A simple, fast, network-based experimental procedure for identifying the dynamics of the high-performance drilling (HPD) process is proposed and successfully applied. This identification technique utilizes a single-input (feed rate), single-output (resultant force) system with a dual step input function. The model contains the delays of both the network architecture (a PROFIBUS type network) and the dead time related with the plant dynamic itself. Classical identification techniques are used to obtain first order, second order, and third order models on the basis of the recorded input/output data. The developed models relate the dynamic behavior of resultant force versus commanded feed rate in HPD. Model validation is performed through error-based performance indices and correlation analyses. Experimental verification is performed using two different work piece materials. The models match perfectly with real-time force behavior in drilling operations and are easily integrated with many control strategies. Furthermore, these results demonstrate that the HPD process is somewhat non-linear with a remarkable difference in gain due to work piece material; however, the dynamic behavior does not change significantly.
This paper presents the design and implementation of a two-input/two-output fuzzy logic-based torque control system embedded in an open architecture computer numerical control (CNC) for optimizing the material removal rate in high-speed milling processes. The control system adjusts the feed rate and spindle speed simultaneously as needed to regulate the cutting torque using the CNC’s own resources. The control system consists of a two-input (i.e., torque error and change of error), two-output (i.e., feed rate and spindle speed increment) fuzzy controller, which is embedded within the kernel of a standard open control. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are a two-input/two-output fuzzy controller and a single-output (i.e., feed rate modification only) fuzzy controller. The results demonstrate that the proposed control strategy provides better accuracy and machining cycle time than other strategies, thus increasing the metal removal rate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.