Response surface methodology (RSM) is an important tool for process parameter optimization, robust design and other quality improvement efforts. When the relationship between influential factors and output response is very complex, it's hard to find the real response surface by using RSM. In recent years Artificial Neural Networks (ANN) has been used in RSM. But the classical ANN does not work well under the constraints of real applications. An algorithm is proposed in this paper, which is a supplement of the classical ANN methodology. It makes networks closer to the response surface, so that training time is reduced and robustness is strengthened. The procedure of improving ANN by regressions is put forward and the comparisons among the three methods are computed graphically in three examples. Our research shows that the regression-based ANN methodology is a good supplement to the RSM and classical ANN methodology, which can yield satisfied results under condition that the scope of experiments is rigidly restricted.

Process capability analysis is to predict how well the process will hold the tolerance. It is very useful for assisting product developers and process developers in selecting or designing product/process, evaluating and selecting among competing vendors and determining the bottleneck process in terms of process quality. The paper discusses the statistical properties of Cp and Cpk and process capability analysis based on non-normal data and attribute data. A general flowchart for conducting process capability analysis is also given.

保持测量系统的稳定性是质量控制的重要内容之一。本文在分析了累计和控制图和传统休哈特控制图的基础上，指出了传统休哈特控制图的不足，提出将累计和控制图应用于测量系统稳定性的控制。理论研究和案例分析均表明，当测量系统引起的被测质量特征值的均值发生小幅度偏移时，用累计和控制图比休哈特控制图更有效。

During process design or process optimization, it is quite common for experimenters to find optimum operating conditions for several responses simultaneously. The traditional multiresponse surfaces optimization methods do not consider the uncertain relationship among these responses sufficiently. For this reason, the authors propose an optimization method based on evidential reasoning theory by Dempster and Shafer. By maximizing the basic probability assignment function, which indicates the degree of belief that certain operating condition is the solution of this multiresponse surfaces optimization problem, the desirable operating condition can be found.

多变异分析是一种用于分析工序过程质量特征值变异规律的重要方法。本文分析了传统控制方法的不足，利用多变异分析的统计模型，提出了基于多变异分析的工序控制方法，对多变异情形下工序质量控制具有一定的指导意义。