Presentation on theme: "Taguchi Design of Experiments"— Presentation transcript:
1Taguchi Design of Experiments Many factors/inputs/variables must be taken into consideration when making a product especially a brand new oneThe Taguchi method is a structured approach for determining the ”best” combination of inputs to produce a product or serviceBased on a Design of Experiments (DOE) methodology for determining parameter levelsDOE is an important tool for designing processes and productsA method for quantitatively identifying the right inputs and parameter levels for making a high quality product or serviceTaguchi approaches design from a robust design perspective
2Taguchi methodTraditional Design of Experiments focused on how different design factors affect the average result levelIn Taguchi’s DOE (robust design), variation is more interesting to study than the averageRobust design: An experimental method to achieve product and process quality through designing in an insensitivity to noise based on statistical principles.
3Robust DesignA statistical / engineering methodology that aim at reducing the performance “variation” of a system.The input variables are divided into two board categories.Control factor: the design parameters in product or process design.Noise factor: factors whoes values are hard-to-control during normal process or use conditions
4The Taguchi Quality Loss Function The traditional model for quality lossesNo losses within the specification limits!Scrap CostLSLUSLTargetCostThe Taguchi loss functionthe quality loss is zero only if we are on target
5Example (heat treatment process for steel) Heat treatment process used to harden steel componentsDetermine which process parameters have the greatest impact on the hardness of the steel componentsunitLevel 2Level 1ParametersParameter numberOC900760Temperature1OC/s14035Quenching rate2s300Cooling time3Wt% c6Carbon contents4%205Co 2 concentration
6Taguchi methodTo investigate how different parameters affect the mean and variance of a process performance characteristic.The Taguchi method is best used when there are an intermediate number of variables (3 to 50), few interactions between variables, and when only a few variables contribute significantly.
7Two Level Fractional Factorial Designs As the number of factors in a two level factorial design increases, the number of runs for even a single replicate of the 2k design becomes very large.For example, a single replicate of an 8 factor two level experiment would require 256 runs.Fractional factorial designs can be used in these cases to draw out valuable conclusions from fewer runs.The principle states that, most of the time, responses are affected by a small number of main effects and lower order interactions, while higher order interactions are relatively unimportant.
8Half-Fraction Designs A half-fraction of the 2k design involves running only half of the treatments of the full factorial design. For example, consider a 23 design that requires 8 runs in all.A half-fraction is the design in which only four of the eight treatments are run. The fraction is denoted as 2 3-1with the “-1 " in the index denoting a half-fraction.In the next figure: Assume that the treatments chosen for the half-fraction design are the ones where the interaction ABC is at the high level (1). The resulting 23-1 design has a design matrix as shown in Figure (b).
9Half-Fraction Designs 23No. of runs = 82 3-1I= ABCNo. of runs = 42 3-1I= -ABCNo. of runs = 4
10Half-Fraction Designs The effect, ABC , is called the generator or word for this designThe column corresponding to the identity, I , and column corresponding to the interaction , ABC are identical.The identical columns are written as I= ABC and this equation is called the defining relation for the design.
11Quarter and Smaller Fraction Designs A quarter-fraction design, denoted as 2k-2 , consists of a fourth of the runs of the full factorial design.Quarter-fraction designs require two defining relations.The first defining relation returns the half-fraction or the 2 k-1design. The second defining relation selects half of the runs of the 2k-1 design to give the quarter-fraction.Figure a, I= ABCD 2k-1. Figure b, I=AD 2k-2
12Quarter and Smaller Fraction Designs I= ABCD24-1I=AD24-2
13Taguchi's Orthogonal Arrays Taguchi's orthogonal arrays are highly fractional orthogonal designs. These designs can be used to estimate main effects using only a few experimental runs.Consider the L4 array shown in the next Figure. The L4 array is denoted as L4(2^3).L4 means the array requires 4 runs. 2^3 indicates that the design estimates up to three main effects at 2 levels each. The L4 array can be used to estimate three main effects using four runs provided that the two factor and three factor interactions can be ignored.
15Taguchi's Orthogonal Arrays Figure (b) shows the 2III3-1 design (I = -ABC, defining relation ) which also requires four runs and can be used to estimate three main effects, assuming that all two factor and three factor interactions are unimportant.A comparison between the two designs shows that the columns in the two designs are the same except for the arrangement of the columns.
18Analyzing Experimental Data To determine the effect each variable has on the output, the signal-to-noise ratio, or the SN number, needs to be calculated for each experiment conducted.yi is the mean value and si is the variance. yi is the value of the performance characteristic for a given experiment.
20Worked out ExampleA microprocessor company is having difficulty with its current yields. Silicon processors are made on a large die, cut into pieces, and each one is tested to match specifications.The company has requested that you run experiments to increase processor yield. The factors that affect processor yields are temperature, pressure, doping amount, and deposition rate.a) Question: Determine the Taguchi experimental design orthogonal array.
26Worked out Example… Solution: The L9 orthogonal array should be used. The filled in orthogonal array should look like this:This setup allows the testing of all four variables without having to run 81 (=34)
27Selecting the proper orthogonal array by Minitab Software
28Worked out Example…b) Question: Conducting three trials for each experiment, the data below was collected. Compute the SN ratio for each experiment for the target value case, create a response chart, and determine the parameters that have the highest and lowest effect on the processor yield.
29Worked out Example…Standard deviationMeanTrial 3Trial 2Trial 1Deposition RateDoping AmountPressureTemperatureExperiment Number8.580.170.782.387.30.14210015.969.663.274.80.2655.852.445.754.956.50.3839.773.462.378.279.815012.776.577.386.583.2894.760.955.764.8200793.2996.8717475.79
31Worked out Example… b) Solution: For the first treatment, SNi T 3 T 2 D (dep)C (dop)B (pres)A (temp)Experiment Number19.570.782.387.3121.563.274.8219.145.754.956.5317.662.378.279.8414.876.577.3529.383.289622.355.764.8724.093.299820.47475.79
32Worked out ExampleShown below is the response table. calculating an average SN value for each factor. A sample calculation is shown for Factor B (pressure):SNiD (dep)C (dop)B (pres)A (temp)Experiment Number19.5121.5219.1317.6414.8529.3622.3724.0820.49
33Worked out Example Level A (temp) B (pres) C (dop) D (dep) 1 20 19.8 24.318.2220.620.124.4322.222.918.7126.96.36.199.56.1Rank4The effect of this factor is then calculated by determining the range:Deposition rate has the largest effect on the processor yieldand the temperature has the smallest effect on the processor yield.
40Mixed level designsExample: A reactor's behavior is dependent upon impeller model, mixer speed, the control algorithm employed, and the cooling water valve type. The possible values for each are as follows:Impeller model: A, B, or CMixer speed: 300, 350, or 400 RPMControl algorithm: PID, PI, or PValve type: butterfly or globeThere are 4 parameters, and each one has 3 levels with the exception of valve type.