Multivariate Analysis Applied to Discrete Part Manufacturing

Abstract

<p>The overall focus of this thesis is the implementation of a process monitoring system in a real manufacturing environment that utilizes multivariate analysis techniques to assess the state of the process. The process in question was the medium-high volume manufacturing of discrete aluminum parts using relatively simple machining processes involving the use of two tools. This work can be broken down into three main sections.</p><p>The first section involved the modeling of temperatures and thermal expansion measurements for real-time thermal error compensation. Thermal expansion of the Z-axis was measured indirectly through measurement of the two quality parameters related to this axis with a custom gage that was designed for this part. A compensation strategy is proposed which is able to hold the variation of the parts to ±0.02mm, where the tolerance is ±0.05mm.</p><p>The second section involved the modeling of the process data from the parts that included vibration, current, and temperature signals from the machine. The modeling of the process data using Principal Component Analysis (PCA), while unsuccessful in detecting minor simulated process faults, was successful in detecting a miss-loaded part during regular production. Simple control charts using Hotelling's T^2 statistic and Squared Prediction Error are illustrated. The modeling of quality data from the process data of good parts using Projection to Latent Structures by Partial Least Squares (PLS) data did not provide very accurate fits to the data; however, all of the predictions are within the tolerance specifications.</p><p>The final section discusses the implementation of a process monitoring system in both manual and automatic production environments. A method for the integration and storage of process data with Mitutoyo software MCOSMOS and MeasurLink® is described. All of the codes to perform multivariate analysis and process monitoring were written using Matlab.</p>