed high accuracy in predicting parameters, closely matching experimental data.</p>","PeriodicalId":23224,"journal":{"name":"Transactions of The Indian Institute of Metals","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Process Parameters for Electrodeposited Ni-PTFE Composite Coating: A Multi-Response Analysis Using ANFIS Model\",\"authors\":\"S. Jeyaraj, P. S. Sivasakthivel\",\"doi\":\"10.1007/s12666-024-03456-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Nickel-PolyTetraFluoroEthylene (Ni-PTFE) composite coatings were prepared from a watts-type nickel plating bath by varying process parameters in this study. The considered input process variables were current density, potential of hydrogen range, bath temperature, PTFE bath concentration, and stirrer speed. Experiments systematically analyzed their effects on outcomes. The responses measured in the experiments included the surface roughness, mass of deposit, and coating thickness of the coated samples. Scanning electron microscope and microstructure examinations analyzed Ni-PTFE deposition in specimens from mild steel plates. Additionally, an adaptive neural fuzzy inference system model was developed to predict the surface roughness, mass of deposit, and coating thickness of the coated samples. The model showed high accuracy in predicting parameters, closely matching experimental data.</p>\",\"PeriodicalId\":23224,\"journal\":{\"name\":\"Transactions of The Indian Institute of Metals\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of The Indian Institute of Metals\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1007/s12666-024-03456-z\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Materials Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of The Indian Institute of Metals","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s12666-024-03456-z","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Materials Science","Score":null,"Total":0}
Prediction of Process Parameters for Electrodeposited Ni-PTFE Composite Coating: A Multi-Response Analysis Using ANFIS Model
Nickel-PolyTetraFluoroEthylene (Ni-PTFE) composite coatings were prepared from a watts-type nickel plating bath by varying process parameters in this study. The considered input process variables were current density, potential of hydrogen range, bath temperature, PTFE bath concentration, and stirrer speed. Experiments systematically analyzed their effects on outcomes. The responses measured in the experiments included the surface roughness, mass of deposit, and coating thickness of the coated samples. Scanning electron microscope and microstructure examinations analyzed Ni-PTFE deposition in specimens from mild steel plates. Additionally, an adaptive neural fuzzy inference system model was developed to predict the surface roughness, mass of deposit, and coating thickness of the coated samples. The model showed high accuracy in predicting parameters, closely matching experimental data.
期刊介绍：
Transactions of the Indian Institute of Metals publishes original research articles and reviews on ferrous and non-ferrous process metallurgy, structural and functional materials development, physical, chemical and mechanical metallurgy, welding science and technology, metal forming, particulate technologies, surface engineering, characterization of materials, thermodynamics and kinetics, materials modelling and other allied branches of Metallurgy and Materials Engineering.
Transactions of the Indian Institute of Metals also serves as a forum for rapid publication of recent advances in all the branches of Metallurgy and Materials Engineering. The technical content of the journal is scrutinized by the Editorial Board composed of experts from various disciplines of Metallurgy and Materials Engineering. Editorial Advisory Board provides valuable advice on technical matters related to the publication of Transactions.
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