材料

研究人员通过机器学习评估从回收塑料中注入纳米颗粒的3D印刷材料

来自达卡工程技术大学have developed and analyzed 3D printed nanoparticle-infused plastic materials using machine learning techniques.

Designed for the FFF 3D printing process, the six new filaments are made up of a combination of recycled plastics and nanoparticles, with a graphene coating used in two of the materials.

During the study, the team evaluated and compared the materials’ properties, including microstructure, surface texture, mechanical behavior and thermal characteristics. With the help of machine learning, the researchers were able to correlate the printing parameters for given 3D printed products in order to achieve more reliable and enhanced mechanical and physical qualities.

颗粒的混合物:PLA和HDPE,b。PLA,HDPE和TIO2,c。再生塑料,PLA和HDPE,d。再生塑料,PLA,HDPE和TIO2。通过聚合物测试图像。
颗粒的混合物:PLA和HDPE,b。PLA,HDPE和TIO2,c。再生塑料,PLA和HDPE,d。再生塑料,PLA,HDPE和TIO2。通过聚合物测试图像。

Process parameters for FFF

根据对更复杂和多功能的3D印刷产品的不断增长的需求,不断探索新材料,以适合其对增材制造。雷电竞充值

In the study, the researchers focused on the FFF process, where factors such as the dynamic equilibrium of the melt and extrudate pressure and the polymer rheology associated with temperature are critical to achieving optimal 3D printed parts. The dimensional accuracy, surface finish, and mechanical performance of FFF-printed parts are significantly impacted by the performance and quality of the filament being used, as well as the bonding between adjoining filaments.

To optimize FFF-printed parts, therefore, the scientists say it is vital to understand how various process parameter settings affect the parts’ mechanical properties, the most crucial of which are tensile, compression, flexural or impact strengths, and print orientation.

Filament extruded with the mixture of a. PLA and HDPE, b. PLA and HDPE, c. PLA, HDPE, and TiO2, d. recycled plastic, PLA and HDPE, e. recycled plastic, PLA and HDPE, f. recycled plastic, PLA, HDPE, and TiO2. Image via Polymer Testing.
Filament extruded with the mixture of a. PLA and HDPE, b. PLA and HDPE, c. PLA, HDPE, and TiO2, d. recycled plastic, PLA and HDPE, e. recycled plastic, PLA and HDPE, f. recycled plastic, PLA, HDPE, and TiO2. Image via Polymer Testing.

通过机器学习设计新的3D打印丝

The main aim of the study was to explore how to seek more reliable and enriched robust mechanical and physical properties of 3D printed parts compared to commercially available products. The researchers expect the study’s findings and applications to contribute to developing various industry-related processes.

The research team developed six new filaments containing PLA, HDPE, recycled filament material, and titanium oxide (TiO2) nanoparticles to produce 3D printed parts using a commercially available FFF 3D printer and filament extruder.

For two of the filaments, graphene was used to create a hydrophobic coating so that alteration of the original mechanical properties of the end-parts could be minimized, and only the surface of the part would be treated.

For each material, the nozzle temperature was predicted using machine learning suggestions, while print bed temperature and print speed were also determined by the team’s machine learning programs. The researchers stated the quality of FFF-printed products is directly dependent on the flowability of the materials used, which is ensured by accurate nozzle temperatures.

一种。3D中的模型图像,b。45度打印方向。通过聚合物测试图像。
一种。3D中的模型图像,b。45度打印方向。通过聚合物测试图像。

研究人员通过Python平台使用了机器学习程序,该平台采用线性回归算法来构建相对数据点。还应用了火车/测试功能来衡量机器学习模型的适用性,该模型将数据分为训练集和测试集。该功能使团队能够通过将模型与“理论最佳拟合”进行比较来可视化模型的推广程度。

The model was declared valid as the testing data fit with the training data set, meaning the predicted nozzle temperature was good enough to print the samples. Nozzle temperature was highest for the materials comprised of nanoparticle and recycled-based plastics, as was suggested by the machine learning program, while the print speed as at the minimum range when the bed temperature of the printer was at the maximum level.

印刷后,材料随后进行了拉伸强度,伸长,硬度和热重量分析(TGA)测试,以及其他几种测试,以评估印刷样品的优化特性。

一种。SolidWorks model, b. FFF 3D printer, c. printed specimens. Image via Polymer Testing.
一种。SolidWorks model, b. FFF 3D printer, c. printed specimens. Image via Polymer Testing.

最终,与传统的3D印刷零件相比,研究人员旨在将机器学习算法部署为FFF打印零件中更可靠和增强的机械和物理特性的一种手段。展望未来,研究人员看到了研究结果为与行业相关的添加剂制造过程的各种改进铺平了道路。雷电竞充值

研究中的更多信息可以在标题为:“Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer,”published in the Polymer Testing journal. The study was co-authored by M. Hossain, M. Chowdhury, M. Zahid, C. Sakib-Uz-Zaman, M. Rahaman, and M. Kowser.

ML process optimization model validation for 3D printer. Image via Polymer Testing.
ML process optimization model validation for 3D printer. Image via Polymer Testing.

Machine learning in 3D printing

The predictive power of machine learning is being leveraged more and more in many aspects of 3D printing to improve processes and materials development.

机器学习技术以前已经由Argonne National LaboratoryTexas A&M Universityto more effectivelydetect defects in 3D printed parts, 和byNew York University’s Tandon School of Engineeringtoreverse engineer glass and carbon fiber 3D printed components.

关于材料开发,Swinburne University of Technologyhas used machine learning tools to改善3D打印建筑材料的特性, 和University of Cambridge旋转Intellegens已经开发了一个new machine learning algorithm用于设计用于增材制造的新材料。雷电竞充值

Most recently, researchers fromLehigh University提议anovel machine-learning approachto classifying groups of materials together based on their structural similarities.

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特色图片显示ML process optimization model validation for 3D printer. Image via Polymer Testing.

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