Optimizing Cold Spray Deposition on Thermoplastics: A Machine Learning Approach Focused on Powder Properties

IF 2.2 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Alessia Serena Perna, Alessia Auriemma Citarella, Fabiola De Marco, Luigi Di Biasi, Antonio Viscusi, Genoveffa Tortora, Massimo Durante
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Abstract

The cold spray (CS) process offers an advanced method for metallizing thermoplastic polymers, providing a low-temperature solution to overcome the limitations of traditional coating techniques. However, optimizing the cold spray process for metallizing thermoplastic polymers is a complex task due to the numerous interacting parameters that influence coating quality. As traditional trial-and-error approaches are time-consuming and costly, machine learning (ML) could offer a solution to these challenges by providing further insights into the process and enabling more efficient optimization. The aim of this work is to identify the most relevant input parameters for ML models, with a particular focus on powder characteristics, to predict two critical outcomes: particle flattening and penetration depth. Two distinct datasets were created for this study: one focused on particle yield strength and the other on powder density, each combined with further input parameters like impact velocity and substrate yield strength. These datasets were constructed using experimental data and finite element modeling (FEM) simulations, with materials including copper, aluminum, titanium, and others, applied to thermoplastic substrates like polyether ether ketone (PEEK), acrylonitrile butadiene styrene (ABS), and polyamide 66 (PA66). Several ML algorithms, including decision trees, neural networks, and Gaussian process regression, were tested to predict coating behavior, and the effects of Z-score normalization were evaluated for improving model stability and prediction accuracy. The results show that particle yield strength is crucial for flattening, while particle density primarily governs penetration depth. This study demonstrates that ML, when combined with a solid understanding of the process, offers an effective framework for optimizing CS deposition on polymers.

优化热塑性塑料冷喷涂沉积:一种专注于粉末性能的机器学习方法
冷喷涂(CS)工艺为热塑性聚合物金属化提供了一种先进的方法,为克服传统涂层技术的局限性提供了低温解决方案。然而,优化热塑性聚合物金属化冷喷涂工艺是一项复杂的任务,因为影响涂层质量的相互作用参数众多。由于传统的试错法既耗时又昂贵,机器学习(ML)可以通过进一步深入了解流程并实现更有效的优化,为这些挑战提供解决方案。这项工作的目的是确定ML模型最相关的输入参数,特别关注粉末特性,以预测两个关键结果:颗粒平坦化和渗透深度。为这项研究创建了两个不同的数据集:一个专注于颗粒屈服强度,另一个专注于粉末密度,每个数据集都结合了进一步的输入参数,如冲击速度和基材屈服强度。这些数据集使用实验数据和有限元模拟(FEM)构建,材料包括铜、铝、钛等,应用于聚醚醚酮(PEEK)、丙烯腈丁二烯苯乙烯(ABS)和聚酰胺66 (PA66)等热塑性基材。我们测试了包括决策树、神经网络和高斯过程回归在内的几种ML算法来预测涂层行为,并评估了z分数归一化对提高模型稳定性和预测精度的影响。结果表明,颗粒屈服强度对压扁至关重要,而颗粒密度主要决定渗透深度。这项研究表明,当结合对过程的深刻理解时,ML为优化聚合物上的CS沉积提供了有效的框架。
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来源期刊
Journal of Materials Engineering and Performance
Journal of Materials Engineering and Performance 工程技术-材料科学:综合
CiteScore
3.90
自引率
13.00%
发文量
1120
审稿时长
4.9 months
期刊介绍: ASM International''s Journal of Materials Engineering and Performance focuses on solving day-to-day engineering challenges, particularly those involving components for larger systems. The journal presents a clear understanding of relationships between materials selection, processing, applications and performance. The Journal of Materials Engineering covers all aspects of materials selection, design, processing, characterization and evaluation, including how to improve materials properties through processes and process control of casting, forming, heat treating, surface modification and coating, and fabrication. Testing and characterization (including mechanical and physical tests, NDE, metallography, failure analysis, corrosion resistance, chemical analysis, surface characterization, and microanalysis of surfaces, features and fractures), and industrial performance measurement are also covered
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