Design Of Experiment Eng. Ibrahim Kuhail

Introduction Two fundamental approaches to problem solving problems in the discovery of knowledge: Theoretical (physical/mathematical modeling) Experimental measurement (Most often a combination is used) DOE Lecture 1 4/19/2019

Features of Alternative Methods Introduction (Cont.) Features of Alternative Methods Theoretical Models Simplifying assumptions needed General results Less facilities usually needed Can start study immediately Experimental approach Study the “real world”-no simplifying assumptions needed Results specific to apparatus studied High accuracy measurements need complex instruments Extensive lab facilities maybe needed Time delays from building apparatus, debugging DOE Lecture 1 4/19/2019

Experimental Problems Two aspects to any experimental problem: DOE Statistical analysis. DOE Lecture 1 4/19/2019

Design of Experiments Design of Experiment (DoE): is a structured, organized method that is used to determine the relationship between the different factors (Xs) affecting a process and the output of that process (Y). DOE is the process of planning the experiment so that appropriate data will be collected, resulting in valid and objective conclusions. DOE Lecture 1 4/19/2019

Design of Experiments (Cont.) This method was first developed in the 1920s and 1930, by Sir Ronald A. Fisher, the renowned mathematician and geneticist. DOE is used for: Characterizing Processes. Optimizing Processes. Product Design. DOE Lecture 1 4/19/2019

Experiment Test or series of tests where some changes are made to the input variables of a process or system. Experimentation is very important in product design, manufacturing process, process improvements, and in developing a robust process. DOE Lecture 1 4/19/2019

Robust Process A robust process is the process that is affected minimally by external source of variability. The sensitivity of this process is high. DOE Lecture 1 4/19/2019

Objectives of Experiments Determine which variables are most influential on the response Y. Determine where to set the influential X’s, so that Y is almost near the nominal value. Determine where to set the influential X’s, so that variability in Y is very small. Determine where to set the influential X’s, so that the effect of the uncontrollable factors are minimized. DOE Lecture 1 4/19/2019

Process (System) A process or a System is the combination of machines, methods, people, and other resources that transforms some input into an output that has one or more observable responses. DOE Lecture 1 4/19/2019

General model of a process (system) DOE Lecture 1 4/19/2019

Black Box Within the black box; the following techniques may take place: Personal opinion. Scientific theory. Trial and Error. Experimentation. DOE Lecture 1 4/19/2019

Uncontrollable Factors Uncontrollable Factors are factors that you can’t control them. You must minimize their effect on the response of the process. Types of uncontrollable factors: Input Factors. Potential Input. Key Input. DOE Lecture 1 4/19/2019

Strategies of Experimentation Best-Guess Approach. In this approach, the level of one factor is switched for the next test based on the current outcome of the current test. Disadvantages: Time consuming without any guarantee of success. No guarantee on finding best solution. DOE Lecture 1 4/19/2019

Strategies of Experimentation (Cont.) One Factor At A Time Approach: In this approach; a starting point for each factor is selected, then successively varying each factor over its range with the other factors held constant at the baseline. Disadvantages: Time consuming. It doesn’t take into account any possible interaction between the factors. DOE Lecture 1 4/19/2019

Interaction: Is the failure of the factor to produce the same effect on the response at different levels of other factor. DOE Lecture 1 4/19/2019

Strategies of Experimentation (Cont.) Factorial Design: Used when dealing with several factors. Factors are varied together instead of one at a time. Based on Fisher’s factorial concept DOE Lecture 1 4/19/2019

Types of factorial experiments 22 factorial design: 4 tests each is a corner of a square. It may be done by replicating the design twice. It enables the experimenters to investigate the individual effect of each factor. It uses the data efficiently. DOE Lecture 1 4/19/2019

Types of factorial experiments (Cont.) 23 factorial design: There will be 8 tests, each is a corner of a cube. 24 factorial design: All the possible combinations of the levels of the factors are used. Fractional Factorial Experiment: Used when there are 5 or more factors. it is a variation of the basic factorial design, in which only a subset of runs are made. DOE Lecture 1 4/19/2019

Types of Factorial Experiments (Cont.) If there are K-factors, each of two levels; then the factorial design will require 2k runs DOE Lecture 1 4/19/2019

Basic Principles of Experimental Design 3 Basic principle of DOE Replication: repetition of the basic experiment Obtain an estimation of exp. error. Precise estimate of factor effect. But it is costly. Randomization: the allocation of the experimental material and the order in which the individual runs of the experiment are randomly determined By randomization; we averaging out the effect of extraneous factors that may be present. Blocking: it is a technique used to improve the precision with which comparisons among the factors of interest are made. Reduce or eliminate the variability of nuisance factors. DOE Lecture 1 4/19/2019

Nuisance Factors Nuisance Factor: Is a factor that may affect the experiment response, but in which we are not directly interested. They can be divided into: Controllable nuisance factor: a factor whose levels may be set by the experimenter. Uncontrollable nuisance factor: it is uncontrollable, but it can be measured. Covariance analysis is used to control its effect. DOE Lecture 1 4/19/2019

Nuisance Factors (Cont.) Noise factor: a factor that varies naturally and uncontrollably in the process and can be controlled for purposes of the experiment. Each factor will have levels and a range A block: Is a set of homogeneous experimental conditions. DOE Lecture 1 4/19/2019