An efficient and uncertainty-aware reinforcement learning framework for quality assurance in extrusion additive manufacturing

IF 11.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Xiaohan Li, Sebastian W. Pattinson
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引用次数: 0

Abstract

Defects in extrusion additive manufacturing remain common despite its prevalent use. While numerous AI-based quality assurance approaches have been proposed, the dynamic nature of printing processes often causes deterministic models to lose robustness and, in some cases, fail entirely in new or slightly altered environments. This work introduces an agent that adjusts flow rate and temperature in real-time to optimize control while addressing bottlenecks in training efficiency and uncertainty management. A vision-based uncertainty quantification module generates probabilistic distributions from classified extrusion states, which are integrated with a deep Q-learning controller. While the underlying networks are deterministic, the evolving distributions introduce adaptability to the decision-making process. The controller learns optimal asynchronous actions in a simulation calibrated to vision accuracy and trained with progressively tightened elliptically shaped rewards that account for parameter coupling. With zero-shot learning, the agent bridges the sim-to-real gap and reliably corrects 21 tests across three extrusion error levels—slight, moderate, and severe—with average convergence steps of 40.67±17.41, 44.00±13.56, and 49.11±17.91, respectively. The modest increase in convergence steps and stable standard deviations across error levels underscore the controller’s effectiveness and robustness. Beyond extrusion, this scalable framework supports practical AI-driven quality assurance across various additive manufacturing.
一种用于挤压增材制造质量保证的高效不确定性强化学习框架
尽管普遍使用,但挤压增材制造中的缺陷仍然很常见。虽然已经提出了许多基于人工智能的质量保证方法,但打印过程的动态性经常导致确定性模型失去鲁棒性,并且在某些情况下,在新的或稍微改变的环境中完全失败。本研究介绍了一种实时调节流量和温度的智能体,以优化控制,同时解决了训练效率和不确定性管理的瓶颈。基于视觉的不确定性量化模块从分类的挤压状态生成概率分布,并与深度q -学习控制器集成。虽然底层网络是确定性的,但不断发展的分布为决策过程引入了适应性。控制器在校准视觉精度的仿真中学习最优异步动作,并使用考虑参数耦合的逐渐收紧的椭圆形奖励进行训练。通过零射击学习,该智能体弥补了模拟与真实之间的差距,并可靠地纠正了三种挤压误差级别(轻微、中等和严重)的21个测试,平均收敛步分别为40.67±17.41、44.00±13.56和49.11±17.91。收敛步骤的适度增加和跨误差水平的稳定标准差强调了控制器的有效性和鲁棒性。除了挤压,这个可扩展的框架支持各种增材制造中实用的人工智能驱动的质量保证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Additive manufacturing
Additive manufacturing Materials Science-General Materials Science
CiteScore
19.80
自引率
12.70%
发文量
648
审稿时长
35 days
期刊介绍: Additive Manufacturing stands as a peer-reviewed journal dedicated to delivering high-quality research papers and reviews in the field of additive manufacturing, serving both academia and industry leaders. The journal's objective is to recognize the innovative essence of additive manufacturing and its diverse applications, providing a comprehensive overview of current developments and future prospects. The transformative potential of additive manufacturing technologies in product design and manufacturing is poised to disrupt traditional approaches. In response to this paradigm shift, a distinctive and comprehensive publication outlet was essential. Additive Manufacturing fulfills this need, offering a platform for engineers, materials scientists, and practitioners across academia and various industries to document and share innovations in these evolving technologies.
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