• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.149-157
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• 2000

Tool wear in the shearing process such as blanking, piercing and trimming is very important, because it has great effects on the dimensional accuracy, working efficiency and economy. Most of tools in the shearing process have the coated layer at surface fur good wear and corrosion resistance. When the surface of tool is teated, the wear Phenomena of coated surface layer and inner layer may be different. This paper describes a computer modelling technique by the finite element method in order to investigate the wear mechanism and to predict the wear profile of Ti-N coated tool in piercing process according to the volume of Production. Wear coefficients of the coated layer and inner layer are obtained through Pin-on-Disk wear test, respectively. To verify the effectiveness of the suggested technique, the technique is applied to wear analysis in piercing recess of piston pin and simulation results are compared with experimental ones.

• Journal of Powder Materials
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• v.15 no.5
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• pp.359-364
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• 2008

To satisfy the demand of higher cutting performance, mechanical properties with tungsten carbide (WC-Co) tool materials were investigated. Hardness and transverse rupture strength with WC grain size, Co content and density were measured. Compared to H, K, and S manufacture maker as tungsten carbide (WC-Co) tool materials were used for high-speed machining of end-milling operation. The three tungsten carbide (WC-Co) tool materials were evaluated by cutting of STD 11 cold-worked die steel (HRC25) under high-speed cutting condition. Also, tool life was obtained from measuring flank wear by CCD wear measuring system. Tool dynamometer was used to measure cutting force. The cutting force and tool wear are discussed along with tool material characteristics. Consequently, the end-mill of K, H manufacture maker showed higher wear-resistance due to its higher hardness, while the S maker endmill tool showed better performance for high metal removal.

• Design & Manufacturing
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• v.16 no.4
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• pp.46-51
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• 2022

The piercing process of creating holes in sheet metals for mechanical fastening generates high shear force. Real-time monitoring technology could predict tool damage and product defects due to this severe condition, but there are few applications for piercing high-strength aluminum. In this study, we analyzed the load signal to predict the punch's wear level during the process with a piezoelectric sensor installed piercing tool. Experiments were conducted on Al6061 T6 with a thickness of 3.0 mm using piercing punches whose edge angle was controlled by reflecting the wear level. The piercing load increases proportionally with the level of tool wear. For example, the maximum piercing load of the wear-shaped punch with the tip angle controlled at 6 degrees increased by 14% compared to the normal-shaped punch under the typical clearance of 6.7% of the aluminum piercing tool. In addition, the tool wear level increased compression during the down-stroke, which is caused by lateral force due to the decrease in the diameter of pierced holes. Our study showed the predictability of the wear level of punches through the recognition of changes in characteristic elements of the load signal during the piercing process.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.94-103
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• 1993

In this work, the imaginary part of the inner modulation transfer function of the cutting dynamics is introduced for tool wear monitoring. Time-series method is utilized to construct the general three dimensional cutting dynamics whose imaginary part of the inner modulation transfer funcition shows the proportionality to tool wear at the natural frequency of the machine tool dynamics. Thus model is reduced to single-input single-output model without altering the proportionality characteristics to tool wear and implemented to the dual computer system in which one computer performs measurement while the other calculates the imaginary part of the inner modulation transfer function of the cutting dynamics by the batch least square method. The values of the imaginary part at the natural requency of the machine tool structure in the cutting direction are compared to the one calculated during machining with a brand new tool to decide the current status of the tool. The experiments shows the relevance of the proposed concept.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.3-8
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• 1996

To enhance the cutting performance of the tool, single or multilayer coating is applied on the substrate of the tool. Coating material reduces cutting force and heat generation in tool-chip contact zone and enhances resistance against abrasive wear. This paper presents that the effect of different coatings on abrasive wear resistance varies with work material and the flank wear rate is different with depth of cut. Crater wear rate is also found to decrease with higher thermal diffusivity of coating material. It is verified that the estimated thermal diffusivity of multilayer coating has consistent effect on the crater wear.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.3
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• pp.15-21
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• 1996

This study is focused on the prediction of appropriate tool life by clarifying the correlation between progressive tool wear and AE(Acoustic Emission) signals, while cutting stainless steel by end mill on the machining center. The results of this study were that RMSAE tends to increase linearly along with the increase of the cutting speed, and it was more sensitive to depth of cut than to the variation of feed rate at the same cutting conditions, and RMSAE increases around 0.21mm flank wear hereby AE-HIT also increases. AE signals depend upon tool wear and fracture from the above results. Therefore, the AE signals can be utilized in order to monitor the tool condition.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.1
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• pp.222-228
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• 2003

A reliable tool wear monitoring technique is the one of important aspects for achieving an integrated and self-adjusting manufacturing system. In this paper, a tool wear estimation approach for turning is proposed. This approach uses the model of cutting force, spindle displacement and their relation. A series of experiments were conducted by designing experimental techniques to determine the relationship between flank wear and cutting force coefficient as well as cutting parameters such as cutting speed, depth of cut and feed. The proposed model performance has shown that the spindle displacement model predicts tool wear with high accuracy and spindle displacement signal is possible to replace cutting force signal.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.459-464
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• 2001

A reliable tool wear monitoring technique is the one of important aspects for achieving an integrated and self-adjusting manufacturing system. In this paper, a tool wear estimation approach for turning is proposed. This approach uses the model of cutting force, spindle displacement and their relation. A series of experiments were conducted by designing experimental techniques to determine the relationship between flank wear and cutting force coefficient as well as cutting parameters such as cutting speed, depth of cut and feed. The proposed model performance has shown that the spindle displacement model predicts tool wear with high accuracy and spindle displacement signal is possible to replace cutting force signal.