• KSTLE International Journal
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• 제6권1호
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• pp.1-7
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• 2005

Wear model for predicting the vehaviour of a depth is considered in this paper. It is deduced from the energy and volume based wear models such as the Archard equation and the workrate model. A new parameter of the equivalent depth ($D_e$= wear volume /worn area) is considered for the wear model of a depth prediction. A concenpt of a dissipated shear energy density is accommodated for in the suggested models. It is found that $D_e$ can distinguish the worn area shape. A cubic of $D_e$($D_e^3$) gives a better linear regression with the volume than that of the maximmum depth $D_{max}e$($D_{max}^3$) does. Both $D_{max}$ and $D_e$ are used for the presently suggested depth-based wear model. As a result, a wear depth profile can be simulated by a model using $D_{max}$. Wear resistance from the concern of an overall depth can be identified by the wear coefficient of the model using $D_e$.

• Design & Manufacturing
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• 제17권1호
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• pp.7-12
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• 2023

Recently, with the emergence of the 4th industrial revolution, the demand for smart factories and factory automation is increasing. In this study, a tool life prediction program was developed to select optimal machining conditions using CNC milling equipment, which is widely used in flexible production and automation. The equipment used in the experiment was Hwacheon Machine Tool's 5-axis machining equipment, and the tool used was a 17F2R tool. For the machining path, the down-milling cutting method was selected and long-term machining was performed. The analysis standard for side wear on the tool was set at 0.1 to 0.2 mm, and tool life data and wear data were obtained in the cutting experiment. The program was created through the data obtained from the experiment, and a prediction rate of over 90% was secured when comparing the experimental data and the predicted data.

• 한국정밀공학회지
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• 제15권4호
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• pp.118-124
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• 1998

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 variables, and then optimizing the process. Therefore, this paper describes methodology of preform design for minimizing wear of finisher die in multi-stage cold forging processes. The finite element method is combined with the routine of wear prediction. The cold forging process is analyzed using developed simulation method. In order to obtain preform to minimize die wear, the Flexible Polyhedron Search(FPS) algorithm is used. The optimal preform shape is found from iterative deformation analysis and wear calculation.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1998년도 춘계학술대회논문집
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• pp.88-93
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• 1998

The service life of tools in metal forming process is to a large extent limited by wear, fatigue fracture and plastic deformation. In warm forging processes wear is the predominant factor for operating lives of tools. To predict tool life by wear, Archard's wear model is generally applied. Usually hardness of die is considered to be a function of temperature in Archard's wear model. But hardness of die is a function of not only temperature but also operating time of die. To consider softening of die by repeated operations, it is necessary to express hardness of dies by a function of temperatures and operating time. By experiment of reheating of dies, die softening curves were obtained. Finally modified Archard's wear model in which hardness of die was expressed as a function of main tempering curve was proposed.

• 한국정밀공학회지
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• 제13권1호
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• pp.116-122
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• 1996

In forming processes, die failure must be considered before die design. One of the main reasons of die failure in industrial application of metal forming technologies is wear. The die wear affects the tolerances of formed parts, metal flow and costs of processes etc. The only way to control these failures is to develop methods which allow prediction of the die wear and which are suited to be used in the design state in order to optimize the process. In this paper, wire drawing processes were simulated using the rigid-plastic finite element method and its results were used for predicting the die wear by Archard's wear model. The effects of the temperature rising on the wear profiles of die were also investigated. The simulation results were compared with the measured die profiles.

• 소성∙가공
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• 제9권1호
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• pp.43-51
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• 2000

The die wear is one of the main factors affecting product accuracy and die life in hot forging process. It is desired to analyze die wear by developing wear prediction method combined with FE-simulatin and experiment. Lots of researches have been done into the wear analysis of cold forging die, and the results of those researches were successful, but there have been little applications to hot forging die giving successful results. That is because hot forging process has many factors influencing die wear, and there was not accurate in-process data. In this research, change of die surface hardness by thermal softening during the lifetime was obtained by experiment, and hardness distribution of cross section was measured. This wear analysis was applied to hot forging die, and gave comparatively good results compared with actual wear profile.

• Tribology and Lubricants
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• 제15권1호
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• pp.46-51
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• 1999

An analytical model about lateral crack occurring in abrasive wear of brittle solids is developed. Stress field around the lateral crack and stress intensity factor at the crack tip are analytically modeled. Abrasive wear by abrasive particle is experimentally studied. In soda-lime glass, it is observed that chipping by lateral crack occurs and produces the greatest material removal when normal load applied by the abrasive particle is about 1.5∼3.0 N. The prediction of lateral crack length from the model is compared with the experimentally measured length in soda-lime glass.

• Tribology and Lubricants
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• 제36권5호
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• pp.262-266
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• 2020

Herein, we present a numerical investigation of wear analysis of sliding systems with a constant speed subjected to Archard's wear law. For this investigation, we compared two methods: eigen-wear analysis and adaptive meshing technique. The eigen-wear analysis is advantageous to predict the evolution of contact pressure due to wear using the initial contact pressure and contact stiffness. The adaptive meshing technique in finite element analysis is employed to obtain transient wear behavior, which needs significant computational resources. From the eigen-wear analysis, we can determine the appropriate element size required for finite element analysis and the time increment required for wear evolution by a dimensionless variable above a certain value. Since the prediction of wear depends on the maximum contact pressure, the finite element model should have a reasonable representation of the maximum contact pressure. The maximum contact pressure and wear amount according to this dimensionless variable shows that the number of fine meshes in the contact area contributes more to the accuracy of the wear analysis, and the time increment is less sensitive when the number of contact nodes is significantly larger. The results derived from a two-dimensional wear model can be applied to a three-dimensional wear model.

• 한국정밀공학회지
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• 제10권3호
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• pp.69-80
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• 1993

It is important to prevent roll failure in hot rolling process for reducing maintenance coat and production loss. Roll material and rolling conditions such as the roll force and torque have been intensively investigated to overcome the roll failures. In this study, a computer roll life prediction system under working condition is developed and evaluated on IBM-PC level. The system is composed and fatigue estimation models which are stress analysis, crack propagation, wear and fatigue estimation. Roll damage can be predicted by calculating the stress anplification, crack depth propagation and fatigue level in the roll using this computer model. The developed system is applied to a work roll in actual hot rolling process for reliability evaluation. Roll failures can be diagnosed and the propriety of current working condition can be determined through roll life prediction simulation.

• 산업경영시스템학회지
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• 제44권3호
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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.