Identifying Simulation Model Through Alternative Techniques for a Medical Device Assembly Process
This work addresses efficiency in simulation modeling for medical device assembly engineers, but it is incremental as it applies existing techniques to a specific domain.
The paper tackled the problem of time-consuming computations in simulation models for medical device assembly by presenting two methods using Spline functions and Machine Learning to approximate the snap process, resulting in adaptable models that enhance process understanding with limited data.
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.