One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.
Signal processing using orthogonal cutting force components for tool condition monitoring has established itself in literature. In the application of single axis strain sen sors however a linear combination of cutting force compo nents has to be processed in order to monitor tool wear. This situation may arise when a single axis piezoelectric actuator is simultaneously used as an actuator and a sensor, e.g. its vibration control feedback signal exploited for monitoring purposes. The current paper therefore compares processing of a linear combination of cutting force components to the reference case of processing orthogonal components. Recon struction of the dynamic force acting at the tool tip from signals obtained during measurements using a strain gauge instrumented tool holder in a turning process is described. An application of this dynamic force signal was simulated on a ltermodel of that tool holder that would carry a selfsensing actuator. For comparison of the orthogonal and unidirectional force component tool wear monitoring strategies the same timedelay neural network structure has been applied. Wear sensitive features are determined by wavelet packet analysis to provide information for tool wear estimation. The proba bility of a difference less than 5 percentage points between the ank wear estimation errors of above mentioned two process ing strategies is at least 95 %. This suggests the viability of simultaneous monitoring and control by using a selfsensing actuator.
Besides reducing the restricting effects of tool vibrations on productivity, work-piece surface finish and tool life, it is desirable to handle lack of space for sensors at the tool tip and the cost of control systems in turning processes in an effective way. This work considers these two aspects by exploiting the concept of a self-sensing actuator (SSA) in the simulation of tool vibration control. The tool holder structure, in its passive as well as active state, is modeled as a supported cantilever. A feedback filtered-x least-mean-square (LMS) algorithm is chosen to compute the control action. A known technique, which consists of pre-filtering the inputs to the LMS-algorithm maintains the stability of the control system. The self-sensing path is modeled and illustrated. It consists of the transmission of the tool tip displacement to the SSA where it is sensed by converting it into a voltage signal. A considerable reduction of 93% of the displacement r.m.s. values of the tool tip, was obtained when simulating this control system.
The focus in this work is on linear model identification of a Steckel hot rolling mill process on a point during the acceleration phase of the process. It furthermore focuses on the controller design based on the linear models and on controller implementation on a non-linear simulator of this process. The linear models are identified for different cases simulated with and without gauge meter compensation and controlled tensions as part of the simulator. The system identification is accompanied by a heuristic justification of the data obtained. A diagonal PID/PI controller as well as MIMO H 媳 controllers, of which the designs are based on the linear models, are implemented on the simulator. From the system identification data for the different linear models it could be seen that gauge meter compensation successfully counteracts the adverse effect of mill stretch and eliminates oscillations in exit gauge, which result from tension oscillations. Simulations of the different controllers in closed loop with the non-linear plant simulator show that good control can be achieved by controllers of which the designs are based on linear diagonal models. One of these controllers, a diagonal H 媳 controller, tested on a non-linear simulator which incorporated gauge meter compensation and inner loop tension control, was found to be the most suited for a switching control system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations 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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright 漏 2024 scite LLC. All rights reserved.
Made with 馃挋 for researchers
Part of the Research Solutions Family.