Approach To Identify Product And Process State Drivers In Manufacturing Systems Using Supervised Machine Learning

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

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 interrelations 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.

Approach to Identify Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Multiscale Simulation Approach for Battery Production Systems

Addressing the challenge of improving battery quality while reducing high costs and environmental impacts of the production, this book presents a multiscale simulation approach for battery production systems along with a software environment and an application procedure. Battery systems are among the most important technologies of the 21st century since they are enablers for the market success of electric vehicles and stationary energy storage solutions. However, the performance of batteries so far has limited possible applications. Addressing this challenge requires an interdisciplinary understanding of dynamic cause-effect relationships between processes, equipment, materials, and environmental conditions. The approach in this book supports the integrated evaluation of improvement measures and is usable for different planning horizons. It is applied to an exemplary battery cell production and module assembly in order to demonstrate the effectiveness and potential benefits of the simulation.