这一项目研究的是客户反馈问题，GeN2系统在韩国的使用中收到大量的，关于异常噪声的客户抱怨，于是项目组从2004年开始对76个工地的113台扶梯及行了调查。从而总结了一批纠正措施（调整稳压电源、调整CSB涨紧度、优化主机软件、电流编成设置等）来减少整体噪音，使之达到48分贝的要求。
Pareto分析显示对钢丝绳稳定器的调整可以作为减少噪音的关键因素。为进一步探讨这一问题，一个关于鲁棒性设计的DOE试验将被进行，具体步骤将在下面被讨论。
“定义”阶段，也就是定义一个过程图，这花去了该项目开始的一年。
通过大量在工地和试验塔的试验和根源分析、鱼骨图、5W分析法、头脑风暴，项目组最终得出了一张P-图和含有4个可控因素，1个干扰因素，3水平，田口式的DOE方案。如下表所示。
3个不同的稳定器构造（现有形状、C型、L型）将被进行如下的：27次测试，4因素，3水平的DOE试验。将使用两项指标对其结果进行评价：稳定器的传送率和噪声水平。传送率通过给钢丝绳和支架24的加速度进行测试。在每次可控因素测试中三水平的3个干扰因素也被设置在内（通过移动位置负荷在轿厢内的位置进行） 该测试的实施是分耗时，安装和校准的加速度计和麦克风需要几周时间。制备符合测试要求得样品需要数周时间，每次测试都需要重新安装和调整位置。
经过一个月的测试，得出了以下图形。可控因素的影响被通过鲁棒性和信噪比惊醒评价，通过主效应图和信噪比可以看出（如下图）。鲁棒性最好的设计参数是无论C型或L型，稳定器在其最小硬度（50%正常硬度）中心矩156mm压强250千克力/厘米）
As an aid to the DOE practitioner, this chapter contains FAQs about DOE testing. These questions were formulated to highlight some of the potentially confounding aspects of DOE testing and were based on discussions with
participants in UTC’s Design Process Certification 201 two week training classes.
How many replicates do I need to have?
Replicates are the number of times a specific row entry in the DOE test matrix is repeated. Section 2.4 deals with this question. There are two estimated parameters (variance in the process and the minimum practical difference) that can be used to determine the statistical requirements for the number of tests. This number, divided by the number of rows in the DOE test matrix is an estimate of the required number of replicates. See Section 2.3 for more details. Obviously for DOEs using MASC tools no replication is needed.
If I don’t know about nonlinear effects and factor interactions, how should I create a test matrix?
When in doubt one should assume both are significant in Modeling DOEs. That is, Taguchi testing or Fractional factorials design at multiple levels should be used if at all feasible. In general Screening and Robustness DOEs do not consider these higher order terms.
How do Gage R&R results influence my test plan?
Gage R&R is a method for establishing the measurement variance prior to conducting the DOE testing. A general rule is that the measurement system variance should be less than 20% of the estimated process variance.
Do I need to randomize the order of my test matrix?
Ideally one should randomize the test matrix runs, this helps to minimize the potential risk of external noise factors (such as operators, test setups, etc.). It also maximizes one’s ability to extract useful data from prematurely terminated DOE testing. However, if the costs in time and resources required to randomize the testing are significant this might not be practical.
I may have to stop in the middle of testing, what’s the best way to construct a test matrix so this isn’t a problem?
As mentioned in the last question, randomizing the test runs is the best hedge against a premature termination of the DOE testing.
What do I do if the verification runs differ from the expected outcome? There are many reasons this might happen. The first thing to check is the integrity of the data, make sure that no errors were made in filling out the data in the test matrix cells. Another common reason is that factor interactions and/or nonlinearities are not properly considered in the DOE testing. A common practice to check nonlinearities (if you did a 2-level DOE initially) is to add a middle point and check for the deviation of that data from the fixed slope.
Interactions can be checked by including some more datapoints in the DOE test matrix. Consult Section 2.6 for more discussion on this topic.
Should I put in the actual values of the factors into the test data or the intended values?
The standard post-processing plots (i.e., Main effects and Interaction plots) will not work properly if the DOE input factor settings are modified. However, the empirical model construction via linear regression analysis will work fine with altered factor settings.
How can I flow requirements to control factors based on the DOE testing? This, afterall, is the main output of the DOE testing process (i.e., DOE step 7, Specs for CPM). A standard method, as documented in Section 2.7, is to use Monte Carlo Simulation to propagate control factor variability into the system response variability. This can be readily done using MS-Excel and the developed empirical process model from Modeling DOEs.
Does a correlation coefficient greater than 0.95 always indicate a good model?