Improving innovation from science using kernel tree methods as a precursor to designed experimentation

Abstract

A key challenge in applied science when planning a designed experiment is to determine the aliasing structure of the interaction effects and selecting the appropriate levels for the factors. In this study, kernel tree methods are used as precursors to identify significant interactions and levels of the factors useful for developing a designed experiment. This approach is aligned with integrating data science with the applied sciences to reduce the time from innovation in research and development to the advancement of new products, a very important consideration in today's world of rapid advancements in industries such as pharmaceutical, medicine, aerospace, etc. Significant interaction effects for six common independent variables using boosted trees and random forests of k = 1000 and k = 10,000 bootstraps were identified from industrial databases. The four common variables were related to speed, pressing time, pressing temperature, and fiber refining. These common variables maximized tensile strength of medium density fiberboard (MDF) and the ultimate static load of oriented strand board (OSB), both widely-used industrial products. Given the results of the kernel tree methods, four possible designs with interaction effects were developed: full factorial, fractional factorial Resolution IV, Box-Behnken, and Central Composite Designs (CCD).