• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.197-207
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• 2018

SMEs manufacturing Factory, which are small-scale production systems of various types, mass-produce and sell products in order to meet customer needs. This means that the company has an excessive amount of material supply to reduce the loss due to lack of inventory and high inventory maintenance cost. And the products that fail to respond to the demand are piled up in the management warehouse, which is the reality that the storage cost is incurred. To overcome this problem, this paper uses ARIMA model, a time series analysis technique, to predict demand in terms of seasonal factors. In this way, demand forecasting model based on economic order quantity model was developed to prevent stock shortage risk. Simulation is carried out to evaluate the effectiveness of the development model and to demonstrate the effectiveness of the development model as applied to SMEs in the future.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.177-186
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• 2015

This study aims to suggest the direction in which furniture design can contribute, keeping with the trend of small quantity batch production by analyzing cases of furniture design manufacturing. This study analyzed cases of furnitures and lights made by 3D printing with 3 classifications. They are 1st, classification by correlation between 3D printing method and materials, 2nd, classification by 8 formative characteristics of 3D printing furniture design, 3rd, comparison analysis of competitiveness between existing furniture design and 3D printing furniture design by practicality, usability and durability. The competitiveness 3D printing technique arouses in furniture design industry, which is investigated in this thesis, is as follows. 1st, small quantity batch production, which caters to personal taste, is made possible. 2nd, transmission and transportation via digital are became more convenient. 3rd, it brings about a breakthrough in furniture design manufacturing. 4th, there is room for development into the 'smart furniture design' industry through collaborative use of 3D printing and internet of things. 5th, an Eco-friendly method of furniture design is consistently facilitated.

• 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.15 no.2
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• pp.51-56
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• 2016

High brittleness is a characteristic of glass, and in many cases it is broken during the process of machining due to processing problems, such as scratches, chipping, and notches. Machining defects occur due to the vibration of the equipment. Therefore, design techniques are needed that can control the vibration generated in the equipment to increase the strength of tempered glass. The natural frequency of the machine tool via vibration analysis (computer simulation) must be accurately understood to improve the design to ensure the stability of the machine. To accurately understand the natural frequency, 3D modeling, which is the same as actual apparatus, was used and a constraint condition was also applied that was the same as that of the actual apparatus. The maximum speeds of ultrasonic and high frequency, which are 15,000 rpm and 60,000 rpm, respectively, are considerably faster than those of typical machine tools. Therefore, an improved design is needed so that the natural frequency is formed at a lower region and the natural frequency does not increase through general design reinforcement. By restructuring the top frame of the glass processing, the natural frequency was not formed in the operating speed area with the improved design. The lower-order natural frequency is dominant for the effects that the natural frequency has on the vibration. Therefore, the design improvement in which the lower-order natural frequency is not formed in the operating speed area is an optimum design improvement. It is possible to effectively control the vibrations by avoiding resonance with simple design improvements.

• 대한공업교육학회지
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• v.39 no.1
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• pp.66-84
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• 2014

The purpose of this study is to develop STEAM hand-on activity task for middle school manufacturing & automation unit. This study was conducted following three stages. First of all, I carried out documents research and requirements analysis. And the goals for STEAM hand-on activity were set at this stage. Second, topics for STEAM hand-on activity were selected, and the organized for designing hand-on activity related STEAM in the development step. Finally, pilot and field test were conducted in order to amend and/or complement in improvement step. The theme and/or title of the hand-on activities were 'Making the print using wood', 'Making the close up photography & telephoto lens for smart phone'. The STEAM hand-on activities were designed for ten hours for each subject respectively. Each hand-on activity consists of problem situation, objectives statement, materials and tools, an evaluating criteria, related knowledge, portfolio and so on.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.44-53
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• 2021

As environmental issues have emerged worldwide, emission gas regulations have been strengthened. In the construction machinery sector, studies have been actively conducted to utilize the power source of electric motors owing to the increasing demand for zero emissions. In this study, the gear specifications of an electric axle for construction machinery were selected by considering the specifications of the motor, gear tooth contact pattern, and face load factor. The gear strength evaluation was performed at the system level using the simulation model. The bending and contact strength of the spiral bevel gears and the bending strength of the planetary gear set showed a safety factor of 1 or more. However, the contact strength of the planetary gear set showed a safety factor of 0.92. Conservative results were derived by performing the analysis under the rated load condition of the motor. However, the ratio of the equivalent torque to the rated torque of the motor was 45% or less, hence, it was determined that no difficulties should arise regarding the durability of the axle.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.47-52
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• 2021

Recently, demand for artificial intelligence solutions for production process management has been increasing in the manufacturing industry. However, through the application of AI solutions in the manufacturing industry, there are limitations to legacy smart factory solutions such as POP and MES.Therefore, in order to overcome this, this paper aims to improve production management efficiency by applying guidance, an artificial intelligence concept, to image recognition systems. In the system flow, As_is To be separated and actual work flow was applied, and the process was improved for overall productivity efficiency. The pre-processing plan for AI guidance learning was established and the relevant AI model was designed, developed, and simulated, resulting in a 97% recognition rate.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1243-1248
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• 2018

Metal Powder Metallurgy is a manufacturing technology that makes unique model parts or a certain type of product by using a hardening phenomenon when a powder of metal or metal oxide is put it into a mold and compression-molded by a press and then heated and sintered at a high temperature. Powder metallurgical press equipment is mainly used to make the parts of automobile, electronic parts and so on, and most of them are manufactured using precise servo motor. The intelligent precision servo control powder molding press system which is designed and implemented in this paper has advantages of lowering the price and maintaining the precision by using the mechanical camshaft for the upper ram part and precisely controlling the lower ram part using the high precision servo system. In addition, OPC-based monitoring and process data collection systems are designed and implemented to provide scalability that can be applied to smart manufacturing management systems that utilize Big Data in the future.