{"title":"An efficient and uncertainty-aware reinforcement learning framework for quality assurance in extrusion additive manufacturing","authors":"Xiaohan Li, Sebastian W. Pattinson","doi":"10.1016/j.addma.2025.104912","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><math><mrow><mn>40</mn><mo>.</mo><mn>67</mn><mo>±</mo><mn>17</mn><mo>.</mo><mn>41</mn></mrow></math></span>, <span><math><mrow><mn>44</mn><mo>.</mo><mn>00</mn><mo>±</mo><mn>13</mn><mo>.</mo><mn>56</mn></mrow></math></span>, and <span><math><mrow><mn>49</mn><mo>.</mo><mn>11</mn><mo>±</mo><mn>17</mn><mo>.</mo><mn>91</mn></mrow></math></span>, 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.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"110 ","pages":"Article 104912"},"PeriodicalIF":11.1000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Additive manufacturing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214860425002763","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
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 , , and , 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.
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.