• 한국정밀공학회지
• /
• 제20권1호
• /
• pp.222-228
• /
• 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.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2001년도 춘계학술대회 논문집
• /
• pp.459-464
• /
• 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.

• Journal of Welding and Joining
• /
• 제28권6호
• /
• pp.35-39
• /
• 2010

Generally, ceramic material is very difficult to machine due to high strength and hardness. However, ceramic material can be machined at high temperature by plastic flow as metallic material due to the deterioration of the grain boundary glassy phase. Recently, a new method was developed to execute cutting process with CBN cutting tool by local heating of surface with laser. There are various parameters in LAM because it is a complex process with laser treatment and machining. During laser assisted machining, high power results in reducing of cutting force and increasing tool life, but excessive power brings oxidation of the surface. The effect of laser power, feed rate, cutting depth and etc. were investigated on the life of cutting tool. Chips were observed to find out suitable machining conditions. Chips of SSN had more flow-types than HIPSN. It means SSN is easier to machining. The life of cutting tool was increased with increasing laser power and decreasing feed rate and cutting depth.

• 한국기계가공학회지
• /
• 제14권6호
• /
• pp.1-6
• /
• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• Journal of Advanced Marine Engineering and Technology
• /
• 제34권7호
• /
• pp.963-970
• /
• 2010

This study makes an estimate of the laser-assisted machining (LAM) of an economically viable process for manufacturing precision silicon nitride ceramic parts using a high-power diode laser (HPDL). The surface is locally heated by an intense laser source prior to material removal, and the resulting softening and damage of the workpiece surface simplify the machining of the ceramics. The most important advantage of LAM is its ability to produce much better workpiece surface quality compared to conventional machining. Also important are its larger material removal rates and longer tool life. The cutting force and surface temperature were measured on-line using a pyrometer and a dynamometer, respectively. Tool wear, chips and the surface of the workpiece were measured using optical microscopy, and the surface and fractured cross-section of $Si_3N_4$ were measured by SEM. During the LAM process, the cutting force and tool wear were reduced and oxidation of the machined surface was increased according to the increase in the laser power. Moreover, the more the feed rate increased, the more the cutting force and tool wear increased.

• 한국정밀공학회지
• /
• 제12권2호
• /
• pp.123-134
• /
• 1995

In this paper, the fast force algorithm has been studied so that it is used to calculate cutting forces and to develope the NC verification system. The NC verification using the fast force algorithm can verify excessive cutting force which is the cause of deflection and breakage of tool, and adjust the feed rate and spindle speed.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1997년도 추계학술대회 논문집
• /
• pp.59-64
• /
• 1997

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; AEavg, AErms, AEmode, as the base working to monitor the tool wear with in-process. For this purpose, cutting tests were conducted on a CNC lathe with comprehensive cutting conditions.. It is known that AEavg and AErms are proportionaly increased as the increasing of cutting velocity and depth of cut respectively. It is also known that AEmode among three kinds of AE signals may be applied for in-process monitoring to make the self diagnosis system because of its stability to the variation of cutting condition.

• 한국생산제조학회지
• /
• 제7권6호
• /
• pp.64-71
• /
• 1998

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; $AE_{avg}$, $AE_{rms}$, $AE_{mode}$$AE_{avg}$ and $AE_{rms}$ are increased as the increasing of cutting velocity and depth of cut respectively. The new parameters, derived from $AE_{avg}$ and $AE_{rms}$, which may be used for the in-process detection of tool wear is discussed. It is also known that $AE_{mode}$

• 오토저널
• /
• 제14권6호
• /
• pp.113-126
• /
• 1992

Recently the multi-kind, small-amount manufacturing system has been replacing the mass manufacturing system, and domestic machining inustry also is eager to absorb the new technology because of its high productivity and cost reduction. The optimization of the cutting condition has been a vital problem in the machining industry, which would help increase the productivity and raise the international competitiveness. It is intended in this study to investigate the machining costs per unit time which is essential to the analysis of the optimal cutting condition, to computer the cutting speed that lead to the minimum machining costs and the maximum production to suggest the cutting speed range that enables efficient speed cutting, and to review the machining economy in relation to cutting depth and feed. Also considered are the optimal cutting speed and prodution rated in rrelation with feed. It is found that the minimum-cost cutting speed increases and the efficient cutting speed range is reduced as machining cost per unit time increases since the cutting speed for maximum production remains almost constant. The machining cost is also lowered and the production rate increases as the feed increases, and the feed should be selected to satisfy the required surface roughness. The machining cost and production rate are hardly affected by the cutting depth if the cutting speed stays below 100m/min, however, they are subject to change at larger cutting depth and the high-efficient speed range also is restricted. It can be established an adaptive optimal cutting conditions can be established in workshop by the auto-selection progam for optimal operation. It is expected that this method for choosing the optimal cutting conditions might contribute to the improvement of the productivity and reduced the cost. It is highly recommended to prepare the optimal cutting conditionthus obtained for future use in the programing of G-function of CNC machines. If proper programs that automatically select the optimal cutting conditions should be developed, it would be helpful to the works being done in the machine shops and would result in noticeable production raise and cost reduction.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2005년도 춘계학술대회 논문집
• /
• pp.452-455
• /
• 2005

The cutting characteristics of hardened steel by a PCBN tool is investigated with respect to workpiece surface roughness, cutting force and tool flank wear of the vision system. Backpropagation neural networks (BPNs) were used for detection of tool wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output was the tool wear state which was either usable or failure. Hard turning experiments with various spindle rotational speed and feed rates were carried out. The learning process was performed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.