The application of Statistical Process Control and Design of Experiment methods in the research laboratory can lead to significant gains in the Powertrain development process. Empirical methods such as Design of Experiments, Regression, and Neural Network techniques can be applied to help researchers gain better understanding of the cause and effect relationships of emission, alternative fuel source, performance, fuel economy, and engine management system - calibration studies. The use of these empirical modeling techniques along with model based Genetic Algorithm, Gradient, or Constraint based solution search methods will help identify the “process settings” that improve fuel economy, improve performance, and reduce pollutants.
Since empirical methods are fundamentally based on the acquired test data, it is vitally important that the laboratory measurements are repeatable, consistent, and void of sources of variance that have a significant effect on the acquired test data. If significant sources of variance are “contained” within the acquired data, then the subsequent data analysis, solution results, and final conclusions of the study may be jeopardized. With Statistical Process Control and Design of Experiments, “Out of Control” or systemic process problems in the Powertrain Laboratory can be identified and eliminated. Thus, Statistical Process Control and Design of Experiments are invaluable methods and essential elements of a variance reduction program.
This paper will discuss how these methods can be an integral part of powertrain development. Ultimately, Design of Experiments and Statistical Process Control methods are the key ingredients of a successful powertrain research program.