{"title":"使用人工神经网络方法估算添加式制造的再生 ABS 零件的摩擦和磨损特性:层厚、填充率和构建方向的影响","authors":"Ç. Bolat, Abdulkadir Cebi, Sarp Çoban, B. Ergene","doi":"10.1515/ipp-2023-4481","DOIUrl":null,"url":null,"abstract":"\n This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"30 17","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach: effects of layer thickness, infill rate, and building direction\",\"authors\":\"Ç. Bolat, Abdulkadir Cebi, Sarp Çoban, B. Ergene\",\"doi\":\"10.1515/ipp-2023-4481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":\"30 17\",\"pages\":\"\"},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-04-24\",\"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/10.1515/ipp-2023-4481\",\"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/10.1515/ipp-2023-4481","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach: effects of layer thickness, infill rate, and building direction
This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas.
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.