Cold spray (CS) particle deposition, also known as cold spray additive manufacturing, presents opportunities for high-throughput functional metallization on polymeric substrates. However, modeling CS-based polymer metallization and quantifying deposition probability face challenges due to the need for dedicated and cost-intensive experimental characterization tools. This underscores a critical need for predictive approaches such as numerical modeling. Toward this end, the present work aims to address this critical gap through numerical modeling by utilizing the three-network polymer model (TNM) in a manner that deposition probability can be predicted under the given CS process settings. In this regard, CS of both hard and soft particles with varying densities and diameters was modeled, followed by experimental validation. Notably, a dimensional number (η) – representing the fraction of the particle kinetic energy - was derived as a predictive tool to estimate the CS metallization probability on polymeric substrates. Furthermore, the modeling endeavor was extended to develop a correlation between the η number and the percent area coverage of the CS process. It was found that value should be higher than 0.8 for effective CS polymer metallization. Controlled experiments confirmed the viability and reliability of the numerical modeling as a high-fidelity predictive methodology for the CS metallization on polymers, thereby minimizing the need for cost-intensive trial-and-error efforts.

中文翻译：

---

聚合物冷喷涂金属化的预测建模方法

冷喷涂 (CS) 颗粒沉积，也称为冷喷涂增材制造，为聚合物基材上的高通量功能金属化提供了机会。然而，由于需要专用且成本密集的实验表征工具，基于 CS 的聚合物金属化建模和量化沉积概率面临挑战。这强调了对数值建模等预测方法的迫切需求。为此，目前的工作旨在通过利用三网络聚合物模型（TNM）进行数值建模来解决这一关键差距，从而可以在给定的 CS 工艺设置下预测沉积概率。在这方面，我们对具有不同密度和直径的硬颗粒和软颗粒的 CS 进行了建模，然后进行了实验验证。值得注意的是，我们导出了代表粒子动能分数的维数 (η)，作为预测工具来估计聚合物基材上的 CS 金属化概率。此外，建模工作还得到扩展，以开发 η 数和 CS 过程的面积覆盖百分比之间的相关性。结果发现，对于有效的 CS 聚合物金属化，该值应高于 0.8。对照实验证实了数值模型作为聚合物上 CS 金属化的高保真预测方法的可行性和可靠性，从而最大限度地减少了成本密集型试错工作的需要。