Zhen Qi Chee, Zi Jie Choong, Wai Leong Eugene Wong
{"title":"Digitization of Fused Deposited Methods (FDM) Printer for Smart Additive Manufacturing (AM)","authors":"Zhen Qi Chee, Zi Jie Choong, Wai Leong Eugene Wong","doi":"10.1109/ICMT53429.2021.9687227","DOIUrl":null,"url":null,"abstract":"The panic buying during Covid-19 caused farmers to amped-up production. However, farm equipment is costly to purchase. Therefore, some farmers utilized Additive Manufacturing (AM) to manufacture farming tools at low cost. However, the lack of in-situ monitoring in AM to stop printing failed parts can waste materials and time. Thus, this research aims to deploy a low-cost smart remote monitoring system using OctoPrint and Node-red to integrate a 3D printer and Teachable Machine and train a model to pre-emptively detect print errors. The result was satisfactory as the 3D printer stopped when the camera detected a defect with 75% accuracy. Furthermore, the user can easily customize the model to enhance the system versatility via the developed code-free platform.","PeriodicalId":258783,"journal":{"name":"2021 24th International Conference on Mechatronics Technology (ICMT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 24th International Conference on Mechatronics Technology (ICMT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMT53429.2021.9687227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The panic buying during Covid-19 caused farmers to amped-up production. However, farm equipment is costly to purchase. Therefore, some farmers utilized Additive Manufacturing (AM) to manufacture farming tools at low cost. However, the lack of in-situ monitoring in AM to stop printing failed parts can waste materials and time. Thus, this research aims to deploy a low-cost smart remote monitoring system using OctoPrint and Node-red to integrate a 3D printer and Teachable Machine and train a model to pre-emptively detect print errors. The result was satisfactory as the 3D printer stopped when the camera detected a defect with 75% accuracy. Furthermore, the user can easily customize the model to enhance the system versatility via the developed code-free platform.