Showing papers on "Tool wear published in 1994"

Modelling Machining Dynamics Including Damping in the Tool-Workpiece Interface

Abstract: Machining instability, namely chatter, occurs due to the interaction between the structural dynamics and the cutting process. The process damping generated at the tool-workpiece interface is an important parameter of that interaction. A significant enhancement to the chatter simulation model in milling is presented. It includes tracking of the interference between the tool flank and the generated wavy surface, which is the source of process damping. Results of simulation runs performed to determine the limits of stability are presented for sharp tools as well as for tools with various amount of flank wear. The phase relationship between the ploughing force and tool vibrations is explained using these simulations. It is also shown that the improved model accurately predicts the increase in the limit of stability due to tool wear, as well as the effect of the wave length of the machined surface undulations on process damping. Cutting tests of aluminum confirmed the simulation results.

A back-propagation algorithm applied to tool wear monitoring

Abstract: In this paper, a distributed neural network has been applied to a pattern recognition problem for classification of tool wear in a turning operation to discriminate between a worn-out tool and a fresh tool. A multilayered perceptron with back-propagation algorithm has been used. The network was trained offline using 30 patterns each of six inputs. Using the weights obtained during training, fresh patterns were tested. Results for six fresh patterns are presented.

On the closed form mechanistic modeling of milling: Specific cutting energy, torque, and power

Abstract: Specific energy in metal cutting, defined as the energy expended in removing a unit volume of workpiece material, is formulated and determined using a previously developed closed form mechanistic force model for milling operations. Cutting power is computed from the cutting torque, cutting force, kinematics of the cutter, and the volumetric material removal rate. Closed form expressions for specific cutting energy were formulated and found to be functions of the process parameters: pressure and friction for both rake and flank surfaces and chip flow angle at the rake face of the tool. Friction is found to play a very important role in cutting torque and power. Experiments were carried out to determine the effects of feedrate, cutting speed, workpiece material, and flank wear land width on specific cutting energy. It was found that the specific cutting energy increases with a decrease in the chip thickness and with an increase in flank wear land.

Tool life management system

Abstract: A tool life management system for managing a tool life. The system includes a load torque measuring device for measuring an initial load torque of a spindle attached to a tool during an initial machining operation and for measuring an actual load torque of the spindle attached to the tool during a subsequent actual machining operation, a torque calculating device for calculating a tool wear detection torque based on a reference load torque, and for calculating a tool breakage detection torque based on a tool diameter, the reference load torque being the initial load torque of the spindle, a memory for storing the reference load torque, the tool wear detection torque and the tool breakage detection torque, a torque comparing device for comparing the actual load torque of the spindle with each of the tool wear detection torque and the tool breakage detection torque, and for outputting a result of the comparison, and a tool control device for controlling the tool in accordance with the result of the comparison.

Tool Wear Monitoring in Diamond Turning by Fuzzy Pattern Recognition

Abstract: This paper introduces a fuzzy pattern recognition technique for monitoring single crystal diamond tool wear in the ultraprecision machining process. Selected features by which to partition the cluster of patterns were obtained by time series AR modeling of dynamic cutting force signals. The wear on a diamond tool edge appears to be classifiable into two types, micro-chipping and gradual, both very small compared to conventional tool wear. In this regard, we used a fuzzy technique in pattern recognition, which considers the ambiguity in classification as well as the weakness of the cutting force variation, to monitor the diamond tool wear status, with satisfactory results.

Chip formation analysis in high speed machining of a nickel base superalloy with silicon carbide whisker-reinforced alumina

Abstract: High speed turning tests were performed on a heat resistant alloy (Inconel 718), using SiC (20%) whiskers reinforced ceramic tools. The main aims of these tests were the following: (1) mapping cutting speed-feed and machined volume in order to find a region free from tool breakage; (2) analysing tool wear and chip formation mechanisms; and (3) from the experimental results modelling analytically both chip formation processes and tool wear mechanism. Tool and chip were observed at the SEM and EDAX semiquantitative analyses were carried out to evaluate micro-welds on the chip and areas of welded or scattered material over the tool. Micro-hardness mapping was carried out on the longitudinal cross-section of the chips to monitor its dependence by process parameters. Variable wear mechanisms along the tool-chip contact length that were attributed to variations in plastic deformation energy were observed. There variations were analytically modelled in orthogonal cutting conditions. Longitudinal and tranverse shear planes into the chip were also observed. The causes and the mechanisms of wear, chip formation and the hardening of work material were deduced. The presence of whiskers pull out mechanisms due to temperature effects in the tool-chip interface were also observed.

Acoustic emission from single point machining: Source mechanisms and signal changes with tool wear

Abstract: Acoustic emission (AE) was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Heat treatments that increase the strength of 4340 steel substantially increase the amount of AE produced during deformation, while heat treatments that increase the strength of Ti-6Al-4V dramatically decrease the amount of AE produced during deformation. There was little change in root-mean-square (rms) AE level during machining for either alloy as a function of prior heat treatment, demonstrating that chip deformation is not a major source of AE in single point machining. Additional data from a variety of materials suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of AE. Changes in AE signal characteristics with tool wear were also monitored during single point machining. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristics with wear for a given material may be sufficient to be used to monitor tool wear.

Aspects of Wear Prediction in Precision Forging

Abstract: Precision forging is a rather new special hot forming technology with a high economic potential. The lifetime of a forging tool is one of the most decisive factors during the economical evaluation of a forging and is therefore the subject of intensive investigation and research work. This paper shows results obtained in experimental work and theoretical analysis for the determination of tool wear. Observations have shown that the temperature of the raw material is of major importance for tool wear because of damaging effects on crystalline structure and in consequence mechanical properties of boundary layers of the forging tool. The example of temperature calculation which is strongly connected with heat exchange inside the tribological system explains the complex problems of the theoretical analysis. Additionally the heat transfer itself influences the geometry of the finished forged part, which is in the industrial case of major importance. The heat transfer during the different manufacturing stages has...

Feature representations for monitoring of tool wear

Abstract: We address the general problem of reliable, real-time detection of faults in metal-removal processes in manufacturing. As has long been recognized by skilled machine operators, mechanical and acoustic vibrations can be reliable sources of cues for such monitoring. However, conventional dull-tool monitoring systems, which are generally based on stationary signal processing methods, are inadequate for real-time control of drilling procedure. Making use of a database from nine different drill bits, we (a) identify different features which seem to contain tool wear information, (b) document what we found to be superior signal processing tools to identify, extract and process these non-stationary features, and (c) stress the need for a fully annotated public-domain manufacturing signal database. >

Tool Wear Mechanism in Cutting of CFRP.

Abstract: In cutting CFRP (carbon-fiber-reinforced plastic), the major problem is severe tool wear and inferior surface quality. The purpose of this study is to clarify the tool wear mechanism of CFRP cutting. Relationships between the cutting conditions and the tool wear are investigated in the edge cutting test of a CFRP pipe manufactured by the FW (filament winding molding) method. Moreover, the tool wear characteristics were theoretically analyzed in order to explain the difference in those of in cutting CFRP and GFRP. The main results obtained are as follows. (1) In cutting CFRP and GFRP the tool wear characteristics are different under ordinary cutting conditions but the tool wear mechanism is basically the same. (2) In cutting CFRP at less than about 200m/min, the tool wear is hardly influenced by the cutting speed but is influenced by the fiber length of cut. (3) In cutting CFRP, the wear resistance of the tool materials is in the following order, cemented carbide K 10, M 10, P 20, black ceramic, white ceramic and cermet, that is to say, the cemented carbide K 10 has relatively high wear resistance.

Study of Residual Stress Distribution in the Machined Stainless Steel Components

Abstract: The demand for high quality and fully automated production, coupled with advances in alloy development, focuses attention on the surface condition of products, especially the residual stresses on the machined surface because of its effect on component performance, longevity, and reliability. Although stainless steel is an important, material with wide application, it is not easy to obtain favorable surface condition, due to its sensitivity to thermal and mechanical operations. In order to obtain favorable surface conditions in a stainless steel component, it is necessary to have practical data which include information concerning tool wear, surface roughness and surface residual stress. In the research toward developing a machinability chart which can provide suitable cutting parameters for the high production rate and good quality surface, and can be used in computer controlled machining tools, surface residual stress distributions in the turning process for stainless steel were studied. Austenitic 304 s...

High-speed laser cutting of thin metal sheets

Abstract: Increasing productivity in splitting up of metal sheets by means of mechanical cutting processes is today limited by long change-over times as well as nonproductive times and insufficient quality caused by tool wear. In the case of high-quality materials even a slight quality reduction concerning development of dross attachment and induced stress leads to a lot of rejects. In order to increase the cutting speeds within a range economical for industrial use, i.e. about 100 m/min, a completely new type of laser cutting process had to be developed. As opposed to conventional laser cutting, during which a semicylindric cutting front is formed, a closed keyhole with subsequent melt film ejection is produced during the completely new laser cutting process. The incoupling of energy no longer only results from pure surface absorption but in addition from plasma formation and multiple reflection. With the help of the wear resisting tool `laser' the cutting quality is constantly good and can even be significantly improved in comparison with the conventional cutting method with circular knifes. In the case of a sheet thickness of 0.2 mm grain oriented electrical steel can be cut e.g. with a cutting speed of 130 m/min, aluminum with 270 m/min, copper with 95 m/min and zinc with 280 m/min; the necessary laser power is 1300 W. Based on the results of basic research the prototype of a laser slitting line was constructed and went into operation in autumn 1991. Up to now various materials for different customers have been cut on this slitting line and used in industry. Especially when cutting grain oriented electrical steel, which is a material with very high requirements on the cutting process, it becomes evident that the laser cutting process compared with the conventional technique has considerable advantages concerning cutting quality and quality assurance.

Comparing a Neural-Fuzzy Scheme with a Probabilistic Neural Network for Applications to Monitoring and Diagnostics in Manufacturing Systems

Abstract: Introduction The success of unattended manufacturing depends largely on control mechanisms that monitor the machining state and take actions to rectify unsatisfactory performance. Direct sensing methods like quality inspection lack on-line capability, whereas indirect methods using sensors can be thwarted by noise and changes in operating conditions. While knowledge about these changes exists, it does not generally correspond with an available sensor. Two different techniques are applied to the problem of integrating data from multiple sensors in the manufacturing environment: one featuring the integration of fuzzy logic and neural networks, and one using a probabilistic neural network. These techniques are applied to monitor and diagnose tool wear in unattended milling machines an application with implications toward extension to other manufacturing machines. Data from spindle motor current, acoustic emission, and vibration gathered in experiments on a Matsuura machining center are used as input to the two systems. In the case of the fuzzy-neural system, clusters for tool wear are established using the dendrogram ethod, then membership functions for these clusters are learned by a neural network. These clusters can be interpreted as fuzzy rules which are then applied to tool wear diagnosis using other principles of fuzzy logic. For the probabilistic neural network system, a network with fixed size is used for clustering of data and estimating the probability density function using a self-organizing probabilistic neural network (SOPNN). Both systems show promising results with regard to tool wear. The advantage of the fuzzy neural-fuzzy system is that its classification seems to exhibit high reliability due to its redundant structure and efficiency of the preclustering. The advantage of the probabilistic network, on the other hand, is that it allows the use of rigorous probabilistic analysis, supports Bayesian network models and provides a means for the continuous updating of the density functions. The neural-probabilistic system has been tested successfully on data from an industrial power generation plant for application to sensor validation. The need of manufacturers to produce inexpensive, quality products has resulted in increasing demand for unattended and/or automated manufacturing systems. One problem in automating machining is how to deal with common malfunctions and disturbances such as tool wear, chatter, and tool breakage. Tool wear is a process which is very difficult to deal with for a variety of reasons. It is not a linear process: a tool wears fast initially, then at a moderate rate for a longer period of time, and finally at an accelerated rate until total failure. To complicate things, tool life is not constant under the seemingly same operating conditions. Many factors affect the operating life up to the wear limit: slight variations in the material of the workpiece, the degree of inclusions in the workpiece and slight temperature changes are but a few. To avoid costly damage due to tool wear or breakage, manufacturers use conservative operating procedures to prevent these malfunctions (Rangwala and Dornfeld, 1989). However, these result in less efficient and more costly production because of premature tool replacement and excessive machine downtime. To increase operating efficiency, manufacturers can consider the use of sensors to diagnose tool status and control the system on-line. Since each sensor alone cannot reliably render the state of a tool in changing cutting conditions, integrating the information of various sensors becomes the major challenge. By using partly redundant information this sensor fusion can provide data for decision making about the process that will yield accurate diagnostic predictions and early warning of incipient failures. Early research focused on extracting relevant features from sensor data and inferring the tool status; others (Agogino, 1988, 1990) proposed the use of expert systems and sensor fusion using probabilistic influence diagrams. However, these approaches suffer from a high sensitivity to changing cutting conditions and varying sensor integrity and precision. This paper summarizes two approaches to the problems outlined above: (1) a hybrid fuzzy-neural system and (2) system using a probabilistic neural network which can be 45 From: AAAI Technical Report SS-94-04. Compilation copyright © 1994, AAAI (www.aaai.org). All rights reserved.

Tool Wear Monitoring at Turning and Drilling

Abstract: To improve economy and as a contribution in quality assurance in the domain of machining metallic materials a method for automatic tool wear monitoring at turning and drilling has been developed based on the analysis of the acoustic emission (AE) generated on the tool at machining procedure (machining noise). Different wear types (tool flank face and tool chipping) result in changes of the different characteristic values of the continuous part of AE. In case of a uniform abrasion of the insert, e.g. flank face or crater wear, an increased mean signal level is observed, whereas for microbreakage at tool edge, an increase of the crest factor with nearly constant mean signal level is found. The burst-like signals from collision between chip and tool and from chip breakage have to be eliminated from analysis to avoid the distorsion of the signal parameters of continuous AE. This method should be well suited especially for monitoring of finishing processes (with small depth of cut).

Tool wear estimation by group method of data handling in turning

Abstract: Tool wear monitoring and estimation are essential for improved productivity of manufacturing systems. Multi-sensory approaches based on force, vibration and Acoustic Emission (AE) signals have been recognized as potential methods for tool wear monitoring. In the present work, steady-state components of force, dynamics of the main cutting force and vibration in the direction of the main cutting force have been used for on-line tool wear estimation in a turning process. The group method of data handling (GMDH), a heuristic self-organizing method of modelling, has been used to integrate information from different sensors and the cutting conditions to obtain estimates of tool wear. Different methods of preprocessing the forces have been attempted to determine the best method to suit the data. Various heuristics of GMDH have been analysed to obtain the appropriate models for tool wear estimation. The results show that GMDH can be effectively used to integrate sensor information and obtain reliable estimates of...