Electromagnetic actuators are used in a variety of technical applications especially in the automotive industry. In-line process control methods are an essential component of the Lean and Six Sigma methodology to ensure process quality. However, the current state of the art in process and quality control is largely limited to end-of-line measurements of the force output. Analysing the magnetic stray field is a promising method that can be used to draw conclusions on the properties and defects of the flux-conducting magnetic materials. This phenomenon can potentially be used to identify defects in magnetic actuators thus allowing inline quality-monitoring. In order to realize this feature, patterns in the magnetic stray field of an actuator have to be identified and linked to a specific defect. The resulting challenge is the analysis of large datasets in order to characterize the stray field anomalies. This paper summarizes the results of a study on linear magnetic actuators trying to prove a relationship between parasitic magnetic stray field and the overall force output of an actuator by analysing the data with statistical methods. The findings of this study suggest that certain statistical methods, like regression, are not well suited to build a prediction model for defects in actuators using a similar approach of measuring stray field outside the actuator. This is mainly due to the fact that prerequisites for model building are difficult to full fill within the context of stray field analysis. Nevertheless, the findings also suggest that methods of exploratory data analysis can be used to derive quality relevant information from data of stray field measurements. The paper elaborates on the problem of defining a population, choosing variables for model building, as well as model error.
Read full abstract