• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.6
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• pp.1490-1500
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• 1995

Compression molding of SMC (Sheet Molding Compound) in a thin plaque with substructures like a rib is involved with the formation of surface defect along the centerline over the rib area called by sink mark depending on process parameters. The surface quality of the external panels in automotive manufacturing is so critical that this kind of defect should be eliminated during manufacturing stages. The effect of process parameters on sink mark formation and the distribution of chopped fiberglasses in the compression molded thin plaque with a rib was experimentally investigated in the present study. In order to estimate the effect of the molding parameters such as molding temperature, mold closing speed, depth of the rib, corner radius of the rib, and final molded part thickness of flat portion on the depth of sink mark and the distribution of fiberglasses in the molded SMC part with the rib under the present experimental conditions, the molding parameters used in experiments were non-dimensionalized equation for predicting the depth of sink mark was determined through dimensional analysis based on the experimental data. The orientation and distribution of fiberglasses and fillers which directly affect the formation and depth of sink mark were investigated by taking the photographs of the cross-sectional area of the molded specimen using scanning electron microscope. The experimental results proposed from this investigation are useful in understanding the formation of sink mark and predicting the depth of sink mark in compression molding of SMC with substructures.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.4
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• pp.686-692
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• 2013

Today, environmental issues have become a matter of worldwide concern. In particular, automobile industries engage in considerable research and investment to develop high-efficiency and ecofriendly cars. Most ecofriendly cars use natural gas or hydrogen gas instead of fossil fuels. In this regard, low-weight and high-pressure vessels have gradually been developed to increase the driving distance of a car. However, most pressure vessels installed in cars develop many defects over time owing to shocks sustained when the car is being driven. Such defects can cause the explosion of the pressure vessel. Therefore it is important to prevent such explosions due to internal defects. The use of shearography for measuring the internal defects of objects afford many advantages. It is a non-contact and non-destructive method, and it is not limited by the object shape. In this study, the internal defect deformation and strain of an aluminum liner that is used in a CNG bus for the fuel storage tank is measured using shearography. It is important to measure the strain and deformation in order to detect defects and repair the pressure vessel. To verify the accuracy of the shearography measurement method, the measurement results of shearography, out-of-plane ESPI, and FEM are compared quantitatively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.335-341
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• 2017

This paper proposes an image-processing algorithm for inspecting double-cut defects in the motor shaft manufacturing process. The algorithm consists of extracting the outline using the brightness of the image, obtaining a binarized boundary graph using the extracted outline, and determining the defects from the graph. Defects in which two cut surfaces are separated are considered type 1 defects, and those in which two cut surfaces are connected are defined as type 2 defects. In an actual manufacturing process, 112 good samples and 44 defective samples (34 type 1 defects and 10 type 2 defects) were collected and used to verify the algorithm. The samples were judged with 100% accuracy for both type 1 and type 2 defects. The algorithm will be used in the field after securing reliability for various samples.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.43-48
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• 2017

Semiconductors are leading the development of industrial technology, leading to miniaturization and weight reduction of electronic products as a leading technology, we are dragging the electronic industry market Especially, the semiconductor manufacturing process is composed of highly accurate and complicated processes, and effective production is required Recently, a vision system combining a computer and a camera is utilized for defect detection In addition, the demand for a system for measuring the shape of a fine pattern processed by a special process is rapidly increasing. In this paper, we propose a vision algorithm using mask image to detect scratch defect of semiconductor pockage. When applied to the manufacturing process of semiconductor packages via the proposed system, it is expected that production management can be facilitated, and efficiency of production will be enhanced by failure judgment of high-speed packages.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.6
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• pp.529-536
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• 2015

A defect could be generated in bolts for a use of oil filters for the manufacturing process and then may affect to the safety and quality in bolts. Also, fine defects may be imbedded in oil filter system. So it is very important that such defects be investigated and screened during the multiple manufacturing processes. Therefore, in order effectively to evaluate the fine defects, the FEM simulations were performed to make characterization in the crack detection of the bolts and the parameters such as number of turns of the coil, the coil size, applied frequency were calculated based on the simulation results. Simulations were carried out for the defect signal of eddy current probe. Exciter and receiver were utilized. In this paper, the FEM simulations were performed in both bobbin-type and pancake-type probe, both probes were optimized under Eddy current FEM simulations and the results of calculation were discussed.

• Smart Structures and Systems
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• v.22 no.4
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• pp.481-493
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• 2018

Ultrasonic guided waves have attracted increasing attention for non-destructive testing (NDT) and structural health monitoring (SHM) of bridge cables. They offer advantages like single measurement, wide coverage of acoustical field, and long-range propagation capability. To design defect detection systems, it is essential to understand how guided waves propagate in cables and how to select the optimal excitation frequency and mode. However, certain cable characteristics such as multiple wires, anchorage, and polyethylene (PE) sheath increase the complexity in analyzing the guided wave propagation. In this study, guided wave modes for multi-wire bridge cables are identified by using a semi-analytical finite element (SAFE) technique to obtain relevant dispersion curves. Numerical results indicated that the number of guided wave modes increases, the length of the flat region with a low frequency of L(0,1) mode becomes shorter, and the cutoff frequency for high order longitudinal wave modes becomes lower, as the number of steel wires in a cable increases. These findings were used in design of transducers for defect detection and selection of the optimal wave mode and frequency for subsequent experiments. A magnetostrictive transducer system was used to excite and detect the guided waves. The applicability of the proposed approach for detecting and locating wire breakages was demonstrated for a cable with 37 wires. The present ultrasonic guided wave method has been found to be very responsive to the number of brokenwires and is thus capable of detecting defects with varying sizes.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.6
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• pp.128-133
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• 2011

In this study, the steel material for shipbuilding(LR-A class) was used, and FCAW was taken advantage of 3G attitude and they are welded by different welding ways. As a result of analyzing wave with welding monitoring system, the stable values are obtained which are the first floor(electronic current 164~182 A, voltage 24 V), the second floor(electronic current 174~190 A, voltage 22~25 V), the third floor(electronic current 158~188 A, voltage 22~25 V), and fourth floor(electronic current 172~184 A, voltage 22~25 V), at this time, the stable wave standard deviation and changing coefficient could be obtained. When the welding testing through nondestructive inspection was analyzed know defect of welding, there was no defect of welding in A, D, E, but some porosities in B, and slag conclusion near the surface in C, because the length of arc was not accurate, and the electronic current and voltage was not stable. After observing the change of heat affect zone through micro testing, each organization of floor formed as Grain Refinement, so welding part was fine, the distance of heat affect zone is getting wider up to change the values of the electronic current and voltage. As a result of degree of hardness testing, the hardness orders were the heat affect zone(HAZ), Welding Zone(WZ), and Base Metal(BM). When the distribution of degree of hardness is observed. B is the highest degree of hardness The reason why heat effect zone is higher than welding zone and base metal, welding zone is boiled over melting point($1539^{\circ}C$) and it starts to melt after the result of analysis through metal microscope, so we can know that delicate tissue is created at the welding zone. Therefore, in order to get the optimal conditions of the welding, the proper current of the welding and voltage is needed. Furthermore the precise work of welding is required.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.44-51
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• 2019

Three types of samples with defects were measured by lock-in med-IR (infrared) thermography with various lock-in frequencies for different materials. The lock-in method can be used to detect defects when an external energy source is applied to the object, the non-uniformity of the incident thermal energy distribution is eliminated, and the camera's measurement cycle is synchronized with the load cycle of the incident energy source. For inspecting samples with defects, results of thermal images are analyzed when three types of materials, i.e., SM45C, STS316L, and AL6061 are tested and three lock-in frequencies, i.e., 0.08, 0.1, and 0.12 Hz are applied. In this study, the optimal lock-in frequencies were determined by comparing the results of each material and lock-in frequency measured using the mid-IR camera.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2037-2042
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• 2017

The manufacturing facility is generally operated by a pre-set program under the existing factory automation system. On the other hand, the manufacturing facility must decide how to operate autonomously in Industry 4.0. Determining the operation mode of the production facility itself means, for example, that it detects the abnormality such as the deterioration of the facility at the shop-floor, prediction of the occurrence of the problem, detection of the defect of the product, In this paper, we propose a manufacturing process modeling using a queue for detection of manufacturing process abnormalities at the shop-floor, and detect abnormalities in the modeling using SVM, one of the machine learning techniques. The queue was used for M / D / 1 and the conveyor belt manufacturing system was modeled based on ${\mu}$, ${\lambda}$, and ${\rho}$. SVM was used to detect anomalous signs through changes in ${\rho}$.