Cutting force control of milling machine (original) (raw)

Robust Machining Force Control With Process Compensation

Journal of Manufacturing science and …, 2003

Force control is an effective means of improving the quality and productivity of machining operations. Metal cutting force models are difficult to accurately generate and, thus, there is large uncertainty in the model parameters. This has lead to investigations into robust force control techniques; however, the approaches reported in the literature include known process changes (e.g., a change in the depth-of-cut) in the model parameters variations. These changes create substantial variations in the model parameters; thus, only loose performance bounds may be achieved. A novel robust force controller is presented in this paper that explicitly compensates for known process effects and accounts for the force-feed nonlinearity inherent in metal cutting operations. The controller is verified via simulation and experimental studies and the results demonstrate that the proposed controller is able to maintain tighter performance bounds than robust controllers that include known process changes in the model parameter variations.

Fuzzy modelling of machine-tool cutting process

Third International Conference on Industrial Fuzzy Control and Intelligent Systems

Fuzzy control of machining process is undoubtedly a very promissory approach, taking on account machinetool complexity and the need of increasing its efficiency. Creation of the knowledge basis for this fuzzy controller requires something more than operators experience: an objective support. Such objective support is to be obtained from experiments, during which the machinetool actually performs the cutting process and corresponding input and output data must begathered. A rather efficient approach, fuzzy chstering, was chosen for elaborating this data in order to obtain the wanted knowledge basis for the fuzzy model. The paper describes algorithms, experiments and fuzzy models of the cutting process in a milling machine. Models so obtained are to be the basis for designing model-based fuzzy supervisory controllers for this machine.

Model-Based Machining Force Control

Ann Arbor, 2000

Regulating machining forces provides significant economic benefits by increasing operation productivity and improving part quality. Machining force regulation is a challenging problem since the force process varies significantly under normal operating conditions. Since fixed-gain controllers cannot guarantee system performance and stability as the force process varies, a substantial research effort has been invested in the development of adaptive force controllers. However, adaptive controllers can be difficult to develop, analyze, implement, and maintain due to their inherent complexity. Consequently, adaptive machining force controllers have found little application in industry. In this paper, a model-based machining force control approach, which incorporates detailed force process models, is introduced. The proposed design has a simple structure and explicitly accounts for the changes in the force process to maintain system performance and stability. Two model-based machining force controllers are implemented in face milling operations. The stability robustness of the closed-loop system with respect to model parameter uncertainties is analyzed, and the analysis is verified via simulation and experimental studies.

Control scheme for automatically controlling the milling system in between optimal working points

2013

This paper presents a novel control architecture system which is composed of a multi-objective cost function which Pareto optimizes the programming of cutting parameters while controlling the milling process to new cutting conditions if new constraints appear. The paper combines a self-optimized module which looks for and finds Pareto optimal cutting parameters according to multi-objective purposes and, a multi-model control module which keeps the cutting forces under prescribed upper safety limits independently of programmed cutting conditions and material properties while maintaining the performance of the process. An intelligent algorithm acts as decision supportsoftware to automatically switch to the best performance tracking controller among those available at each required time.

A comparison of model-based machining force control approaches

International Journal of Machine Tools …, 2004

Machining force regulation provides significant benefits in productivity and part quality. Adaptive techniques have typically been utilized due to the tremendous parameter variations that are found in machining processes. While adaptive controllers provide greater stability as compared to fixed-gain controllers, they have found very little headway in industry due to complexity in design, implementation, and maintenance. Recently, model-based techniques, with and without process compensation (i.e., the ability to directly adjust controller gains given known changes in process parameters), have been explored. This paper provides a comparison of four model-based machining force controllers; namely, linearization, log transform, nonlinear, and robust. These controllers are compared to an adaptive machining force controller in terms of transient performance and stability robustness with respect to parameter variations, and in terms of stability robustness with respect to unmodeled dynamics via simulation and experimental studies. The developed stability analyzes for the model-based controllers provide excellent predictions of the stability boundaries in the parameter space. Thus, stability robustness in terms of both model parameter variation and controller parameter adjustments can be systematically explored. Also, the results demonstrate that the stability robustness of the model-based controllers is insensitive to unmodeled servomechanism dynamics. While each force control approach performed satisfactorily in a laboratory environment, it can be generally concluded that their implementation should be dictated by the economics of the production environment.

Operational Method for Identification of Specific Cutting Force During Milling

MM Science Journal

Specific cutting force is a key parameter that is important for estimating cutting forces that occur during machining. This information is important for various applications. The most important application is estimation of the stability limit valid for the specific configuration of the machine tool, tool and workpiece. There are a number of procedures used to predict the specific cutting force through various preliminary tests. This paper focuses on an operational method during milling that allows estimation of the specific cutting force using direct information from the machine tool control system. The specific cutting force is calculated as the ratio between the material removal rate and the power measured on the spindle. The method enables easy in-process identification of the specific cutting force that is valid for the specific workpiece material and the specific cutting edge geometry. The method is demonstrated on practical examples.

Direct adaptive cutting force control of milling processes

Automatica, 1990

Adaptive control of peak cutting force of a milling system with saturation nonlinearities is addressed. It is shown theoretically and demonstrated through experimental results that, while most adaptive control methodologies in the literature may not be applicable for such a system due to the presence of the nonlinearities, a modified version of an existing method continues to accomplish the control task.

Dynamic modeling for control of the milling process

J. Eng. Ind.(Trans. ASME), 1988

In the interest of maximizing the metal removal rate and preventing tool breakage in the milling process, it has been proposed that fixed gain feedback controllers, which manipulate the feed rate to maintain a constant cutting force, be implemented. These process controllers have resulted in substantial improvements in the metal removal rate; however, they may have very poor performance when the process parameters deviate from the design conditions. To address these performance problems, an empirical second order model of the force response for a milling system to feedrate changes is presented along with experimental results which show that the parameters of this model vary significantly with cutting conditions. These variations are shown to have significant effects on the performance of fixed-gain proportional plus integral action and linear model following controllers. This is demonstrated using machining tests as well as through digital simulations.

Cutting process stability evaluation by process parameters monitoring

2009

It is already accepted by most of the researchers working in this domain, that cutting process dynamics is nonlinear and, more than that, there are proves to sustain the appearance, under certain conditions, of chaos. There are also suggestions of non-linear and chaotic models to characterize the dynamic behaviour of the cutting process. This paper aims to be a first step made in order to conceive a system to control the machining process stability, based on a chaotic approach; this purpose requires, first of all, the existence of a reliable method to evaluate the position of the manufacturing system operating point relative to its stability limit, which can be done by monitoring cutting process characteristic parameters.