Hybrid model approaches for compensating environmental influences in machine tools using integrated sensors

Abstract

Abstract Uncontrolled environmental conditions often impact manufacturing processes and lead to product quality fluctuations. For machine tools, thermal influences are a major limitation to the volumetric performance. Climate controls for the shop floor, and machines, or thermally stable structural designs are economically not feasible, promoting control-based compensation as a possible solution. Since the relationship between disturbing quantities and effects are complex and specific to each machine, appropriate modelling is a critical requirement. The authors describe an approach for developing hybrid models, superposing white-box model knowledge, and machine learning. The overall effort can be optimized by combining and balancing different modelling methods, like designing the physical model part and training intelligent algorithms. A general model structure allows a continuous integration of different white-box and black-box model components. The authors integrate self-developed smart sensors into a demonstrator machine tool to test and validate the performance of the approach.