OMG Borchers used the design of experiments method to develop a mixture experiment to screen the effects of four candidate additives. The challenge was to find a second source for an associative thickener used in a family of waterborne coatings that matched the properties of the incumbent thickener in three different classes of products with a different chemical composition than the incumbent.
An industrial equipment supplier wanted to find the best operating conditions, as well as determine what performance its product could deliver for ethanol producers, before putting the device on the market. A DOE was run to successfully identify and validate a measurement method that has enabled the supplier to accurately evaluate the performance of the new product in a large number of plants under a wide range of operating conditions.
This article provides insights on how many runs are required to make it very likely that a test will reveal any important effects. Due to the mathematical complexities of multifactor Design of Experiments (DOE) matrices, the calculations for adequate power and precision (Oehlert and Whitcomb 2002) are not practical to do by 'hand' so the focus is kept at a high level--scoping out the forest rather than detailing all the trees. By example, reader will learn the price that must be paid for an adequately-sized experiment and the penalty incurred by conveniently grouping hard-to-change factors. (The article is not available on the ITEA Journal web site without membership. Click on the "Download" link to view the manuscript.)
Due to operational or physical considerations, standard factorial and response surface method (RSM) design of experiments (DOE) often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess “goodness of design” such evaluations must consider the model choice, specific optimality criteria (in particular D and I), precision of estimation based on the fraction of design space (FDS), the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.
In an effort to recover additional copper and gold at KGHM International's Robinson Mine located near Ruth, Nevada, an in-plant study was undertaken to quantify potential flotation recoveries from the concentrator's final tailings stream. Tests were conducted by passing a small continuous sample of final tailings through a single 1.5 m3 FLSmidth XCELL™ demonstration flotation machine. This paper reviews the results obtained from the in-plant testing with the single 1.5 m3 flotation cell and provides a comparison to the subsequent operational performance of multiple 160 m3 flotation machines
Design of experiments (DOE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DOE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DOE in non-manufacturing arena are gathered.
Our new version of Design-Expert now offers additional optimal design options not just D-optimal. The most popular of these new options is likely to be the 'IV ' optimal design, which makes use of an integrated variance criterion that minimizes the average variance for responses throughout a region of interest. An IV-optimal design tends to place fewer runs at the extremes of the experimental region than D-optimal.
To accelerate their product development, Z Corporation tooled up their engineers with the knowledge and software to do statistical design of experiments (DOE). The company developed a procedure by which every factor with a reasonable chance of affecting product performance is systematically and simultaneously evaluated via these controlled experiments.
Optimizing biological assays conditions is often a challenging process facing scientists. The demand to produce quality and robust assays that work across a range of biological conditions is often strived for along with a short development timeframe. In addition, automated systems are often required to enable scientists to screen in a high-throughput environment.