Statistical design of experiments (DOE) got its start in the 1920s when British statisticians Ronald Fisher and Frank Yates devised the method as a means of making breakthrough discoveries. Their brainchild involved lengthy and detailed mathematical calculations, and applying it required a deep knowledge of statistics. DOE use increased during the industri-al boom following World War II. Dur-ing the 1950s, U.S. statistician and en-gineer W. Edwards Deming introduced the idea of statistical quality control to industries in Japan, U.S. manufacturers having already rejected his theories. Statistical methods for quality con-trol and product development gained traction in the U.S. during the 1990s, with such initiatives as Total Quality Management and Six Sigma, aided by the emergence of desktop computers that took on the burden of performing the calculations and assisted in inter-preting the results. 1 The steady evolution of DOE soft-ware has made this approach more accessible to non-experts with a ba-sic knowledge of statistical principles. Today DOE is used for such things as reducing the amount of scrap in a ski manufacturer’s operations, coming up with a new paint that won’t peel off of an aluminum tractor body and ﬁnding new formulations for metalworking ﬂuids.
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ŽƌƌŽƐŝŽŶ Figure 1 | The relationship between additive packages and ﬂuid properties and performance is complicated. For example, additives that improve lubricity (solid line) also might affect emulsion stability and foaming (dashed lines). (Figure courtesy of Philip Zhao, Houghton International, Inc.) MORE INFORMATION FROM FEWER EXPERIMENTS Statistical DOE uses numerical screen-ing and modeling methods to extract the maximum amount of information from the minimum number of experi-ments. Fluid formulations developers use this systematic method to narrow their list of additive candidates, identify the formulation components that have the strongest effects on performance, examine interactions among multiple components and devise an optimum formulation window (including cost, performance and stability factors) that they can then validate in the lab. This method is widely used in many industries (pharmaceuticals, for exam-ple), but it’s just beginning to emerge in developing lubricants and metalwork-ing ﬂuids. STLE-member Yixing (Phil-ip) Zhao, senior research scientist and innovation team leader at Houghton International, Inc., in Valley Forge, Pa., notes that metalworking ﬂuid devel-opers often use a one-factor-at-a-time approach, sometimes called an Ediso-nian approach. This method is good for initial screening efforts but often falls short during optimization, he says. It’s not uncommon for a metalworking ﬂuid to have 15-20 components, and many of them interact with each other ( see Figure 1 ). “This industry really needs DOE,” Zhao says. “We’re a small industry with limited resources and a reliance on old knowledge. There’s a reluctance to use 41 Point 5: Improve constantly and forever. Emphasize training and education so everyone can do their jobs better.