Usually, data subject to experimental error (noise) are involved, and the results can be significantly affected by noise. Genichi Taguchi in the 1980s, despite having been very controversial (described briefly in heading 2.4), had a significant impact in making statistical experimental design popular and stressed the importance it can have in terms of quality improvement. The second era for statistical experimental design began in 1951 with the work of Box and Wilson, who applied the idea to industrial experiments and developed the response surface methodology (RSM), which is used to find out the relationships between various process parameters and one or more responses. Fisher was the statistician who created the foundations for modern statistical science. Statistical experimental design, together with the basic ideas underlying DOE, was born in the 1920s from the work of Sir Ronald Aylmer Fisher. The reason to use design of experiments is to implement valid and efficient experiments that will produce quantitative results and support sound decision-making. ![]() These developments are due in part to the successful implementation of design of experiments. ![]() Design of experiments has been applied successfully in diverse fields such as agriculture (improved crop yields have created grain surpluses), the petrochemical industry (for highly efficient oil refineries) and Japanese automobile manufacturing (giving them a large market share for their vehicles), and still its implementation area is spreading and providing the optimized results. And one of the most important purposes of it is to design sampling experiments that are productive and cost-effective and provide a sufficient data base in a qualitative sense. In a general way, the process analysis can be expressed as the study of the cause-effect relationships which may be carried out by drawing inferences from a finite number of samples. DOE, or experimental design, is the name given to the techniques used for guiding the choice of the experiments to be performed in an efficient way. In today’s era, the purpose of experiments in industries is essentially optimization and robust design analysis (RDA, which is used to make the system less sensitive to variations in uncontrollable noise factors or in other words to make the system robust). The objectives of the experiment include: determining which variables are most influential on the response, determining where to set the influential controllable variables so that the response is almost always near the desired optimal value, so that the variability in the response is small, so that the effect of uncontrollable variables are minimized.” Some of the process variables are controllable, whereas other variables are uncontrollable, although they may be controllable for the purpose of a test. The process is a combination of machines, methods, people and other resources that transforms some input into an output that has one or more observable responses. “Experiments are performed in almost any field of enquiry and are used to study the performance of processes and systems. Experimentation is a frequent task in these activities to measure and analyse the output, and for this purpose engineers/researchers use many tools like statistics, analytical models, etc., regardless of their background in it. Industries are engaged in a variety of activities such as developing new products, improving previous designs, maintenance, controlling and improving ongoing processes and some more. Taguchi method is a broadly accepted method of DOE, which has proven in producing high-quality products at subsequently low cost. And Taguchi, also known as orthogonal array design, adds a new dimension to conventional experimental design. And since the last four decades, there were limitations when conventional experimental design techniques were applied to industrial experimentation. Thus, DOE using Taguchi approach has become a much more attractive tool to practicing engineers and scientists. Taguchi has standardized the methods for each of these DOE application steps. Based on years of research and applications, Dr. ![]() The aim of this chapter is to stimulate the engineering community to apply Taguchi technique to experimentation, the design of experiments, and to tackle quality problems in industrial chemical processes that they deal with. It is a statically approach where we develop the mathematical models through experimental trial runs to predict the possible output on the basis of the given input data or parameters. Design of experiment is the method, which is used at a very large scale to study the experimentations of industrial processes.
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