• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2009.04a
• /
• pp.62-64
• /
• 2009

Control of back-up roll deformation and wear profile in plate rolling is important not only for enhancement of the structural precision of mill, but also for improving the yield and rolling operation. in the heavy plate mill, there have been strong demands for upgrading back up roll operation technology. In order to satisfy these demands, it is essential to develop the backup roll deformation analysis models and wear profile prediction models. This paper gives a general description of the backup roll deformation and wear model, simulation result for deformation analysis and wear profile.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.575-578
• /
• 2000

Die design to minimize the die wear in the cold forging process is very important as it reduce the production cost and the increase of the production rate. The quantitative estimation for the die wear is too hard because the prediction of the die wear is determined with many process variables. So, in this paper, the optimal shape of the backward extrusion punch is newly designed through the FE-analysis considering the surface expansion and Archard's wear model in order to reduce the rapid wear rate that is generated for the backward extrusion product exceeding the forming limit. The main shape variables of the backward extrusion punch are the flat, angle, and round of the punch nose part. As the flat and angle of the punch nose are larger, the surface expansion is reduced. and, the wear rate is decreased according to the reduction of the punch round. These results obtained through this study are applied to the real manufacturing process, it is implemented the reduction of the wear rate.

• Journal of the Korean Society of Safety
• /
• v.38 no.4
• /
• pp.1-7
• /
• 2023

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.20 no.6
• /
• pp.1091-1104
• /
• 2018

Wear prediction of TBM disc cutters is a very important issue during design as well as construction stages for hard rock TBMs as the cutter head intervention is directly related to the time and cost of tunneling. For that, some methods such as NTNU, CSM and Gehring models were used to predict disc cutter wear and intervention interval. There are however some problems to be addressed in these models in terms of accuracy and time for testing, so that a NAT (New Abrasion Tester) model has been developed in order to achieve simplicity and reliability together at the same time (Farrokh and Kim, 2018). On the basis, the proposed NAT model has been applied to ${\bigcirc}{\bigcirc}$ project in Korea. A comparative study was performed to compare with the conventional methods and as a result the NAT model showed a very good agreement with actual cutter life. The NAT model will be further applied to other projects to establish credibility.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.13 no.1
• /
• pp.48-55
• /
• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.4 s.97
• /
• pp.229-236
• /
• 1999

The surface, generated by end milling operation, is deteriorated by tool runout, vibration, tool wear and tool deflection, etc. Among them, the effect of tool deflection on surface accuracy is analyzed. Surface generation model for the prediction of the topography of machined srufaces has been developed based on cutting mechanism and cutting tool geometry. This model accounts for not only the ideal geometrical surface, but also the deflection of tool due to cutting force. For the more accurate prediction of cutting force, flexible end mill model is used to simulate cutting process. Computer simulation has shown the feasibility of the surface generation system. Using developed simulation system, the relations between the shape of end mill and cutting conditions are analyzed.

• Tunnel and Underground Space
• /
• v.32 no.6
• /
• pp.502-517
• /
• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.6
• /
• pp.553-581
• /
• 2022

Shortly after tunnel boring machine (TBM) was introduced in the tunneling industry, the use of TBM has surprisingly increased worldwide due to its performance together with the benefit of being safely and environmentally friendly. One of the main cost items in the TBM tunneling in rock and soil is changing damaged or worn cutters. It is because that the cutter change is a time-consuming and costly activity that can significantly reduce the TBM utilization and advance rate and has a major effect on the total time and cost of TBM tunneling projects. Therefore, the importance of accurately evaluating the cutter life can never be overemphasized. However, the prediction of cutter wear in soil, rock including mixed face is very complex and not yet fully clarified, subsequently keeping engineers busy around the world. Various prediction models for cutter wear have been developed and introduced, but these models almost usually produce highly variable results due to inherent uncertainties in the models. In this study, a case study of design and construction of disc cutter change is introduced and analyzed, rather than proposing a prediction model of cutter wear. As the disc cutter is strongly affected by the geological condition, TBM machine characteristic and operation, authors believe it is very hard to suggest a generalized prediction model given the uncertainties and limitations therefore it would be more practical to analyze a real case and provide a detailed discussion of the difference between prediction and result for the cutter change. By doing so, up-to-date idea about planning and execution of cutter change in practice can be promoted.

• Geomechanics and Engineering
• /
• v.29 no.3
• /
• pp.219-228
• /
• 2022

The most widely used parameter to represent rock abrasiveness is the Cerchar abrasivity index (CAI). The CAI value can be applied to predict wear in TBM cutters. It has been extensively demonstrated that the CAI is affected significantly by cementation degree, strength, and amount of abrasive minerals, i.e., the quartz content or equivalent quartz content in rocks. The relationship between the properties of rocks and the CAI is investigated in this study. A database comprising 223 observations that includes rock types, uniaxial compressive strengths, Brazilian tensile strengths, equivalent quartz contents, quartz contents, brittleness indices, and CAIs is constructed. A linear model is developed by selecting independent variables while considering multicollinearity after performing multiple regression analyses. Machine learning-based regression methods including support vector regression, regression tree regression, k-nearest neighbors regression, random forest regression, and artificial neural network regression are used in addition to multiple linear regression. The results of the random forest regression model show that it yields the best prediction performance.

• Proceedings of the KSME Conference
• /
• 2005.11a
• /
• pp.113.2-113.2
• /
• 2005