{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T17:13:42Z","timestamp":1747329222570,"version":"3.38.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>3D printing technology is a popular new technology that combines information technology with mechanical engineering and material science, and is being widely used in various fields. From the computer point of view, the basic technology of 3D printing is to slice and scan each part of the 3D model. The slicing algorithm used directly affects the speed of slicing and the effect of post-printing, and the result of slicing needs to be optimized. This paper analyzes the slicing algorithm and partition scanning strategy related technologies of 3D printing models and solves the practical problems of models in the 3D printing process. This paper first studies the slicing algorithm in the 3D printing process, then obtains and intercepts the cross-sectional contour, and finally performs mosaic and partition scanning on the sliced polygon, and uses experiments to verify the effectiveness of the research. The experimental results show that compared with other 3D printing methods, the printing method in this paper reduces the slicing time by 23.61%, and the fitting error range and the fluctuation range of the residual height of the fusion volume are smaller.<\/jats:p>","DOI":"10.3233\/idt-230247","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T17:20:15Z","timestamp":1715966415000},"page":"731-741","source":"Crossref","is-referenced-by-count":1,"title":["3D printing slicing algorithm and partition scanning strategy for big data"],"prefix":"10.1177","volume":"18","author":[{"given":"Qingyu","family":"Huang","sequence":"first","affiliation":[{"name":"College of Intelligent Manufacturing and Automobile, Chengdu Vocational and Technical College of Industry, Chengdu, Sichuan, China"}]},{"given":"Yi","family":"Zhong","sequence":"additional","affiliation":[{"name":"Electrical Automation, Chengdu Tiancheng Hengxin Technology Co., Ltd., Chengdu, Sichuan, China"}]},{"given":"Wenya","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering College, Chengdu College of University of Electronic Science and Technology of China, Chengdu, Sichuan, China"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/IDT-230247_ref1","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.compositesb.2016.11.034","article-title":"3D printing of polymer matrix composites: A review and prospective","volume":"110","author":"Wang","year":"2017","journal-title":"Composites Part B Engineering"},{"issue":"6","key":"10.3233\/IDT-230247_ref2","first-page":"102","article-title":"Polymers for 3D printing and customized additive manufacturing","volume":"2017","author":"Ligon","year":"2017","journal-title":"Chemical Reviews"},{"issue":"9","key":"10.3233\/IDT-230247_ref3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.apmt.2017.02.004","article-title":"Fundamentals and applications of 3D printing for novel materials","volume":"7","author":"Lee","year":"2017","journal-title":"Applied Materials Today"},{"issue":"2","key":"10.3233\/IDT-230247_ref4","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.jcmg.2016.12.001","article-title":"Cardiac 3D Printing and its Future Directions","volume":"10","author":"Vukicevic","year":"2017","journal-title":"Jacc Cardiovascular Imaging"},{"issue":"1","key":"10.3233\/IDT-230247_ref5","first-page":"134","article-title":"3D printing technology and its application trend","volume":"9","author":"Ren","year":"2018","journal-title":"Journal of Chengdu Technological University"},{"issue":"2","key":"10.3233\/IDT-230247_ref6","first-page":"83","article-title":"Programming your network at run-time for big data applications","volume":"6","author":"Wang","year":"2017","journal-title":"ACM"},{"issue":"9","key":"10.3233\/IDT-230247_ref7","first-page":"105","article-title":"Big data analytics in logistics and supply chain management","volume":"2018","author":"Wamba","year":"2018","journal-title":"International Journal of Logs Management"},{"issue":"9","key":"10.3233\/IDT-230247_ref8","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.jbusres.2016.08.001","article-title":"Critical analysis of big data challenges and analytical methods","volume":"70","author":"Sivarajah","year":"2017","journal-title":"Journal of Business Research"},{"issue":"2","key":"10.3233\/IDT-230247_ref9","first-page":"1149","article-title":"Information security in big data: Privacy and data mining","volume":"2","author":"Xu","year":"2017","journal-title":"IEEE Access"},{"issue":"2","key":"10.3233\/IDT-230247_ref10","first-page":"674","article-title":"A big data architecture design for smart grids based on random matrix theory","volume":"8","author":"Xing","year":"2017","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"2","key":"10.3233\/IDT-230247_ref11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1088\/1758-5090\/aa7279","article-title":"Current and emerging applications of 3D printing in medicine","volume":"9","author":"Liaw","year":"2017","journal-title":"Biofabrication"},{"issue":"6","key":"10.3233\/IDT-230247_ref12","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.ijpe.2016.11.008","article-title":"Adoption of 3D-printing technologies in manufacturing: A survey analysis","volume":"183","author":"Schniederjans","year":"2017","journal-title":"International Journal of Production Economics"},{"issue":"3","key":"10.3233\/IDT-230247_ref13","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2217\/3dp-2018-0012","article-title":"3D printing in dentistry","volume":"2","author":"Srinivasa","year":"2018","journal-title":"Journal of 3D Printing in Medicine"},{"issue":"9","key":"10.3233\/IDT-230247_ref14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.ijpharm.2017.09.003","article-title":"R1 Fabrication of drug-loaded hydrogels with stereolithographic 3D printing","volume":"2017","author":"Martinez","year":"2017","journal-title":"International Journal of Pharmaceutics"},{"issue":"3","key":"10.3233\/IDT-230247_ref15","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10699-016-9489-4","article-title":"The deluge of spurious correlations in big data","volume":"22","author":"Calude","year":"2017","journal-title":"Foundations of Science"},{"issue":"1","key":"10.3233\/IDT-230247_ref16","first-page":"2751","article-title":"Big privacy: Challenges and opportunities of privacy study in the age of big data","volume":"4","author":"Shui","year":"2017","journal-title":"IEEE Access"},{"issue":"1","key":"10.3233\/IDT-230247_ref17","first-page":"1","article-title":"Networking for big data: A survey","volume":"9","author":"Shui","year":"2017","journal-title":"IEEE Communications Surveys and Tutorials"},{"issue":"8","key":"10.3233\/IDT-230247_ref18","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","article-title":"A survey on deep learning for big data","volume":"42","author":"Zhang","year":"2018","journal-title":"Information Fusion"},{"issue":"10","key":"10.3233\/IDT-230247_ref19","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","article-title":"Machine learning on big data: Opportunities and challenges","volume":"237","author":"Zhou","year":"2017","journal-title":"Neurocomputing"},{"issue":"9","key":"10.3233\/IDT-230247_ref20","first-page":"1985","article-title":"Big data analytics in mobile cellular networks","volume":"4","author":"Ying","year":"2017","journal-title":"IEEE Access"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-230247","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T05:48:37Z","timestamp":1741672117000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-230247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,7]]},"references-count":20,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/idt-230247","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"type":"print","value":"1872-4981"},{"type":"electronic","value":"1875-8843"}],"subject":[],"published":{"date-parts":[[2024,6,7]]}}}