Optimization and Performance Improvement of Solenoid Systems with Artificial Intelligence and Machine Learning Algorithm

Solenoid plays a vital role in many applications across several industries, including automotive systems, industrial automations, and consumer electronics. To increase energy efficiency, cut costs and improve system functionality, it is crucial to achieve optimum Solenoid performance. The traditional design process is time-consuming because multiple design revisions might be needed. This work presents an innovative approach with combination of Finite Element Analysis and artificial intelligence, machine learning methods to address the issue. It starts with collecting information on Solenoid design parameters such as input current, number of windings, material properties and air gap. For data creation, a parametric analysis is conducted utilizing the Finite Element Analysis software. Different machine learning algorithms were tested using the Commercial software module to find relationships between the input parameters and the intended output (in this case, electromagnetic force). Additionally, algorithms based on artificial intelligence and machine learning are used to precisely forecast and optimize design parameters. Finally, FEA and data driven approaches are combined for reduction in lead time and cost for the designs of Solenoids.