首页|New Findings Reported from Deakin University Describe Advances in Artificial Int elligence (Artificial Intelligence-augmented Additive Manufacturing: Insights On Closed-loop 3d Printing)
New Findings Reported from Deakin University Describe Advances in Artificial Int elligence (Artificial Intelligence-augmented Additive Manufacturing: Insights On Closed-loop 3d Printing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news reporting from Geelong, Australia, by N ewsRx journalists, research stated, “The advent of 3D printing has transformed m anufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge.” Financial support for this research came from Australian Research Council. The news correspondents obtained a quote from the research from Deakin Universit y, “Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed-loop artificial intelligence-augm ented additive manufacturing (AI2AM) technology that integrates AI-based monitor ing, automation, and optimization of printing parameters and processes. AI2AM us es AI to improve defect detection and prevention, improving additive manufacturi ng quality and efficiency. This article explores generic 3D printing processes a nd issues using existing research and developments. Next, it focuses on fused de position modeling (FDM) printers and reviews their parameters and issues. The cu rrent remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI-based 3D printing monitoring, c losed-loop feedback systems, and parameter optimization development. Finally, cl osed-loop 3D printing challenges and future directions are discussed. AI-based s ystems detect and correct 3D printing failures, enabling current printers to ope rate within optimal conditions and minimizing the risk of defects or failures, w hich in turn leads to more sustainable manufacturing with minimum waste and exte nding the library of materials. This review delves into artificial intelligence (AI)-augmented additive manufacturing, enhancing defect detection and closed-loo p optimization to boost manufacturing efficiency and quality.”
GeelongAustraliaAustralia and New Ze alandArtificial IntelligenceEmerging TechnologiesMachine LearningDeakin University
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