Developing the optimized control scheme for digital light processing 3D printing by combining numerical simulation and machine learning-guided temperature prediction

IF 6.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
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引用次数: 0

Abstract

Digital light processing (DLP) 3D printing has attracted significant attention for its rapid printing speed, high accuracy, and diverse applications. However, the continuous DLP printing process releases substantial heat, resulting in a swift temperature rise in the curing area, which may lead to printing failures. Due to the lack of effective means to measure real-time temperature changes of the curing surface during continuous DLP 3D printing, the prevailing approach is to predict temperature variations during printing via numerical simulation. Nevertheless, temperature prediction methods relying solely on numerical simulation tend to be slow and overlook heat exchange dynamics during printing, potentially resulting in prediction inaccuracies, particularly for complex models. To address these issues, this paper proposes a method to combine numerical simulation and a machine learning approach for temperature prediction in the DLP 3D printing process, along with a printing control scheme generation method. Firstly, the m+nth order autocatalytic kinetic model considering the light intensity and the Beer–Lambert law are employed to formulate the heat calculation equation for the photopolymer resin curing reaction. Subsequently, a heat exchange calculation equation is established based on Fourier heat conduction law and Newton’s cooling equation. A numerical simulation model for temperature changes during the printing process is then developed by integrating the heat calculation equation, heat exchange calculation equation, and measurement data from Photo-DSC. Furthermore, a temperature measurement device for the printing process is designed to validate the accuracy of the numerical simulation. Following this, an improved Long Short-term Memory (LSTM) network is proposed, using temperature change data generated by the numerical simulation model to train the network for rapid (2×104 s/layer) prediction of temperature changes during printing. Finally, aiming for the shortest printing time, an optimized control scheme planning algorithm and a target function are designed based on the model’s temperature change data and the monomer’s flash point to ensure the temperature remains below this threshold. This algorithm can automatically generate the optimal printing control scheme for any model. Experimental results demonstrate that the proposed temperature prediction method can predict temperature variation accurately. Based on this, the generated printing control scheme can guarantee efficient and high-quality manufacturing for anymodel.
结合数值模拟和机器学习引导的温度预测,开发数字光处理 3D 打印的优化控制方案
数字光处理(DLP)三维打印因其打印速度快、精度高和应用多样化而备受关注。然而,连续 DLP 打印过程会释放大量热量,导致固化区域温度迅速升高,从而可能导致打印失败。由于缺乏有效的方法来测量连续 DLP 3D 打印过程中固化表面的实时温度变化,目前普遍采用的方法是通过数值模拟来预测打印过程中的温度变化。然而,仅仅依靠数值模拟的温度预测方法往往速度较慢,而且会忽略打印过程中的热交换动态,从而可能导致预测不准确,尤其是对于复杂的模型。为了解决这些问题,本文提出了一种结合数值模拟和机器学习的方法,用于 DLP 3D 打印过程中的温度预测,并提出了一种打印控制方案生成方法。首先,采用考虑光照强度和比尔-朗伯定律的 m+nth 阶自催化动力学模型,建立光聚合物树脂固化反应的热量计算方程。随后,根据傅立叶热传导定律和牛顿冷却方程建立了热交换计算方程。然后,通过整合热量计算公式、热交换计算公式和光致发光扫描仪的测量数据,建立了印刷过程中温度变化的数值模拟模型。此外,还设计了用于印刷过程的温度测量装置,以验证数值模拟的准确性。随后,利用数值模拟模型生成的温度变化数据,提出了一种改进的长短期记忆(LSTM)网络,用于训练网络快速(2×10-4 s/层)预测印刷过程中的温度变化。最后,以最短印刷时间为目标,根据模型的温度变化数据和单体闪点设计了优化控制方案规划算法和目标函数,以确保温度保持在该阈值以下。该算法可为任何模型自动生成最佳印刷控制方案。实验结果表明,所提出的温度预测方法可以准确预测温度变化。在此基础上,生成的印刷控制方案可以保证任何模型的高效和高质量制造。
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来源期刊
Journal of Manufacturing Processes
Journal of Manufacturing Processes ENGINEERING, MANUFACTURING-
CiteScore
10.20
自引率
11.30%
发文量
833
审稿时长
50 days
期刊介绍: The aim of the Journal of Manufacturing Processes (JMP) is to exchange current and future directions of manufacturing processes research, development and implementation, and to publish archival scholarly literature with a view to advancing state-of-the-art manufacturing processes and encouraging innovation for developing new and efficient processes. The journal will also publish from other research communities for rapid communication of innovative new concepts. Special-topic issues on emerging technologies and invited papers will also be published.
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