Additive Manufacturing (AM) is a powerful technology that produces complex 3D geometries using various materials in a layer-by-layer fashion. However, quality assurance is the main challenge in AM industry due to the possible time-varying processing conditions during AM process. Notably, new defects may occur during printing, which cannot be mitigated by offline analysis tools that focus on existing defects. This challenge motivates this work to develop online learning-based methods to deal with the new defects during printing. Since AM typically fabricates a small number of customized products, this paper aims to create an online learning-based strategy to mitigate the new defects in AM process while minimizing the number of samples needed. The proposed method is based on model-free Reinforcement Learning (RL). It is called Continual G-learning since it transfers several sources of prior knowledge to reduce the needed training samples in the AM process. Offline knowledge is obtained from literature, while online knowledge is learned during printing. The proposed method develops a new algorithm for learning the optimal defect mitigation strategies proven the best performance when utilizing both knowledge sources. Numerical and real-world case studies in a fused filament fabrication (FFF) platform are performed and demonstrate the effectiveness of the proposed method.
翻译:添加制造(AM)是一种强大的技术,它利用各种材料逐层生成复杂的3D地形。然而,质量保证是AM行业的主要挑战,因为AM过程中可能出现时间变化的处理条件。值得注意的是,印刷过程中可能会出现新的缺陷,而这种缺陷无法通过侧重于现有缺陷的离线分析工具来减轻。这一挑战促使这项工作开发在线学习方法,以应对印刷过程中的新缺陷。由于AM通常制造少量定制产品,本文件旨在创建在线学习战略,以减轻AM过程中的新缺陷,同时尽量减少所需的样本数量。拟议方法基于无模式强化学习(RL),因为其称为Continual G-learning,因为它转让了以前的若干知识来源,以减少AM过程中所需的培训样本。从文献中获取离线知识,同时在打印过程中学习在线知识。拟议方法为学习最佳缺陷缓解战略开发了一种新的算法,在使用知识来源时证明了最佳的绩效。在精细纤维纤维制造(FFF)平台中,进行了数字和真实的案例研究。