• 산업경영시스템학회지
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• 제43권4호
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.174-181
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• 1999

In this paper the technique to predict tool wear theoretically in shearing process is suggested. The tool wear in the process affects the tolerances of final pans, metal flows and costs of processes. In order to predict the tool wear the deformation of workpiece during the process is analyzed by using non-isothermal finite element program. The ductile fracture criterion and the element kill method are also used to estimate if and where a fracture will occur and to investigate the features of the sheared surface in shearing process. Results obtained from finite element simulation, such as nodal velocities and nodal forces, are transformed into sliding velocity and normal pressure on tool monitoring points respectively. The monitoring points are automatically generated and the wear rates on these points are accumulated during the process. It is assumed that the wear depth on the tool surface is linear function of the lot sizes based upon the known experimental results. The influence of clearance between die and punch upon tool wear is also discussed.

• 한국자동차공학회논문집
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• 제10권4호
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• pp.199-205
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• 2002

This paper presents a numerical technique to analyze wear life of automotive disk brake pad, where FFT-FEM method is adopted to determine the transient temperature distribution of the disk surface. A specimen ova frictional material is tested on a small scale brake dynamometer to find the dependency of the wear rate on temperature change, from which and the temperature analysis results, given the wear test mode, wear behavior of the pad material fur the vehicle can be predicted. Numerical examples show the predicted wear life of the vehicle coincides with the manufacture's recommended time interval for replacing the pads.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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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.

• 대한기계학회논문집A
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• 제32권11호
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• pp.930-936
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• 2008

This paper studies the effect of pad at initial stage and wear during braking on the dynamic contact pressure distribution. Wear is influenced by variable factor (contact pressure, sliding speed, radius, temperature) during dynamic braking and variation in contact pressure distribution. Many researchers have conducted complex eigenvalue analysis considering wear characteristic with Lim and Ashby wear map. The conventional analysis method is assumed the pad has smooth and flat surfaces. The purpose of this paper is to validate that wear rate induced by braking is considered for the precise squeal prediction. After obtaining pad wear from experiment, it is incorporated with FE model of brake system. Finally, the comparisons in fugitive nature of squeal will be carried out between the complex eigenvalue analysis and noise dynamometer experiment.

• 한국공작기계학회논문집
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• 제12권5호
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• pp.53-58
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• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.

• Journal of Computational Design and Engineering
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• 제3권4호
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• pp.337-348
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• 2016

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.

• 소성∙가공
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• 제7권3호
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• pp.282-290
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• 1998

In bulk metal forming processes prediction of tool life is very important for saving production cost and achieving good material properties. Generally the service life of tools in metal forming process is limited to a large extent by wear, fracture and plastic deformation of tools. In case of hot and warm forging processes tool life depends on wear over 70%. In this study finite element analyses are con-ducted to warm and hot forging by adopting suggested wear model. By comparison of simulation and eal profile of die suggested wear model. By comparison of simulation and real profile of die suggested model is verified.

• 한국정밀공학회지
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• 제16권12호
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• pp.46-53
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• 1999

Cutting force characteristics is closely related with tool wear on the end milling. And it is found that the tool wear can be properly obtained by observation through the tool-maker's microscope when STS 304 is cut using an end mill. The relationship between the tool wear and the cutting force is established based on data obtained from a series of experiments. A cutting force model can be derived from basic cutting force model using parasitic force components of this tool wear. The results of th simulation using the cutting force model proposed in this paper were verified experimentally and a good agreement was partly obtained. The proposed model is capable of predicting increased cutting force due to tool wear.

• 한국정밀공학회지
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• 제20권4호
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• pp.136-142
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• 2003

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45 ${\mu}{\textrm}{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.