Diviya Mariya Louis, Subramanian Manivel, Kaliappan Seeniappan, N. L
{"title":"针对 AM60B 生物医学材料的线切割多反应优化和基于网络的预测建模","authors":"Diviya Mariya Louis, Subramanian Manivel, Kaliappan Seeniappan, N. L","doi":"10.1177/09544062241264939","DOIUrl":null,"url":null,"abstract":"The AM60B magnesium alloy remains a pivotal player in the pursuit of lighter, biodegradable and more sustainable solutions for various biomedical implants because of its excellent machinability, exceptional strength and resistance to degradation. However, AM60B magnesium alloys exhibit poor machinability when machined by conventional methods, since they are susceptible to deformation and degradation at elevated temperatures. The current work focused on multiresponse optimization for AM60B magnesium alloys for wire electrical discharge machining (WEDM), where multiple output variables including the machining rate, surface irregularity, and microindentation hardness, were considered simultaneously. Response surface methodology (RSM) along with artificial neural network (ANN) were utilized to investigate the effect of these parameters on the control of these output characteristics. The findings demonstrated the best combination of input specifications, with a discharge duration of 112.743 µs, a spark gap time of 57.6532 µs, a discharge voltage of 6.63 V and a wire advance rate of 5.39357 mm/min which yielded the best surface irregularity, microindentation hardness, and machining rate from the RSM. ANN and RSM models were effective in simulating the experiments, with predicted values closely aligning with the experimental results. An optimally trained network model exhibited good agreement with a mean error less than 5%. Additionally, the condition of the machined surface, including any cracks, voids, or other flaws, was examined using scanning electron microscope (SEM).","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"8 12","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiresponse optimization and network-based prediction modelling for the WEDM of AM60B biomedical material\",\"authors\":\"Diviya Mariya Louis, Subramanian Manivel, Kaliappan Seeniappan, N. L\",\"doi\":\"10.1177/09544062241264939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The AM60B magnesium alloy remains a pivotal player in the pursuit of lighter, biodegradable and more sustainable solutions for various biomedical implants because of its excellent machinability, exceptional strength and resistance to degradation. However, AM60B magnesium alloys exhibit poor machinability when machined by conventional methods, since they are susceptible to deformation and degradation at elevated temperatures. The current work focused on multiresponse optimization for AM60B magnesium alloys for wire electrical discharge machining (WEDM), where multiple output variables including the machining rate, surface irregularity, and microindentation hardness, were considered simultaneously. Response surface methodology (RSM) along with artificial neural network (ANN) were utilized to investigate the effect of these parameters on the control of these output characteristics. The findings demonstrated the best combination of input specifications, with a discharge duration of 112.743 µs, a spark gap time of 57.6532 µs, a discharge voltage of 6.63 V and a wire advance rate of 5.39357 mm/min which yielded the best surface irregularity, microindentation hardness, and machining rate from the RSM. ANN and RSM models were effective in simulating the experiments, with predicted values closely aligning with the experimental results. An optimally trained network model exhibited good agreement with a mean error less than 5%. Additionally, the condition of the machined surface, including any cracks, voids, or other flaws, was examined using scanning electron microscope (SEM).\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":\"8 12\",\"pages\":\"\"},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/09544062241264939\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544062241264939","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Multiresponse optimization and network-based prediction modelling for the WEDM of AM60B biomedical material
The AM60B magnesium alloy remains a pivotal player in the pursuit of lighter, biodegradable and more sustainable solutions for various biomedical implants because of its excellent machinability, exceptional strength and resistance to degradation. However, AM60B magnesium alloys exhibit poor machinability when machined by conventional methods, since they are susceptible to deformation and degradation at elevated temperatures. The current work focused on multiresponse optimization for AM60B magnesium alloys for wire electrical discharge machining (WEDM), where multiple output variables including the machining rate, surface irregularity, and microindentation hardness, were considered simultaneously. Response surface methodology (RSM) along with artificial neural network (ANN) were utilized to investigate the effect of these parameters on the control of these output characteristics. The findings demonstrated the best combination of input specifications, with a discharge duration of 112.743 µs, a spark gap time of 57.6532 µs, a discharge voltage of 6.63 V and a wire advance rate of 5.39357 mm/min which yielded the best surface irregularity, microindentation hardness, and machining rate from the RSM. ANN and RSM models were effective in simulating the experiments, with predicted values closely aligning with the experimental results. An optimally trained network model exhibited good agreement with a mean error less than 5%. Additionally, the condition of the machined surface, including any cracks, voids, or other flaws, was examined using scanning electron microscope (SEM).
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.