Abdul Rahman Sani, Ali Zolfagharian, Abbas Z. Kouzani
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Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI-based 3D printing monitoring, closed-loop feedback systems, and parameter optimization development. Finally, closed-loop 3D printing challenges and future directions are discussed. 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引用次数: 0
摘要
三维打印技术的出现改变了制造业。然而,扩展材料库以提高 3D 打印质量仍然是一项挑战。如果打印速度和温度等打印参数选择不当,就会出现缺陷。这可能会导致最终产品出现结构或尺寸问题。本综述研究了闭环人工智能增强增材制造(AI2AM)技术,该技术集成了基于人工智能的打印参数和流程的监控、自动化和优化。AI2AM 利用人工智能改进缺陷检测和预防,从而提高增材制造的质量和效率。本文利用现有的研究和开发成果,探讨了一般的 3D 打印流程和问题。接下来,它将重点关注熔融沉积成型(FDM)打印机,并回顾其参数和问题。文章介绍了目前针对 FDM 3D 打印机缺陷检测和监控开发的补救措施。然后,文章研究了基于人工智能的 3D 打印监控、闭环反馈系统和参数优化开发。最后,讨论了闭环 3D 打印面临的挑战和未来发展方向。基于人工智能的系统可检测和纠正三维打印故障,使当前的打印机在最佳条件下运行,并最大限度地降低缺陷或故障风险,进而实现更可持续的制造,减少浪费并扩展材料库。
Artificial Intelligence-Augmented Additive Manufacturing: Insights on Closed-Loop 3D Printing
The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. 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-augmented additive manufacturing (AI2AM) technology that integrates AI-based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI-based 3D printing monitoring, closed-loop feedback systems, and parameter optimization development. Finally, closed-loop 3D printing challenges and future directions are discussed. AI-based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials.