Pub Date : 2023-09-01DOI: 10.1007/s12541-023-00890-9
Changwon Oh, Ju-Hyeong Lee, Tae In Ha, Byung-Kwon Min
{"title":"Model Parameter Identification of a Machining Robot Using Joint Frequency Response Functions","authors":"Changwon Oh, Ju-Hyeong Lee, Tae In Ha, Byung-Kwon Min","doi":"10.1007/s12541-023-00890-9","DOIUrl":"https://doi.org/10.1007/s12541-023-00890-9","url":null,"abstract":"","PeriodicalId":49178,"journal":{"name":"International Journal of Precision Engineering and Manufacturing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134915617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-06-14DOI: 10.1007/s12541-023-00836-1
Seungeun Lim, Changmo Yeo, Fazhi He, Jinwon Lee, Duhwan Mun
In machining feature recognition, geometric elements generated in a three-dimensional computer-aided design model are identified. This technique is used in manufacturability evaluation, process planning, and tool path generation. Here, we propose a method of recognizing 16 types of machining features using descriptors, often used in shape-based part retrieval studies. The base face is selected for each feature type, and descriptors express the base face’s minimum, maximum, and equal conditions. Furthermore, the similarity in the three conditions between the descriptors extracted from the target face and those from the base face is calculated. If the similarity is greater than or equal to the threshold, the target face is determined as the base face of the feature. Machining feature recognition tests were conducted for two test cases using the proposed method, and all machining features included in the test cases were successfully recognized. Moreover, we have compared the proposed method with the latest artificial neural network for test cases 3 and 4. As a result, the proposed method demonstrated a significantly higher recognition performance, with F1 scores of 0.94 and 1.0 for test cases 3 and 4, respectively, compared to the latest artificial neural networks (each with F1 scores of 0.86 and 0.49).
{"title":"Machining Feature Recognition Using Descriptors with Range Constraints for Mechanical 3D Models","authors":"Seungeun Lim, Changmo Yeo, Fazhi He, Jinwon Lee, Duhwan Mun","doi":"10.1007/s12541-023-00836-1","DOIUrl":"https://doi.org/10.1007/s12541-023-00836-1","url":null,"abstract":"In machining feature recognition, geometric elements generated in a three-dimensional computer-aided design model are identified. This technique is used in manufacturability evaluation, process planning, and tool path generation. Here, we propose a method of recognizing 16 types of machining features using descriptors, often used in shape-based part retrieval studies. The base face is selected for each feature type, and descriptors express the base face’s minimum, maximum, and equal conditions. Furthermore, the similarity in the three conditions between the descriptors extracted from the target face and those from the base face is calculated. If the similarity is greater than or equal to the threshold, the target face is determined as the base face of the feature. Machining feature recognition tests were conducted for two test cases using the proposed method, and all machining features included in the test cases were successfully recognized. Moreover, we have compared the proposed method with the latest artificial neural network for test cases 3 and 4. As a result, the proposed method demonstrated a significantly higher recognition performance, with F1 scores of 0.94 and 1.0 for test cases 3 and 4, respectively, compared to the latest artificial neural networks (each with F1 scores of 0.86 and 0.49).","PeriodicalId":49178,"journal":{"name":"International Journal of Precision Engineering and Manufacturing","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135916827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-05-30DOI: 10.1007/s12541-023-00831-6
Hamid Mostaghimi, Simon S. Park, Dong Yoon Lee, Soohyun Nam, Eunseok Nam
{"title":"Prediction of Tool Tip Dynamics Through Machine Learning and Inverse Receptance Coupling","authors":"Hamid Mostaghimi, Simon S. Park, Dong Yoon Lee, Soohyun Nam, Eunseok Nam","doi":"10.1007/s12541-023-00831-6","DOIUrl":"https://doi.org/10.1007/s12541-023-00831-6","url":null,"abstract":"","PeriodicalId":49178,"journal":{"name":"International Journal of Precision Engineering and Manufacturing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135478148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-05-30DOI: 10.1007/s12541-023-00806-7
Kyungmok Kim, Seung Yub Baek
{"title":"Influence of Counterpart Material on Fretting Wear of FDM Printed Polylactic Acid Plates","authors":"Kyungmok Kim, Seung Yub Baek","doi":"10.1007/s12541-023-00806-7","DOIUrl":"https://doi.org/10.1007/s12541-023-00806-7","url":null,"abstract":"","PeriodicalId":49178,"journal":{"name":"International Journal of Precision Engineering and Manufacturing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135643259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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