Sensors as an enabler for self-optimizing grinding machines

Abstract

Today, an operator performs experiments to adaptively select grinding process parameters using observations, expert knowledge, and rules of thumb. Self-optimizing grinding machines cannot use operator observations and must therefore extract enough information out of the grinding process. In this study, a holistic sensor set-up as foundation for self-optimizing machines are presented exemplarily for cup wheel grinding machines. In-process detection of grinding burn, based on temperature and gas measurements, is tested and compared. Afterwards, the influence of input variables such as feed rate and cutting speed on grinding cost, grinding burn, and surface roughness are investigated.