S. Ros

Fuzzy model and hierarchical fuzzy control integration: an approach for milling process optimization

Abstract Process optimization is a very important subject to several industrial sectors in confronting the growth on markets competition. However, due to the complexity of some processes, their optimization is not an easy task; therefore, to accomplish this objective, intelligent techniques should be used. We are working on end-milling process optimization through combining analytic and fuzzy techniques. This paper describes in a general form a hierarchical structure of fuzzy control and fuzzy model used in end-milling process. These modules represent possible solutions to complex problems: optimization and supervision of milling process. The articles goal is to discuss some results of integrated modules in on-line operation.

Toward intelligent machining: hierarchical fuzzy control for the end milling process

The difficulties in implementing adaptive and other advanced control schemes in industrial machining processes have encouraged researchers to combine the utilization of one hierarchical level, a fuzzy control algorithm, and robust sensing systems. The main idea of this paper deals with self-regulating controllers (SRCs). The control signals scaling factor (output scaling factor) is self-regulated during the control process, and it can assure the optimum gain setting for the hierarchical fuzzy controller. An important role in this strategy is performed by a robust sensing system based on current sensors. For comparison, the CNC-PLCs own control loops, a hierarchical fuzzy controller based on look-up tables, and the hierarchical fuzzy controller with a self-regulating output scaling factor GC are studied. The performances of these controllers are compared. The results indicate that the hierarchical fuzzy controller with a self-regulating output scaling factor yields the best performances among them. The index known as the metal removal rate is increased, and the in-process time is reduced by 50%. Thus, higher production rates are obtained. The hierarchical fuzzy controller is equipped with three basic requirements: flexibility, low cost, and compatibility with any CNC manufacturer.

Fuzzy supervisory control of end milling process

Abstract The necessity of raising the efficiency of the milling process in a vertical (end) milling machine determines the implementation of a supervisory control system, which should manipulate the cutting process variables, in order to keep constant a high level of the cutting force. Modeling and control of this process using conventional techniques are quite difficult due to its complexity and nonlinearity. Therefore, a fuzzy control approach is undertaken. The algorithm used for the resulting MIMO controller has classic structure, partitions, and rule base, with IF…THEN rules. Also, the classic “sup-min” compositional operator and center of gravity for the defuzzification strategy are employed. A precalculated look-up table is the actual kernel of the implemented control algorithm. The system hierarchical structure has two levels: the lower one includes the CNC loops, and the higher one the fuzzy controller of the cutting force, acting over the cutting variables set points, and based on a PC. The necessary high- and low-level software as well as the complementary hardware were created. The results are evaluated as very satisfactory, especially concerning the increase of the metal removal rate and the suitable transient response, also compared with linear controllers.

Dynamic Model of the Machining Process on the Basis of Neural Networks: From Simulation to Real Time Application

Nowadays, the modeling of complex manufacturing tasks is a key issue. In this work, as a case study is selected the application of a dynamic model to predict cutting force in machining processes. A model created using Artificial Neural Networks (ANN), able to predict the process output is introduced in order to deal with the characteristics of such an ill-defined process. This model describes the dynamic response of the output before changes in the process input command (feed rate) and process parameters (depth of cut). Experimental tests are made in a professional machining centre, with different cutting conditions, on real time data. The model provides sufficiently accurate prediction of cutting force, since the process-dependent specific dynamic properties are adequately reflected.

Hierarchical fuzzy control of the milling process with a self-tuning algorithm

This paper presents a hierarchical fuzzy logic controller (HFLC) with a self-tuning (ST) procedure based on pattern recognition of the closed-loop system response. A real-time tuning of the controller scaling factors is performed on the basis of the measured peaks in the error signal. In order to demonstrate the improved performance and effectiveness of this scheme, the ST-HFLC is applied to the end milling process. The response of the controlled process using the proposed controller (ST-HFLC), a standard HFLC and an HFLC with a self-regulating output scaling factor is analyzed. The ST-HFLC provides a better transient performance than the others. Without significant variation in rise time, a non-oscillating system with a short settling time can be achieved. These positive features, in the case of our particular application, may represent an increase of cutting tool life as well as the avoidance of chatter marks due to inappropriate cutting conditions.

Modeling and simulation of high-speed machining processes based on matlab/simulink

This paper shows the mathematical development to derive the integral-differential equations and the algorithms implemented in MATLAB to predict the cutting force in real time in high speed machining processes. This paper presents a cutting-force-based model able to describe a high-speed machining process. The model considers the cutting force as an essential output variable in the physical processes taking place in high-speed machining. For the sake of simplicity, only one type of end mill shapes is considered (i.e., cylindrical mill) for real-time implementation of the developed algorithms. The developed model is validated in slot-milling operations. The results corroborate the importance of the cutting-force variable for predicting tool wear in high-speed machining operations.

A neural network-based model for the prediction of cutting force in milling process. A progress study on a real case

In spite of recent developments focusing on milling process optimization through an effective cutting force control, there is a need for the analysis of the transient response of these systems because undesirable oscillations in cutting force can be harmful to the quality of the finishing surface and tools. The main goal of this work is to develop a versatile neural network model which can online predict the mean cutting force under commonly encountered conditions. Using this model, easily obtained from a straightforward machining test, developments of complex adaptive controllers and monitoring systems can be carried out. As a result, a good model for predicting the cutting process was obtained.

Fuzzy Control of Spindle Torque in High-Speed Milling Processes

Torque control through feed rate and spindle speed manipulation can produce significant economic benefits for machining processes by reducing the cycle time. This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC’s own resources without requiring additional hardware overhead. The control system consists of a two inputs (i.e., torque error and change of error) -two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, which are 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 the two inputs-two outputs for the fuzzy controller and a single-output fuzzy controller (i.e., only feed rate modification). The results demonstrate that the proposed control strategy provides better accuracy, and machining cycle time than the others, thus increasing the metal removal rate.Copyright

EXTENDED CIRCLE CRITERION: STABLE FUZZY CONTROL OF A MILLING PROCESS.

Abstract In this work, the Extended Circle Criterion and fuzzy approaches are combined to develop a practical technique for checking stability of Fuzzy Control Systems (FCS). The controller is designed on the basis of skilled operator criteria and engineering knowledge about the process. Afterwards, stability is verified by means of the so-called Extended Circle Criterion, under the assumption that the static characteristic of the controller is sector-bounded (nonlinear portion) and the process (linear portion) can be rendered Strictly Positive Real (SPR). The method is tested in a FCS applied to the milling process. Theoretical and experimental results show that the closed loop system is stable.