Identifying Simulation Model Through Alternative Techniques for a Medical Device Assembly Process

TL;DR

This paper compares two methods, spline functions and machine learning, for efficiently identifying simulation models of a medical device assembly process, aiming to improve accuracy and adaptability with limited data.

Contribution

It introduces and evaluates two novel approaches for simulation model identification in medical device assembly, enhancing process understanding and decision-making.

Findings

Spline-based models effectively approximate the snap process.

Machine learning models provide adaptable and accurate simulations.

Both methods reduce computational complexity compared to traditional models.

Abstract

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.