Showing papers on "Tool wear published in 1995"

High pressure waterjet cooling/lubrication to improve machining efficiency in milling

Abstract: The efficiency of the metal cutting operations depends upon the thermal/frictional conditions at the tool-chip interface. The use of high pressure waterjet as a coolant/lubricant to improve the thermal/frictional conditions in milling operations was studied here. The influence of high pressure waterjet delivered into tool-chip interface in two different methods, namely, waterjet injected directly into the tool-chip interface through a hole in the tool rake face, and waterjet injected into tool-chip interface through an external nozzle, was explored in this study. The effectiveness of these developed methods was evaluated in terms of cutting force, surface finish, chip shape and tool wear.

The application of polycrystalline diamond (PCD) tool materials when drilling and reaming aluminium based alloys including MMC

Abstract: Following a review of metal matrix composite (MMC) materials and production methods, the paper outlines the development and application of PCD cutting tools. Experimental data are presented for the drilling and single blade reaming of aluminium-silicon alloys containing 7% and 13% silicon and aluminium 2618 MMC alloy reinforced with 15 vol% silicon carbide (SiC) particulate. Though initially aimed only at aerospace and defence products, MMCs have progressively moved into higher volume applications and are currently under evaluation for volume automotive components. Compared with standard hypoeutectic and hypereutectic cast aluminium-silicon alloys, tool wear when machining MMC is shown to be up to seven times more severe. Tools used for drilling include HSS-M2, diamond plated HSS, WC, TiN coated WC and PCD. Other than when using PCD drills, tool life was extremely short due to the abrasive nature of the (SiC) reinforcement. Similarly, results from PCD reaming tests confirmed that diamond tooling provides the only realistic tooling option.

A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear

Abstract: A customized neural network for sensor fusion of acoustic emission and force in on-line detection of tool wear is developed. Based on two critical concerns regarding practical and reliable tool-wear monitoring systems, the minimal utilization of «unsupervised» sensor data and the avoidance of off-line feature analysis, the neural network is trained by unsupervised Kohonen's Feature Map procedure followed by an Input Feature Scaling algorithm. After levels of tool wear are topologically ordered by Kohonen's Feature Map, input features of AE and force sensor signals are transformed via Input Feature Scaling so that the resulting decision boundaries of the neural network approximate those of error-minimizing Bayes classifier. In a machining experiment, the customized neural network achieved high accuracy rates in the classification of levels of tool wear. Also, the neural network shows several practical and reliable properties for the implementation of the monitoring system in manufacturing industries

Force-based model for tool wear monitoring in face milling

Abstract: One of the most important techniques for automation which should be developed at current stage is on-line tool wear monitoring technique. In this paper, an on-line tool wear monitoring approach for face milling process is proposed. This approach uses the relationship between flank wear and average cutting force coefficients to estimate tool wear. The relationship between flank wear and average cutting force coefficients is established based on data obtained from a series of experiments. It is found that tool wear can be properly estimated by knowing the average cutting force coefficients and cutting parameters when fly cutting aluminum with a single cutter and workpiece geometry.

Robust Tool Wear Estimation With Radial Basis Function Neural Networks

Abstract: In this paper, a unified method for constructing dynamic models for tool wear from prior experiments is proposed. The model approximates flank and crater wear propagation and their effects on cutting force using radial basis function neural networks. Instead of assuming a structure for the wear model and identifying its parameters, only an approximate model is obtained in terms of radial basis functions. The appearance of parameters in a linear fashion motivates a recursive least squares training algorithm. This results in a model which is available as a monitoring tool for on-line application. Using the identified model, a state estimator is designed based on the upperbound covariance matrix. This filter includes the errors in modeling the wear process, and hence reduces filter divergence. Simulations using the neural network for different cutting conditions show good results. Addition of pseudo noise during state estimation is used to reflect inherent process variabilities. Estimation of wear under these conditions is also shown to be accurate. Simulations performed using experimental data similarly show good results. Finally, experimental implementation of the wear monitoring system reveals a reasonable ability of the proposed monitoring scheme to track flank wear.

Wear of Rock Cutting Tools: Laboratory Experiments on the Abrasivity of Rock

Abstract: This text provides an insight into the wear processes which take place during the cutting of rock with steel cutting tools. Rock cutting experiments in different rock types leading to a new approach to the estimation of rock cutting tool wear are described.

Tribological aspects of hard turning with ceramic tools

Abstract: Process parameters in hard turning are completely different from those in conventional turning. The tribological effects which occur in finishing hardened steels, using coolants and in dry cutting, were compared by means of turning tests with ceramic tools. Tool wear, surface integrity and chip formation, as influenced by different coolants in hard turning, are described. Cooling effect reduces the thermal load ot the cutting edge, and thus increases tool life as compared to dry cutting. In addition, surface roughness of the workpiece can be improved either by chemical interaction between workpiece surface and extreme-pressure additives of the coolant or by pure mineral oil application. On the workpiece surface, rehardened layers were found both in dry and wet cutting, a result of rapid self-cooling. However, rehardened zones at the bottom side of the chips appeared only when coolant was supplied because this operation exceeded the critical quench speed in the chip

Single-point scratching of 6061 Al alloy reinforced by different ceramic particles

Abstract: Aluminium alloys reinforced by ceramic particles have been widely used in aerospace and automotive industries for their high stiffness and wear resistance. However, the machining of such materials is difficult and would usually cause excessive tool wear. The effect of ceramic particles on the cutting mechanisms is also unclear. The purpose of this study is to investigate the cutting mechanisms and the relationship between specific energy of scratching and depth of cut (size effect). The single-point scratch test was carried out on 6061 Al and its composites reinforced by Al2O3 and SiC ceramic particles using a pyramid indenter. The results indicated that the scratch process was composed of rubbing, ploughing, plastic cutting and reinforcement fracture. A simple model was proposed to interpret the apparent size effect. The effect of reinforcement on the specific energy was correlated to the ratio of volume fraction to particle radius. The paper found that for machining MMCs, a larger depth of cut should be used to maintain a lower machining energy, especially for those with a larger ratio of volume fraction to particle radius.

On-line cutting state recognition in turning Using a neural network

Abstract: Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate.

Design and implementation of tool wear monitoring with radial basis function neural networks

Abstract: In this paper, a unified method for constructing dynamic models for tool wear from prior experiments is proposed. The model approximates flank and crater wear propagation and their effects on cutting force using radial basis function neural networks. Instead of assuming a structure for the wear model and identifying its parameters, only an approximate model is obtained in terms of radial basis functions. The appearance of parameters in a linear fashion motivates a recursive least squares training algorithm. This results in a model which is available as a monitoring tool for online application. Using the identified model, a state estimator is designed based on the upper bound covariance matrix. This filter includes the errors in modeling the wear process, and hence reduces filter divergence. Simulations using the neural network for different cutting conditions show good results. Finally, experimental implementation of the wear monitoring system reveals a reasonable ability of the proposed monitoring scheme to track flank wear.