  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.
