## 2 State-of-the-Art Methodology
### P-Diagram of T-Peel Test
From the design of apparatus condition, the control factors are parallel spring thickness, module of spur gears, and drum diameter. The factor's level is decided based on the experiments' objective. The levels must not be so close to each other that the effect on the response is not observable or undetected. Levels must also not be very far apart that there is a region of unknown process behavior. Previous process knowledge is useful to determine the level. For example, three levels are chosen to observe the curvature effect on the response. Based on Figure 3, control factors B, C, D, E, F, and G are analyzed with three levels. Two levels are chosen to determine whether the factor has an effect on the response. Control factor A tensile weight is analyzed with two levels 4 g and 8 g. More than three levels are suitable to observe significant trend or behavior, such as sudden rise or drop at certain levels. The experimental design space is large, and it needs a strategy to explore. After determining the control factors and factor's level, they are assigned into an orthogonal array. An orthogonal array is used for optimization to maximize the signal-to-noise ratio [1,8,9]. Balance set of experimentation runs is provided by orthogonal array. Design of experiments using orthogonal array L\({}_{18}\) is utilized with one two-level factor (tensile weight) and six three-level factors (peel angle, peel speed, data region, spring thickness, module of spur gear, and drum diameter) as shown in Table 1.  
The optimization approach starts with the research motivation to establish a procedure on T-peel test optimization followed by a determination of optimum conditions. An experimental confirmation test was performed to validate the estimated condition. The experience from L\({}_{18}\) in selecting control and noise level is presented and needs to be carefully done. Ideally, a system with zero or minimum noise is desired. After optimization, the gap between noise level N1 and N2 must be as small as possible to produce an ideal function, shown in Figure 8.  
Figure 6: Deviation in peel angle during T-peel. Figure 4:0 N1 and N2 plot for noise factor. The robust design engineering methodology is developed and briefly described as follows:  
_Step 1:_ Enable functionality of the system. Carefully analyze the ideal function that transforms the energy into quality characteristic. Construct P-diagram to get a whole picture of the system. _Step 2:_ Identify the problem by selecting the response based on experiment's objective. The response may be maximized, minimized, or taken to a target value. The mean and variance of a response can be studied simultaneously. Construct an ideal function and P-diagram. Determine the input (signal factor) and output (response) of the experiment. _Step 3:_ Select noise factor and level for outer array. Relate with response objective, for example, if the objective is to minimize variation of peel strength, make sure the noise factor can produce the variation in peel strength and the design space is covered as best as it can. _Step 4:_ Select control factor and level for inner array. Consideration of factor level must in line with objective or intended effect on the response such as curvature, effect presence, and other behavior or trend. _Step 5:_ Construct an orthogonal array based on number of factors and levels. Implement the experiment. SNR and sensitivity response plot are analyzed. _Step 6:_ Check on reproducibility. Estimation and confirmation dB gain is compared. Rule of thumb: less than 3 dB gain difference is good. _Step 7:_ If the intention is to move the mean to target, perform adjustment step. If there is no intention to move the mean to certain target, steps 1 to 6 are sufficient enough. Identifying the experiment's objective is crucial because it can affect the selection of noise and control factors. General guidelines are described step-by-step from selecting the response up to decision making on the optimum dB gain.