This scientific paper explores two distinct approaches for identifying and approximating the simulation model, particularly in the context of the snap process crucial to medical device assembly. Simulation models play a pivotal role in providing engineers with insights into industrial processes, enabling experimentation and troubleshooting before physical assembly. However, their complexity often results in time-consuming computations. To mitigate this complexity, we present two distinct methods for identifying simulation models: one utilizing Spline functions and the other harnessing Machine Learning (ML) models. Our goal is to create adaptable models that accurately represent the snap process and can accommodate diverse scenarios. Such models hold promise for enhancing process understanding and aiding in decision-making, especially when data availability is limited.

本研究探讨了两种不同的方法来识别和近似仿真模型，尤其是在医疗器械装配的关键性切合过程中。这些模型对工程师提供了对工业过程的洞察力，能够在物理装配之前进行实验和故障排除。然而，由于其复杂性，常常需要耗费大量的计算时间。为了解决这个问题，我们提出了两种不同的方法来识别仿真模型：一种利用样条函数，另一种利用机器学习模型。我们的目标是创建适应性模型，准确地代表切合过程并适应各种情况。这样的模型有望提高工艺理解，并在数据有限的情况下辅助决策。

通过替代技术识别医疗器械组装过程的仿真模型