• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.164-167
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• 1997

The die wear is one of the main factors affecting die accuracy and tool lifetime. It is desired to reduce die wear by developing simulation method to predict wear based on process parameters, and then optimize the process. Therefore, this paper describes disign methodology of preform for minimizing wear of finisher die in multi-stage cold forging processes. The finite element method is combined with the routine of wear prediction and the cold forging processes. The finite element method is combined with the routine of wear prediction and the cold forging process is analyzed. In order to obtain preform to minimize die wear, the FPS algorithm is applied and the optimal preform shape is found from iterative deformation analysis and wear calculation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.61-66
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• 1994

Tool condition monitoring is one of the most important aspects to improve productivity and quality and to achieve intelligent machining system. The tool state is classified into three groups as chipping, wear and fracture. In this study, wear of a ceramic cutting tool for hardened die material (SKD11) was investigated. Flank wear was occured more dominant than crarer wear. Therefore, to predict flank wear, the modeling of cutting force has been performed. The modeling of cutting force by an assumption that act the stress distribution on the tool face obtained through a numerical analysis. The relationships between the cutting force and the tool wear can be constructed by machining paraneters with cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions of the flank wear is approximately in good agreement with experimental result.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.39-46
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• 2003

Productivity and machining performance can be improved by cutting analysis including cutting force prediction, surface error prediction and machining stability evaluation. In order to perform cutting analysis, cutting force coefficients database have to be constructed. Since cutting force coefficients are dependent on cutting condition in the existing research, a large number of calibration tests are needed to obtain cutting force coefficients, which makes it difficult to build the cutting force coefficients database. This paper proposes a generalized method for constructing the cutting force coefficients database us ins cutting-condition-independent coefficients. The tool geometry and workpiece material were considered as important components for database construction. Cutting force coefficients were calculated and analyzed for various helix and rake angles as well as for several workpiece. Furthermore, the variation of cutting force coefficients according to tool wear was analyzed. Tool wear was found to affect tool geometry, which results in the change of cutting force coefficients.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.182-190
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• 1999

The objective of this study is to develop an analysis scheme in order to predict regrinding due to tool wear in shearing process. The analysis of material now and fracture in shearing process should precede the prediction of tool wear. Thus the developed FE-program to analyze shearing process is used. In order to predict tool wear, the wear model is reformulated as an incremental form and then the wear depth of tool is calculated at each deformation path. Because the regrinding of shearing tool is determined on the basis of allowable size of burr, the analysis of shearing process is iteratively performed using the worn profile of tool. To show the effectiveness of the scheme the simulation result is compared with experimental one.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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• pp.151-157
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• 1996

The tool condition monitoring is one of the most important aspects to improve productivity and quality of workpiece. In this study, the wear of ceramic tool (A1$_{2}$0$_{3}$-TiC Series) cutting the hardened die material(SKD11) was investigated. Flank wear was more dominant than crater wear. Therefore the modeling of cutting force related to flank wear has been performed. The cutting force model was construct- ed by an assumption that the stress distribution on the tool face is affected by tool wear. The relationship between characteristics as cutting force and tool wear can be suggested by machining parameters depending on cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions on the flank wear are approximately in good agreement with experimental results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.04a
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• pp.67-70
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• 2000

The tool life is not long enough under sever forming condition in warm forging. The tool life is affected by wear heat fatigue plastic deformation and so on. Especially wear is one of the most serious factors for tool life. To increase tool life we should consider various factors like processing design die design die materials lubrication and cooling system This study design to obtain the steady state temperature of die by FEM analysis under several conditions of cooling. There are four cooling conditions in this study no cooling internal cooling external cooling and both internal and external cooling. With above obtained temperatures tool life is predicted using Archard's model that is considered softening of die. The effect of internal cooling system is better than that of externally cooled die. To predict the die life the steady state temperature is calculated by using mean temperature of die. Considering only wear the die life much longer as the cooling effect is bigger. The more accurate die life will be predicted if we consider heat crack as well as wear.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2002.05a
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• pp.163-168
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• 2002

Hot forging is widely used in the manufacturing of industry machine component. The mechanical, thermal load and thermal softening which are happened by the high temperature in hot forging process. Tool life decreases considerably due to the softening of the surface layer of a tool caused by a high thermal load and long contact time between the tool and billet. Also, tool life is to a large extent limited by wear, heat crack and plastic deformation in hot forging process. These are one of the main factors affecting die accuracy and tool life. That is because hot forging process has many factors influencing tool life, and there was not accurate in-process data. In this research, life prediction of hot forging tool by wear and plastic deformation analysis considering tempering parameter has been carried out for automobile component. The new developed technique in this study for predicting tool life can give more feasible means to improve the tool life in hot forging process.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.