Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning - Thorsten Wuest Gebunden

The book reports on a novel approach for holistically identifying the relevant state drivers of complex multi-stage manufacturing systems. This approach is able to utilize complex diverse and high-dimensional data sets which often occur in manufacturing applications and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation chemical and semiconductor). The results which are reported in detail in this book confirmed that it is possible to incorporate implicit process intra- and int errelations on both a process and programme level by applying SVM-based feature ranking. In practice this method can be used to identify the most important process parameters and state characteristics the so-called state drivers of a manufacturing system. Given the increasing availability of data and information this selection support can be directly utilized in e.g. quality monitoring and advanced process control. Importantly the method is neither limited to specific products manufacturing processes or systems nor by specific quality concepts.