• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.2
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• pp.45-50
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• 2021

In the recent fourth industrial revolution, the demand for additive processes has emerged rapidly in many mechanical industries, including the aircraft and automobile industries. Additive processes, in contrast to subtractive processes, can be used to produce complex-shaped products, such as three-dimensional cooling systems and aircraft parts that are difficult to produce using conventional production technologies. However, the limitations of additive processes include nonuniform surface quality, which necessitates the use of post-processing techniques such as subtractive methods and grinding. This has led to the need for hybrid machines that combine additive and subtractive processes. A hybrid machine uses additional additive and subtractive modules, so product deformation, for instance, deflection, is likely to occur. Therefore, structural analysis and design optimization of hybrid machines are essential because these defects cause multiple problems, such as reduced workpiece precision during processing. In this study, structural analysis was conducted before the development of an additive/subtractive hybrid processing machine. In addition, structural optimization was performed to improve the stability of the hybrid machine.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.159-165
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• 2023

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

• Design & Manufacturing
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• v.18 no.2
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.109-118
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• 2020

In recent years, the needs of consumers for personality and personalized clothing are increasing. Knitting machines, which produce clothing by the user at low cost, are a good way to meet consumer demand. However, the user is having difficulty in using the knitting machine pattern program, which is a software program, independently of the operation of the knitting machine. Therefore, this study conducted a literature review prior to the empirical research and evaluated the usability by selecting three kinds of frequently used knitting machine pattern programs as research subjects. Based on the nine usability evaluation principles derived from expert group discussions, the study found that the needs of users for nine evaluation principles: visibility, conciseness, operability, consistency, accuracy, flexibility, intuition, error recognition, and supplementary explanation (The purpose of this study is to identify the direction and alternatives of usability improvement for the interface of the knitting machine pattern program.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.98-105
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• 2001

The machining accuracy is affected by geometric, volumetric errors of the machine tools. To improve the product quality, we need to enhance the machining accuracy of the machine tools. To this point of view, measurement and inspection of finished part as error analysis of machine tools ahas been studied for last several decades. This paper suggests the enhancement method of machining accuracy for precision machining of high quality metal reflection mirror or optics lens, etc. In this paper, we study 1) the compensation of linear pitch error with NC controller compensation function using laser interferometer measurement, 2) the method for enhancing the accuracy of NC milling machining by modeling and compensation of volumetric error, 3) the spherical surface manufacturing by modeling and compensation of volumetric error of the machine tool, 4) the system development of OMM without detaching work piece from a bed of machine tool after working, 5) the generation of the finished part profile by OMM. Furthermore, the output of OMM is compared with that of CMM, and verified the feasibility of the measurement system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.65-66
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• 2010

To improve the gear noise quality, gear tooth grinding machine are widely used in automotive industry. While using the gear profile grinding machine to improve the gear tooth quality of the transmission, several defects such as chattering, tooth waves that cause the gear noise occasionally happened. But it is very difficult to solve that problem, because there is no one who knows the setting up the optimal grinding condition appropriately. The abnormal manufacturing conditions which make the gear noise make the engineer to spend a lot of time, effort, and money. Due to demands for solving the serious abnormal gear noise happened in the new FF 6th stage automatic transmission in the mass product stage, the vibration checking process in the worm wheel axis, work rotation and fixed axis of the grinding machine were adapted to find the root causes. As a result, gear profile wave are affected by the work rotation axis's unbalance which is caused by worm wheel feeding speed. And a primary and the secondary grinding feeding speed, cutting oil, work fixed forces are also proved as the important factors. After setting up the grinding condition reported in this paper, it was adapted successfully to the grinding machine to manufacture the new FF 6th speed automatic transmissions' output gear. The gear noise was dramatically disappeared and the process and results will offer good guides to the engineers who manufacture the gear with the grinding machine.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.721-731
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• 2018

Manpower in domestic construction sites is becoming more and more aging. Various methods have been devised to prevent productivity and quality deterioration of construction due to absence of skilled workers and difficulty in supplying manpower. Especially, many researchers study various methods such as Machine Guidance (MG) and Remote Machine Control to improve productivity and quality. Although many prior studies have been conducted since the advent of MG, There is lack of field test in a difficult site to stakeout. In this study, field test of MG excavator was carried out at difficult site to stakeout, and productivity analysis and quality evaluation were conducted. As a result of the analysis of productivity, the minimum value was 20.5%, the maximum value was 56.9%, and the average productivity in 4 days was 38.3% higher than the standard product. As a result of the analysis of quality, the horizontal error ${\pm}1cm$ and the vertical error ${\pm}2cm$ confirmed in the previous study were verified.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.7
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• pp.665-671
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• 2010

To improve the gear noise quality, gear tooth grinding machine are widely used in automotive industry. While using the gear profile grinding machine to improve the gear tooth quality of the transmission, several defects such as chattering, tooth waves that cause the gear noise occasionally happened. But it is very difficult to solve that problem, because there is no one who knows the setting up the optimal grinding condition appropriately. The abnormal manufacturing conditions which make the gear noise make the engineer to spend a lot of time, effort, and money. Due to demands for solving the serious abnormal gear noise happened in the automatic transmission in the mass product stage, the vibration checking process in the worm wheel axis, work rotation and fixed axis of the grinding machine were adapted to find the root causes. As a result, gear profile wave are affected by the work rotation axis's unbalance which is caused by worm wheel feeding speed. And a primary and the secondary grinding feeding speed, cutting oil, work fixed forces are also proved as the important factors. After setting up the grinding condition reported in this paper, it was adapted successfully to the grinding machine to manufacture the new automatic transmissions' gear. The gear noise was dramatically disappeared and the process and the results will offer good guides to the engineers who manufacture the gear with the grinding machine.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.263-270
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• 2014

This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.