Adaptive process control based on a self-learning mechanism in autonomous manufacturing systems

To survive in the highly competitive global economy, manufacturing systems must be able to adapt to new circumstances. An important prerequisite for adaptation is the ability to learn, a process based on knowledge discovery and growth. The aim of this research is to uncover knowledge by examining a large volume of real-time manufacturing data collected during manufacturing operations and to use the insights gained to support decision-making and adaptive process control. The paper presents the concept of a self-learning autonomous work system. This concept introduces a learning loop into a manufacturing system composed of data acquisition, data mining (DM), and knowledge-building models. Two methods for DM are applied. A descriptive DM method enables discovery of patterns in data that may contribute to a better understanding of the manufacturing processes. A predictive process provides knowledge in the form of rules, which can then be used for enhanced decision-making. To illustrate the utility of the knowledge models, the concept of adaptive process control is introduced and implemented in a high pressure die-casting domain. A case study based on industrial data collected during die-casting operations provides a demonstration of the concept.