Pub Date : 2023-09-26DOI: 10.1007/s40684-023-00548-x
Michael Keitel, Berend Denkena, Benjamin Bergmann
Abstract The dry grinding process is challenging due to the induced thermal loads into the workpiece, which leads to a reduction of the workpiece quality. One approach to reduce the thermal loads is to adjust the grinding tool geometry by inserting a porous structure for dry grinding. This porous structure can be implemented, for example, by additively manufactured grinding tools. For this purpose, the suitability of additively manufactured vitrified cubic boron nitride grinding tools for dry grinding of tempered AISI M3:2 was investigated and compared with conventionally manufactured grinding tools to investigate the possibility of reducing the high temperatures and to verify the advantage of additively manufactured grinding tools. For this the resulting topographies and residuals stress states as well as wear of the grinding tools were analyzed. Additively manufactured grinding tools generated constant surface roughnesses of below 1 µm as well as contant compressive residual stress states. These results were attributed to a continuous self-sharpening of the grinding tools, which was shown qualitatively and quantitatively on the basis of the tool surfaces. Additively manufactured grinding tools with a porous structure thus have the potential to increase the possibilities of dry grinding.
{"title":"Dry Grinding by Means of Additively Manufactured Toric Grinding Pins","authors":"Michael Keitel, Berend Denkena, Benjamin Bergmann","doi":"10.1007/s40684-023-00548-x","DOIUrl":"https://doi.org/10.1007/s40684-023-00548-x","url":null,"abstract":"Abstract The dry grinding process is challenging due to the induced thermal loads into the workpiece, which leads to a reduction of the workpiece quality. One approach to reduce the thermal loads is to adjust the grinding tool geometry by inserting a porous structure for dry grinding. This porous structure can be implemented, for example, by additively manufactured grinding tools. For this purpose, the suitability of additively manufactured vitrified cubic boron nitride grinding tools for dry grinding of tempered AISI M3:2 was investigated and compared with conventionally manufactured grinding tools to investigate the possibility of reducing the high temperatures and to verify the advantage of additively manufactured grinding tools. For this the resulting topographies and residuals stress states as well as wear of the grinding tools were analyzed. Additively manufactured grinding tools generated constant surface roughnesses of below 1 µm as well as contant compressive residual stress states. These results were attributed to a continuous self-sharpening of the grinding tools, which was shown qualitatively and quantitatively on the basis of the tool surfaces. Additively manufactured grinding tools with a porous structure thus have the potential to increase the possibilities of dry grinding.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This comprehensive review paper aims to provide an in-depth analysis of the most recent developments in the applications of artificial intelligence (AI) techniques, with an emphasis on their critical role in the demand side of power distribution systems. This paper offers a meticulous examination of various AI models and a pragmatic guide to aid in selecting the suitable techniques for three areas: load forecasting, anomaly detection, and demand response in real-world applications. In the realm of load forecasting, the paper presents a thorough guide for choosing the most fitting machine learning and deep learning models, inclusive of reinforcement learning, in conjunction with the application of hybrid models and learning optimization strategies. This selection process is informed by the properties of load data and the specific scenarios that necessitate forecasting. Concerning anomaly detection, this paper provides an overview of the merits and limitations of disparate learning methods, fostering a discussion on the optimization strategies that can be harnessed to navigate the issue of imbalanced data, a prevalent concern in power system anomaly detection. As for demand response, we delve into the utilization of AI techniques, examining both incentive-based and price-based demand response schemes. We take into account various control targets, input sources, and applications that pertain to their use and effectiveness. In conclusion, this review paper is structured to offer useful insights into the selection and design of AI techniques focusing on the demand-side applications of future energy systems. It provides guidance and future directions for the development of sustainable energy systems, aiming to serve as a cornerstone for ongoing research within this swiftly evolving field.
{"title":"AI-Empowered Methods for Smart Energy Consumption: A Review of Load Forecasting, Anomaly Detection and Demand Response","authors":"Xinlin Wang, Hao Wang, Binayak Bhandari, Leming Cheng","doi":"10.1007/s40684-023-00537-0","DOIUrl":"https://doi.org/10.1007/s40684-023-00537-0","url":null,"abstract":"Abstract This comprehensive review paper aims to provide an in-depth analysis of the most recent developments in the applications of artificial intelligence (AI) techniques, with an emphasis on their critical role in the demand side of power distribution systems. This paper offers a meticulous examination of various AI models and a pragmatic guide to aid in selecting the suitable techniques for three areas: load forecasting, anomaly detection, and demand response in real-world applications. In the realm of load forecasting, the paper presents a thorough guide for choosing the most fitting machine learning and deep learning models, inclusive of reinforcement learning, in conjunction with the application of hybrid models and learning optimization strategies. This selection process is informed by the properties of load data and the specific scenarios that necessitate forecasting. Concerning anomaly detection, this paper provides an overview of the merits and limitations of disparate learning methods, fostering a discussion on the optimization strategies that can be harnessed to navigate the issue of imbalanced data, a prevalent concern in power system anomaly detection. As for demand response, we delve into the utilization of AI techniques, examining both incentive-based and price-based demand response schemes. We take into account various control targets, input sources, and applications that pertain to their use and effectiveness. In conclusion, this review paper is structured to offer useful insights into the selection and design of AI techniques focusing on the demand-side applications of future energy systems. It provides guidance and future directions for the development of sustainable energy systems, aiming to serve as a cornerstone for ongoing research within this swiftly evolving field.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135957775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1007/s40684-023-00562-z
Sungjun Choi, Yong-Rae Jang, Hak-Sung Kim, Caroline Sunyong Lee
{"title":"Flashlight Sintering Characteristics of the Inkjet-Printed Nanosized Copper Ink on an Auxiliary Heated Paper Substrate","authors":"Sungjun Choi, Yong-Rae Jang, Hak-Sung Kim, Caroline Sunyong Lee","doi":"10.1007/s40684-023-00562-z","DOIUrl":"https://doi.org/10.1007/s40684-023-00562-z","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1007/s40684-023-00564-x
Sungjea Park, Ali Akbar, Jonghyun Lee, Young-Beom Kim, Sukkee Um
{"title":"Nanoscopic Post-Compression Effects on Transport Phenomena and Electrochemical Utilization in Quaternion Catalyst Layers for Fuel Cell Applications","authors":"Sungjea Park, Ali Akbar, Jonghyun Lee, Young-Beom Kim, Sukkee Um","doi":"10.1007/s40684-023-00564-x","DOIUrl":"https://doi.org/10.1007/s40684-023-00564-x","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1007/s40684-023-00559-8
Ji-Seob Choi, Jinhong Noh, Hongsoo Choi, Yong-Jin Yoon, Woo-Tae Park
{"title":"Characterizing the Performance of a Resonance-Based MEMS Particle Sensor with Glass Beads","authors":"Ji-Seob Choi, Jinhong Noh, Hongsoo Choi, Yong-Jin Yoon, Woo-Tae Park","doi":"10.1007/s40684-023-00559-8","DOIUrl":"https://doi.org/10.1007/s40684-023-00559-8","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135153489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-09DOI: 10.1007/s40684-023-00555-y
Daizheng Hou, Yafu Zhou, Qichao Dong
{"title":"Taguchi Robust Design-Based Component Matching and Energy Management of Plug-in Hybrid Electric Buses for City Multiple Bus Routes","authors":"Daizheng Hou, Yafu Zhou, Qichao Dong","doi":"10.1007/s40684-023-00555-y","DOIUrl":"https://doi.org/10.1007/s40684-023-00555-y","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136192398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1007/s40684-023-00558-9
Zhaojing Gao, Heng Zhang, Min Ji, Chenlong Zuo, Jinsheng Zhang
{"title":"Influence of Various Cooling and Lubrication Conditions on Tool Wear and Machining Quality in Milling Inconel 718","authors":"Zhaojing Gao, Heng Zhang, Min Ji, Chenlong Zuo, Jinsheng Zhang","doi":"10.1007/s40684-023-00558-9","DOIUrl":"https://doi.org/10.1007/s40684-023-00558-9","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"21 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89552303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1007/s40684-023-00557-w
Jiahao Liu, Chen Jiang, Xue Yang, Shijie Sun
{"title":"Review of the Application of Acoustic Emission Technology in Green Manufacturing","authors":"Jiahao Liu, Chen Jiang, Xue Yang, Shijie Sun","doi":"10.1007/s40684-023-00557-w","DOIUrl":"https://doi.org/10.1007/s40684-023-00557-w","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"125 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89667850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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