## 4 Discussions
### Noise System (N)  
Noise factors cannot be controlled during normal production or use. Thus, noise factor is likely to produce variability in response. In other words, noise is a variable that affects product functions. Three types of noise are outer noise caused by environmental conditions, inner noise caused by deterioration of elements or materials in the product, and between-product noise caused by piece-to-piece variation between products [5; 6]. A robust design experiment searches for values of the control factors that can be controlled during production and it is important to make the product or process insensitive to changes in noise factors.For a robust design experiment, start by selecting the response, and then choose noise factors that are likely to produce variability in response, and finally select the control factors that are likely to affect the variability and the mean of the response. After identifying the experiment objectives, it is usually preferable to select the responses before selecting the noise factor. Therefore, the \"Act\" stage of constructed ideal function and P-diagram in F sub-system is connected to the \"Plan\" stage in N sub-system. After function measurement is determined for the quality characteristic, noise measurement is done. When planning an experimental design, selecting factors that affect the response and their levels of value or setting are very important. If incorrect factors and levels are chosen in the experiment, the results may be incomplete or misleading. A number of factors and levels are chosen based on the objectives of the experiment such as smaller-the-better, bigger-the-better, or nominal-the-best. Engineering knowledge of the process can be used to select noise factors and levels. Historical data, previous experimental results, theoretical knowledge, expert opinions, observational data, and other relevant data can be used in judging what the noise factor should be. The noise strategy is very important because it involves the selection of noise factor. Engineering knowledge is very useful in ensuring the stability of the optimum condition in long term. When the source of variation is identified clearly, this will minimize the risk of having an unstable  
Figure 12: Function (F) sub-system of F-N-C-O. optimum condition. The robustness of the measurement result is heavily dependent on the noise factor. In the long term, reproducibility of measurement must be equal or almost equal to the measurand. Figure 13 describes the important flow for Noise (N) system strategy. In selecting the noise level, the range of factor levels should be selected as the levels are not so close to each other because the effect on the response is not observable or important nearby effects will be undetected. The level also should not be so far apart that there is a region of unknown process behavior between the factor levels. The level also depends on the response being considered. Two-level is chosen when the factor has a linear effect on the response. Three-level of a factor is chosen to study curvature in the response. Normally in two-level factor, it is possible to assess whether there is curvature in one or more factors by adding center points at the center of its range. Four-level factor or more is to study further curvature to locate sudden rise or drop in the response. In other words, extra level is meant to understand some patterns or change behavior. Then, the noise validation study is made in \"Do\" stage to categorize the group of noise parameters. For example, set 1, or usually denoted as N1, gathers the low setting of noise parameters. Set 2, or N2, denotes the high setting of noise parameters. This is the special characterization of the Taguchi method that the noise parameter is separated in outer array. The effect of noise parameter is studied in the Taguchi method and placed in outer array. The Taguchi method focuses on achieving robustness in functional performance.