Tool Wear Estimation Research Articles

Modelling of tool wear based on cutting forces in turning

Tool wear estimation is essential for on-line process control and optimization. Wear and breakage of the tool are usually monitored by measuring force, load current, vibration, acoustic emission and temperature. These measurements are important for reliability and for the implementation of an adaptive control system. This paper proposes a development of mathematical models to describe the wear-time and wear-force relationships for turning operation. Cutting force components have been found to correlate well with progressive wear and tool failure. The results show that the ratio between force components is a better indicator of the wear process, compared with the estimate obtained using absolute values of the forces. It also eliminates variation in material properties, which was identified as a major noise source in signals measured during machining. This paper presents data on techniques based on force measurement for tool wear monitoring.

An empirical multi-sensor estimation of tool wear

Automation of metal cutting machinery requires continuous estimation of tool wear. Variations in the type of machining process, materials or tools make a reliable estimation of the tool state by a single sensor signal difficult. A multi-sensor system has been implemented for cutting process monitoring in a lathe.Once tool life intervals are selected, a study of optimal descriptors capable of characterising sensor signals is carried out. Data dispersion inherent to a noisy signal suggests strict quantifier selection over a wide initial set. Pattern recognition procedures such as distance functions, neural networks and information entropy-based procedures offer empirical methods which deal with non-homogeneous data with length flexibility capabilities.An experimental example shows multiple parameter tool wear estimation in a multisensor environment. Good estimation of wear is obtained through the sensor system implanted in the machine.

Modelling of multivariate time series for tool wear estimation in finish-turning

In finish-turning, the traditional flank or crater wear estimation alone is no longer adequate, whilst the wear at the minor cutting edge is of primary concern since it directly affects the surface quality and dimensional accuracy of a finished product. This paper describes the development of an overall tool wear monitoring strategy in finish-turning. To estimate various types of wear developing at the different faces of a tool insert, multidimensional force and vibration signals are used, and a stochastic technique, based on multivariate autoregressive moving average vector models (ARMAV), is used to quantify the process dynamics embedded in the signals. Based on that, a dispersion analysis is carried out to discriminate among signal ingredients each of which is sensitive to a particular type of wear. Experimental results show that the groove wear at the minor cutting edge, once formed, dominates the finishing-tool life, while in the case of no groove formation at the minor cutting edge, the minor flank wear reaches its critical point earlier than the major flank or crater wear does. The results also show that the method derived from the dispersion analysis is a feasible approach to on-line tool wear estimation in finish-turning.

Multi-sensor integration—An automatic feature selection and state identification methodology for tool wear estimation

Artificial systems obtain knowledge from the real world through sensors. The nature of the transducer, the location in space and the acquisition time must be considered to define the perceived state of the environment. Example base techniques are able to learn from the system experience. They are specially adequate for a complex multi-sensor system which lacks a formal knowledge background. Data logging from diverse sensors, signal feature extraction, identification methods and evaluation strategies for the results have been integrated in a unique programming environment. Some critical aspects of empirical methods, such as automatic feature generation and selection or compaison of pattern recognition procedures, are approached from a global point of view. Some results obtained monitoring tool machines for the estimation of tool wear validate the presented work.

Monitoring machining processes using multi-sensor readings fused by artificial neural network

This paper proposes a tool wear model based on Artificial Neural Network which is used as Pattern Associator. As such, the network is trained to associate an input vector, consists of readings of several different sensors, with an output vector, consists of actual tool wear measurements, by a training process during which both vectors are presented to the network. During operation, the network is fed with the sensors' readings, it fuses this data and provides an estimate value for the wear according to the “model” established during the training. The estimation of tool wear obtained by this method is by far more accurate than other methods which use the same sensory inputs. In addition, a new process monitoring scheme by which Artificial Neural Network is used for: 1) Extract knowledge from an experienced operator; 2) Fuse sensory input; and 3) Accordingly monitor the process by varying process parameters, is presented.

Statistical analysis evaluation of a multisensor system

Abstract In many manufacturing processes accurate and reliable sensors are needed in order to measure and control some important variables. However, not all the information obtained by these sensors may be necessary to describe the process, and some of it, may be redundant. The interpretation of sensor data can be performed by several statistical techniques. This paper presents, as an alternative, a statistical evaluation of a multisensor system used in a turning process to find the most important variables at critical stages. Some parameters are monitored and used to estimate tool wear.

Comprehensive Tool Wear Estimation in Finish-Machining via Multivariate Time-Series Analysis of 3-D Cutting Forces

Summary In finish-machining, geometric accuracy and surface quality are adversely affected by the tool wear at the minor flank and nose area. This paper describes an investigation into “comprehensive” tool wear estimation, including flank-, crater-. Minor flank-, and nose-wear, based on an analysis of dynamic cutting force in oblique machining. The force, measured in terms of its three orthogonal components, was used to develop trivariate Autoregressive Moving Average Vector (ARMAV) time series models. Based on these, dispersion analysis (DA) was used to extract features sensitive to the rate of various types of wear. The results show that minor flank wear reaches a critical value first in finish-machining, so that optimum cutting conditions or an appropriate tool change strategy must be determined on the basis of minor flank wear. The results also show that the method is a feasible means for on-line tool wear monitoring in finish-machining.

Applications of adaptive control to machine tool process control

Recent research in the area of adaptive machine tool process control is described. The process control problem in metal-cutting operations is considered, and the problems of online tool wear estimation and adaptive process control are discussed. Research in these areas is then summarized, and some results from the research of the authors, including recent laboratory experiments, are presented. Two adaptive control systems, one for turning and the other for milling processes, are described. >

An adaptive observer for on-line tool wear estimation in turning, Part II: Results

The basic concept and design of an adaptive observer for tool wear estimation in turning, based on force measurement, has been presented in the previous paper (Part I). This paper shows that numerical problems in the estimation of the states of tool wear precludes the use of this method in multi-wear cases where both flank wear and crater wear are present. The method can be applied, however, when one type of wear (either flank wear or crater wear) dominates. The method is applied in turning experiments to a case where flank wear is dominant, and to a second case where crater wear dominates. For the first case the flank wear estimates show excellent agreement with actual wear measurements. For the second case the crater wear estimates are satisfactory, but not as good as in the first case.

An adaptive observer for on-line tool wear estimation in turning, Part I: Theory

On-line sensing of tool wear has been a long-standing goal of the manufacturing engineering community. In the absence of any reliable on-line tool wear sensors, a new model-based approach for tool wear estimation has been proposed. This approach is an adaptive observer, based on force measurement, which uses both parameter and state estimation techniques. The design of the adaptive observer is based upon a dynamic state model of tool wear in turning. This paper (Part I) presents the model, and explains its use as the basis for the adaptive observer design. This model uses flank wear and crater wear as state variables, feed as the input, and the cutting force as the output. The suitability of the model as the basis for adaptive observation is also verified. The implementation of the adaptive observer requires the design of a state observer and a parameter estimator. To obtain the model parameters for tuning the adaptive observer procedures for linearisation of the non-linear model are specified. The implementation of the adaptive observer in turning and experimental results are presented in a companion paper (Part II).

An investigation of methods for the on-line estimation of tool wear

The paper examines the use of the measurements of spindle motor power for the estimation of wear and the detection of the end of effective tool life for a vertical milling machine. The measurements are analysed using spectral analysis and the effectiveness of using moving averages, running-means and cumulative sum of the power spectrum values to estimate wear is evaluated. For a specific machine tool (Beaver NC5), workpiece material (stainless steel) and under a particular set of cutting conditions it is shown that (a) there is a strong correlation between the cumulative sum of the power spectral energies and the total flank wear, and (b) the residuals obtained from computing a least squares linear fit to the plot of cumulative sums of the power spectral energies against cutting time clearly indicates when the tool is entering the region of high wear.

Current estimation of tool wear in unmanned machines

Current estimation of tool wear in unmanned machines

The practical estimation of tool wear in turning

The effects of various factors on the three main types of tool wear were investigated experimentally. A fourth type of tool wear, nose wear, is included in flank wear and the experimental results show that it is larger than crater wear. Several important results on the effects of the factors investigated are obtained.