• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.12
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• pp.38-44
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• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.1-11
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• 1993

This paper concentrates on models useful for analyzing the error performance of ML(Maximum Likelihood) estimators of a single unknown signal parameter: that is the error intensity model. We first develop the point process representation for the estimation error and the conditional distribution of the estimator as well as the distribution of error candidate point process. Then the error intensity function is defined as the probability dessity of the estimate and the general form of the error intensity function is derived. We then develop several intensity models depending on the way we choose the candidate error locations. For each case, we compute the explicit form of the intensity function and discuss the trade-off among models as well as the extendability to the case of multiple parameter estimation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.2
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• pp.10-17
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• 2004

A real depth of cut in deformed zone has larger than an ideal depth of cut. So the heat generated during grinding operation makes the deformation of a workpiece surface as convex farm. Consequently the workpiece surface remains a geometric error as concave form after cooling In this study, the grinding force and the geometric error were examined in surface grinding. Through magnitude and mode of geometric error were evaluated according to grinding conditions, an optimal grinding condition was proposed to minimize the geometric error In addition, the relationship between the geometric error and the grinding force was examined. Due to least square regression, It was possible to predict the geometric error by using the grinding force.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.449-451
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• 2003

The estimation of time-series data is fundamental process in many data analysis cases. However, the unwanted measurement error is usually added to true data, so that the exact estimation depends on efficient method to eliminate the error components. The wavelet transform method nowadays is expected to improve the accuracy of estimation, because it is able to decompose and analyze the data in various resolutions. Therefore, the wavelet based Kalman filter method for the estimation of time-series data is proposed in this paper. The wavelet transform separates the data in accordance with frequency bandwidth, and the detail wavelet coefficient reflects the stochastic process of error components. This property makes it possible to obtain the covariance of measurement error. We attempt the estimation of true data through recursive Kalman filtering algorithm with the obtained covariance value. The procedure is verified with the fundamental example of Brownian walk process.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.551-556
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• 2011

This research presents an implementation strategy of Process Capability Index (PCI) according to the types of process characteristics. The types of process feature are classified as four perspectives of variation range, time period, error position, and process stage. The paper examines short-term or long-term PCI, within or between variation, position of precision or accuracy, and inclusion of measurement or calibration stage. Moreover, the study proposes normality test of unilateral PCI.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.7-12
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• 2004

Inspection and analysis are essential process to determine whether a completed product is in given specification or not. Analysis of products with very complicated shape is difficult to carry out direct comparison between inspected coordinate and designed coordinates. So process called as matching or registrations is needed to solve this problem. By defining error between two coordinates and minimizing the error, registration is done. Registration consists of translation, rotation and scale transformations. Error must be defined to express feature of inspected product. In this paper, registration algorithm is developed to determine pose of sub-frame at assembly with body of automobile by defining error between two coordinates considering geometric feature of sub-frame.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.282-289
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• 2020

Flexible roll forming is an advanced sheet-metal-forming process that allows the production of parts with various cross-sections. During the flexible process, material is subjected to three-dimensional deformation such as transverse bending, inhomogeneous elongations, or contraction. Because of the effects of process variables on the quality of the roll-formed products, the approaches used to investigate the roll-forming process have been largely dependent on experience and trial- and-error methods. Web-warping is one of the major shape defects encountered in flexible roll forming. In this study, an SVR model was developed to predict the web-warping during the flexible roll forming process. In the development of the SVR model, three process parameters, namely the forming-roll speed condition, leveling-roll height, and bend angle were considered as the model inputs, and the web-warping height was used as the response variable for three blank shapes; rectangular, concave, and convex shape. MATLAB software was used to train the SVR model and optimize three hyperparameters (λ, ε, and γ). To evaluate the SVR model performance, the statistical analysis was carried out based on the three indicators: the root-mean-square error, mean absolute error, and relative root-mean-square error.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.157-161
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• 1996

In the geometric accuracy, most of studies have been concentrated on the analysis of the geometric error, or a control path of grinding using the value of measured geometric error. In this paper, by using the value of measured motor current through hall sensor, detection of the geometric error have been accomplished, and in-process control path of grinding for improvement geometric accuracy, too.

• Journal of the Korean Institute of Gas
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• v.7 no.2 s.19
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• pp.42-47
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• 2003

To prevent major accident from toxic gas release, explosion, or fire in chemical processes, it needs dynamic control of human error with mechanical failure. Although most of major accidents occur with a coupling of human error and mechanical failure, numbers of researches have studied human error and mechanical reliability independently, but no where cross each other, to reduce the risk in the process. This work focuses on the coincidence of human error and mechanical failure for management of human error, and on some important performance shaping factors to propose a method for improving safety effectively of the process industries.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.3
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• pp.80-85
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• 2006

In an effort to reduce the evaluation time of the precision of manufactured disk cams, an effective measuring method with an exclusively built profile-measuring machine and subsequent data analysis procedure is proposed. The design and measuring data are interpolated by cubic spline curves to compute the precision error which is defined by the maximum and minimum distances between two curves. The minimum zone criterion of ISO is employed to evaluate the form error, and genetic algorithm is used to search the orientation and location of design data for the measured data which minimizes the form error. The proposed system was applied to marine engine cams, and it shows that the form error is reduced to 30% down compared with the method which minimizes the form error with the assumption that the centers of measured data design cam curve are identical.