• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1996년도 춘계학술대회 논문집
• /
• pp.135-139
• /
• 1996

In this a paper, a new method to measure the wear of the single crystal diamond(SCD) tool using image processing is presented. To increase resoultion, high magnifying lens is used and to enlarge the measurement field of view, a image region matching method is applied. The shape of SCD tool is modeled by mathematical analysis. Cutting edge chipping and wear are calculated by the model. This method is proved to be efficient in detecting a few micron of wear and cutting edge loss by chipping along the whole cutting edge.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2000년도 춘계학술대회 논문집
• /
• pp.135-142
• /
• 2000

This Paper describes a characteristics of wheel wear of high speed train running on the conventional line. Conventional line has many curved tracks that cause severe wheel flange wear. The influences of lubrication, cant deficiency, owe radius on wheel wear are also described considering the operation performance of the highspeed trainset. A method of calculation using contact patch work model is presented far determination of the evolution by wear of railway wheels.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1992년도 춘계학술대회 논문집
• /
• pp.306-312
• /
• 1992

The prevention of roll breakage in hot rolling process is improtant to reduce maintenance cost and production loss. Rolling conditions such as the roll force and torque have been intensively studied to overcome the roll breakage. in the present work, a model for life prediction of work rolls under working condition was developed and discussed. The model consists of stress analysis, crack propagation, wear and fatigue calculation model. Roll life can be predicted by stress, crack depth and fatigue damage calculated from this model. The reliability of stress analysis is backed up by the FEM analysis. From the result of simulation using by pressent model, although the fatigue damage of back up roll reachs 80% of practical limit, that of workroll was less than 40%. In edge section of workroll stress amplification is found by wear and bender effect. We can judge that workroll failures are not due to fatigue damage, crack propagation by bending stress but stress amplification by wear and bender in present working condition.

• 산업경영시스템학회지
• /
• 제43권3호
• /
• pp.95-100
• /
• 2020

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. The representative type of the degeneration is wear of tool or machine. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore a periodic preventive resetting the process is necessary. The total cost consists of three items: adjustment cost (or replacement cost), non-conforming cost due to product out of upper or lower limit specification, and quality loss cost due to difference from the process target value and the product characteristic value among the conforming products. In this case, the problem of determining the adjustment period or wear limit that minimizes the total cost is called the 'process mean shift' problem. It is assumed that both specifications are set and the wear level can be observed directly. In this study, we propose a new model integrating the quality loss cost, process variance, and production volume, which has been conducted in different fields in previous studies. In particular, for the change in production volume according to the increasing in wear level, we propose a generalized production quantity function g(w). This function can be applied to most processes and we fitted the g(w) to the model. The objective equation of this model is the total cost per unit wear, and the determining variables are the wear limit and initial process setting position that minimize the objective equation.

• 소성∙가공
• /
• 제24권4호
• /
• pp.264-271
• /
• 2015

In the current study, a method was proposed to quantitatively predict the wear and fatigue life of a shearing die in order to determine an effective replacement period for the die. The shearing die model of a retainer manufacturing process was used for the proposed method of quantitative life prediction. The retainer is produced through shearing steps, such as piercing and notching. The shearing die of the retainer is carefully controlled because the dimensional accuracy of the retainer is critical. The fatigue life for the shearing die was predicted using ANSYS considering S-N curves of STD11 and Gerber’s equation. The wear life for the shearing die was predicted using DEFORM-3D considering the Archard’s wear model. Experimental shearing of the retainer was conducted to verify the effectiveness of the proposed method for predicting die life. The fatigue failure of the shearing die was macroscopically measured. The wear depth was measured using a 3D coordinate measuring machine. The results showed that the wear and fatigue life in the FE analysis agree well with the experimental results.

• 한국기계가공학회지
• /
• 제12권4호
• /
• pp.47-54
• /
• 2013

The main cause of die failure in hot forging is wear. Die wear directly generates the gradual loss of part tolerances, thereby causing deterioration in the dimensional accuracy of a forged part. It is very important to estimate forging cycles, called as die life, at which the die should be repaired or replaced. In this study, in order to estimate the hot forging die life, the finite element simulation of wear on an asymmetric part like a ball joint socket used in vehicle was carried out based on Archard's model. Finite element simulation results were compared with wear amounts of a used die that were measured using a contact stylus profilometer. The simulation results were in relatively good agreement with measurements obtained from the virtual die which was used by 7,000 forging cycles in a forging industry. Consequently, the die life in the hot forging of the ball joint socket was estimated by 10,500 forging cycles on the finisher die.

• 한국의류학회지
• /
• 제36권2호
• /
• pp.218-230
• /
• 2012

This study examines the influence of innovativeness and price sensitivity on the purchase intention of smart wear. A total of 211 copies of data were analyzed to investigate a structural model and test research hypotheses using AMOS 7.0 package. The results were summarized as follows. First, fashion innovativeness and technology innovativeness negatively affected price sensitivity; however, information innovativeness positively affected price sensitivity. Second, technology innovativeness positively affected the purchase intention of smart wear; however, fashion innovativeness and information innovativeness did not affect the purchase intention of smart wear. Third, price sensitivity negatively influenced the purchase intention of smart wear. This study is useful for marketers and managers who establish marketing and a price strategy for smart wear in an exploration of the relation of consumer innovativeness, price sensitivity, and the purchase intention of smart wear.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2005년도 춘계학술대회 논문집
• /
• pp.855-859
• /
• 2005

Carbon fiber polymeric composites have been widely used in bearing materials under high pressure without oil-lubrication due to their self-lubricating characteristics. However, the severe wear of carbon composite surface occurs due to the generation of wear debris when the pressure applied on the composite surface is higher than the critical value of composite surface. In this work, in order to remove wear debris continuously during sliding operation, composite specimens with many micro-grooves on their sliding surfaces were devised. To investigate the effect of wear debris on the tribological behavior of carbon/epoxy composites, dry sliding tests were performed with respect to applied pressure using the composite specimens with and without micro-grooves. From the measurement of friction coefficients and wear rates, a model for the effect of wear debris on the friction and wear of composites was proposed.

• Elastomers and Composites
• /
• 제56권1호
• /
• pp.1-5
• /
• 2021

Tire and road wear particles (TRWPs) were collected from road dust and thermogravimetric analysis (TGA) was performed to measure the content of tire wear particles (TWPs) in the TRWPs. The TGA thermograms of TRWPs showed two weight loss steps associated with polymer decomposition including weight loss after 480℃ which may be due to road wear particles. Different samples gave different TGA thermograms because the types and contents of the road wear particles attached to the TWPs should be different from each other, and each TWP might have different composition. The TGA results of the model asphalt pavement wear particles, with (volatile organics + polymers + carbon black) : ash = 33.5 : 66.5, was applied to the TRWP results, and the TWP contents of TRWPs were found to be 50-65%. The zinc oxide content in the rubber compound was negligible.

• Geomechanics and Engineering
• /
• 제29권3호
• /
• pp.249-258
• /
• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.