研究人员Dhaka University of Engineering and Technologyhave 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.
在研究期间，团队评估并比较了材料的特性，包括微观结构，表面纹理，机械行为和热特性。在机器学习的帮助下，研究人员能够将给定的3D印刷产品的印刷参数相关联，以实现更可靠和增强的机械和物理质量。
Process parameters for FFF
In line with the ever-increasing demand for more complex and multi-functional 3D printed products, new materials are being continually explored for their suitability for additive manufacturing.
在研究中，研究人员专注于FFF过程，其中诸如熔体和挤压压力的动态平衡以及与温度相关的聚合物流变学对于实现最佳3D印刷部分至关重要。FFF打印零件的尺寸准确性，表面表面和机械性能受到所使用的丝的性能和质量以及相邻细丝之间的粘结的显着影响。
因此，为了优化FFF打印的零件，科学家说，了解各种过程参数设置如何影响零件的机械性能至关重要，其中最关键的是拉伸，压缩，弯曲或冲击力强度以及印刷方向。
Devising new 3D printing filaments with machine learning
该研究的主要目的是探索如何与市售产品相比，寻找3D印刷零件的更可靠和丰富的鲁棒机械和物理性能。研究人员希望该研究的发现和应用有助于开发各种与行业相关的流程。
研究小组开发了六个新细丝，其中包含PLA，HDPE，再生细丝材料和氧化钛（TiO2）纳米颗粒，以使用市售的FFF 3D打印机和细丝挤出机生产3D印刷零件。
对于两个丝，使用石墨烯来产生疏水涂层，以便可以最大程度地减少端部的原始机械性能的改变，并且只能处理零件的表面。
对于每种材料，使用机器学习建议预测喷嘴温度，而印刷床温度和打印速度也由团队的机器学习计划确定。研究人员指出，FFF打印产品的质量直接取决于所使用的材料的流动性，这可以通过准确的喷嘴温度来确保。
The researchers used machine learning programs via the python platform, which employed a linear regression algorithm to build up the relative data points. A Train/Test function was also applied to measure the suitability of the machine learning model, which divided the data into a training set and test set. This function enabled the team to visualize how well the model was generalized by comparing it with the “theoretical optimal fit”.
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.
Once printed, the materials then underwent tensile strength, elongation, hardness and thermal gravimetric analysis (TGA) tests, among several others, to evaluate the optimized properties of the printed samples.
Ultimately, the researchers aimed to deploy machine learning algorithms as a means of achieving more dependable and enhanced mechanical and physical characteristics in FFF-printed parts compared to traditional 3D printed parts. Going forwards, the researchers see the results of the study paving the way for various improvements to industry-related additive manufacturing processes.
Further information in the study can be found in the paper titled:“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.
3D打印中的机器学习
在3D打印的许多方面，机器学习的预测能力越来越多，以改善工艺和材料的开发。
Machine learning techniques have been previously leveraged by阿贡国家实验室and德克萨斯农工大学到more effectivelydetect defects in 3D printed parts，和New York University’s Tandon School of Engineering到reverse engineer glass and carbon fiber 3D printed components。
Regarding materials development, theSwinburne University of Technology已经使用了机器学习工具improve upon the properties of 3D printed construction materials, and剑桥大学spin-outintellegenshas developed anew machine learning algorithmfor designing new materials for additive manufacturing.
Most recently, researchers from利哈伊大学proposed anovel machine-learning approach到classifying groups of materials together based on their structural similarities.
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Featured image showsML process optimization model validation for 3D printer. Image via Polymer Testing.
