Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach (original) (raw)
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International Journal of Computer Applications in Technology, 2011
In metal cutting, as a result of the cutting motion, the surface of work piece will be influenced by cutting parameters, cutting force, vibrations, etc. The effects of vibrations have been paid little attention. Accurate detection of the tool conditions under given cutting conditions is very important so that worn tools can be identified and replaced in time. Objective of the present work is to predict the effects of displacements due to vibration during face milling and to examine the correlation of surface roughness along with progression of tool wear at different machining combinations so as to develop a base for online tool condition monitoring system. A laser doppler vibrometer and FFT analyser are used for online data acquisition and subsequent processing of signals. The encouraging results of the work pave the way for the development of a real-time and reliable tool-condition-monitoring system.
Tool Condition Monitoring in Metal Cutting
2018
Automatic tool condition monitoring is based on the measurements of physical phenomena which are correlated with the tool wear, and thus can be exploited as the tool wear symptoms. However measured quantities depend not only on the tool wear but also on a variety of other process parameters of random nature, making the relationship between tool wear and measured value very complex which has a statistical rather than strict, predictable nature. Therefore, the development of a robust and reliable tool condition monitoring system requires a combination of different, meaningful signal features, which best describe the tool wear. There are numerous signal features (SFs) that can be extracted from time domain, frequency domain or time-frequency domain signal. As it is really not possible to predict which signal features will be useful in a particular case thus these informative, correlated with tool wear, should be automatically selected. The information extracted from one or several sens...
2011
Condition monitoring systems of machining processes are essential technology for improving productivity and automation. Tool wear monitoring of cutting tools is one of the important applications in this area. In this paper, the effect of collet fixturing quality on the design of condition monitoring systems to detect tool wear is discussed. The paper investigates the difference in the system’s behaviour and the changes in the condition monitoring system when the cutting tool is not rigidly fastened to the collet. A group of sensors, namely acoustic emission, force, strain, vibration and sound, are used to design the condition monitoring system. Automated Sensor and Signal Processing Selection (ASPS) approach 1 is implemented to address the effect of the tool holding device (collet) on the monitoring system and the most sensitive sensors and signal processing method to detect tool wear. The results prove that the change in the fixturing quality could have significant effect on the de...
CONTEMPORARY CHALLENGES IN TOOL CONDITION MONITORING
Journal of Machine Engineering, 2019
Implementation of robust, reliable tool condition monitoring (TCM) systems in one of the preconditions of introducing of Industry 4.0. While there are a huge number of publications on the subject, most of them concern new, sophisticated methods of signal feature extraction and AI based methods of signal feature integration into tool condition information. Some aspects of TCM algorithms, namely signal segmentation, selection of useful signal features, laboratory measured tool wear as reference value of tool condition-are nowadays main obstacles in the broad application of TCM systems in the industry. These aspects are discussed in the paper, and some solutions of the problems are proposed.
An industrial tool wear monitoring system for interrupted turning
Mechanical Systems and Signal Processing, 2004
An effective wear-monitoring system for machine tool inserts could yield significant cost savings for manufacturers. Over the years, various methods have been proposed to achieve tool condition monitoring (TCM), and recently sensor-based approaches for indirectly estimating tool wear have become highly popular. One difficulty with collecting sensory information from machine tools is that the signal-to-noise ratio of useful information about the tool wear is extremely poor. This problem can be overcome by using advanced signal-processing methods and also by fusing the information obtained from numerous sensors into a single modelling or decision-making scheme such as neural networks (NNs). Neural networks are known for their capacity to solve problems effectively in cases where theoretical/analytical models cannot be established. Furthermore, NNs can handle noisy and incomplete data such as that typically obtained from machining operations. Although numerous authors have proposed the NN approach for TCM, various problems still hamper a practical method of applying the technique for industrial use. This paper proposes a technique which should overcome these difficulties. A cost-effective and reliable tool condition monitoring system (TCMS) was developed, utilising the advantages of NNs for a typical industrial machining operation. The operation considered is interrupted turning (facing and boring) of Aluminium alloy components for the automotive industry. The development and implementation of various hardware and software components for the proposed technique are described in this paper. The main advantages of the technique are its accuracy, reliability and cost-effectiveness.
Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application
CIRP Annals - Manufacturing Technology, 1995
The use of sensor systems for tool condition monitoring in machining and grinding is becoming more commonplace to enhance productivity. Many approaches have been proposed to accomplish tool condition monitoring and a number of these are successfully employed in industry. This paper reviews the motivation and basis for the utilization of these systems in industry, the sensors used in such systems including industrial application, new developments in signal and information processing, sensor based process optimization and control and directions for future developments. Main developments noted include the use of multiple sensors in systems for increased reliability, the development of intelligent sensors with improved signal processing and decisionmaking capability and the implementation of sensor systems in open architecture controllers for machine tool control.
Integrated Tool Condition Monitoring Systems and Their Applications: A Comprehensive Review
Procedia Manufacturing, 2020
In conventional metal cutting, different tool wear modes, and their individual deterioration rates play vital roles in overall production performance. For a given tool (i.e., geometry or materials), many shop floors still follow a standard rule by pre-setting a tool life, which is conservative but not realistic. Premature failure of a tool can cause unexpected machine downtime and material losses, while another tool could serve beyond that pre-set life. As a result, optimized tool life and productivity cannot be achieved. Moreover, nowadays, there is an increased demand of process monitoring and optimization on the unmanned and the semi-automated shop floors. Tool condition monitoring (T CM) systems for process improvement and optimization have been in research for several decades. Both offline and online T CM systems are invented and discusse d. A wide range of original publications are reported focusing on different sub-topics, e.g., specific machining process-based T CM methods, measurement or signal acquisition methods, processing methods, and classifiers. With the recent evolution of smart sensors in the era of Industry 4.0, development of online T CM systems received much attention to the researchers. Accordingly, research on some sub-topics also gets motivated into different directions, such as, feasibility of power or current sen sors, machine vision technique, and combination of multi-sensors. Thus, from the industrial viewpoint, the current state of implementation of the proposed T CM systems for (near) real-time process monitoring and control needs to be clear. This paper present s the state-of-the-art of the T CM systems covering three major machining operations, discusses their application feasibility in industry environments, and states some current T CMS implementations. Challenges being faced by the industry are concluded, along with direction and suggestions for future researches.
An Investigation of Tool Condition Monitoring
2012
In any manufacturing industry, machine tools play an important role in the production of parts. The dimensional accuracy and surface finish of the work piece depends mainly on the condition of the machine. The vibration signatures for different arrangement are recorded to determine the dynamic characteristics of the system, which include work piece, tool and lathe components. These vibration signatures are analyzed to determine causes of inaccuracy in the manufacturing process and faulty components. Many condition monitoring techniques are available to monitor the machine tool experimentally. Among these techniques vibration monitoring is the most widely used technique because most of the failures in the machine tool could be due to increased vibration level. Experimental vibration analyses are conducted for a lathe system to detect the possibility of faults and to develop accurate cutting process. Experiments are carried out using the condition monitoring instrument VIBROMETER to m...
Adaptive tool condition monitoring system: A brief review
Materials Today: Proceedings, 2019
The increasing demand for manufacturing and scientific exploration is the process automation that leads to the broad research area in online monitoring during the machining operation. Keeping this in view, online tool monitoring the newer concept has been introduced to monitor the tool wear during the machining process. Additionally, an extensive study has been performed globally regarding adaptive tool monitoring system. With proper selection of monitoring technique, the machine tool damages scrapped parts and downtime can be circumvented. This paper presents a concise outline of tool condition monitoring and decision-making tools in the various machining process.
Some aspects of AE application in tool condition monitoring
Ultrasonics, 2000
Acoustic emission (AE ) is rather a well-known form of non-destructive testing. In the last few years the technology of the AE measurement has been expanded to cover the area of tool condition monitoring. The paper presents some experience of Warsaw University of Technology ( WUT ) in such applications of AE. It provides an interpretation of common AE signal distortions and possible solutions to avoid them. Furthermore, a characteristic study of several different AE and ultrasonic sensors being used in WUT is furnished. Evaluation of the applicability of some basic measures of acoustic emission for tool condition monitoring is also presented in the paper. Finally paper presents a method of the catastrophic tool failure detection in turning, which uses symptoms other than the direct magnitude AE RMS signal. The method is based on the statistical analysis of the distributions of the AE RMS signal.