High-throughput in-situ mechanical evaluation and parameter optimization for 3D printing of continuous carbon fiber composites

IF 5.3 Q2 MATERIALS SCIENCE, COMPOSITES
Yuichiro Yuge, Ryosuke Matsuzaki
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引用次数: 0

Abstract

The mechanical properties of carbon fiber reinforced thermoplastic (CFRTP) molded parts produced by thermal fusion lamination 3D printing vary with printing conditions. This study assesses the influence of the 3D printing parameters on the mechanical properties of resulting CFRTP products through parameter evaluation testing. An in-situ three-point bending test mechanism was developed to enhance the efficiency of these tests, allowing the same 3D printer to handle all processes from printing multiple CFRTP specimens simultaneously to conducting a bending test, reducing manual handling time to about one minute. Using this modified 3D printer, 700 specimens with varying printing conditions were produced, and their flexural strength was measured semi-automatically. Results revealed that the flexural strength of the 3D-printed CFRTP object varied with nozzle temperature, printing pitch, and stacking pitch, but not with printing speed. Machine learning was then employed to predict the maximum flexural strength and determine optimal printing parameters using the collected data as training data.
用于连续碳纤维复合材料三维打印的高通量原位力学评估和参数优化
通过热熔层压 3D 打印技术生产的碳纤维增强热塑性塑料(CFRTP)模塑件的机械性能随打印条件的不同而变化。本研究通过参数评估测试评估了三维打印参数对所生产的 CFRTP 产品机械性能的影响。为了提高这些测试的效率,我们开发了一种原位三点弯曲测试机制,使同一台 3D 打印机能够处理从同时打印多个 CFRTP 试样到进行弯曲测试的所有过程,将人工处理时间减少到约一分钟。使用这种改进的三维打印机,制作了 700 个不同打印条件的试样,并对其抗弯强度进行了半自动测量。结果显示,三维打印 CFRTP 物体的抗弯强度随喷嘴温度、打印间距和堆叠间距的变化而变化,但与打印速度无关。随后,利用机器学习预测了最大弯曲强度,并以收集到的数据作为训练数据确定了最佳打印参数。
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
Composites Part C Open Access
Composites Part C Open Access Engineering-Mechanical Engineering
CiteScore
8.60
自引率
2.40%
发文量
96
审稿时长
55 days
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