• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.810-814
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• 1996

The purpose of this exclusive welding-machine process using the welding Torch-rotation form is to develop a mechanism which can solve the problem of twisted welding wires and cables. The technique was developed by revising the torch position and smooth controlling of both the formal and reverse rotation. Some of the advantages of using the Torch-rotation form over the Work-rotation technique are the practical uses of increased work space and link work with the automation system of the plant. Using this welding machine process, It is possible to design a specific tool in order to solve the implemental problem. And I produced a control plate which can manipulate the progress of the entire process at the work place. Even if another kind of axle casing's welding work is used this process can be utilized if the fixed tip and work is produced and changed. The development if this exclusive welding-machine could reduce the manpower of skilled welding labor and after considerable analysis, this machine was found to increase productivity and better quality product in comparison to the handmade product.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• 무역학회지
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• 제47권2호
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• pp.69-88
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• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• 한국생산제조학회지
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• 제24권1호
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• pp.7-14
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• 2015

The machine tools builders are trying to improve the efficiency and performance of the machine tools. The electro-mechanical driven broaching machine has many advantages such as lower noisy operating, higher energy efficiency, and smaller space of installation. This paper presents the structural and mechanical development of an electro-mechanical driven broaching machine that is replaced for traditional hydraulic one. The servo motor, ball screw and roller linear guide are used instead of hydraulic cylinder and translation frictional sliding guides. The simulation method based on FEM was applied to analyze the stress, deformation of the machine for static analysis. The dynamic analysis was carried out for verifying and assessing the mechanical behavior of the developed broaching machine. This work helps broaching machine developer make a better product at the early design stage with lower cost and development time.

• Coupled systems mechanics
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• 제1권2호
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• pp.205-217
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• 2012

A smart beam made of magneto-electro-elastic (MEE) material having piezoelectric phase and piezomagnetic phase, shows the coupling between magnetic, electric, thermal and mechanical under thermal environment. Product properties such as pyroelectric and pyromagnetic are generated in this MEE material under thermal environment. Recently studies have been published on the product properties (pyroelectric and pyromagnetic) for magneto-electro-thermo-elastic smart composite. Hence, the magneto-electro-elastic beam with different volume fractions, investigated under uniform temperature rise is the main aim of this paper, to study the influence of product properties on clamped-free boundary condition, using finite element procedures. The finite element beam is modeled using eight node 3D brick element with five nodal degrees of freedom viz. displacements in the x, y and z directions and electric and magnetic potentials. It is found that a significant increase in electric potential observed at volume fraction of $BaTiO_3$, $v_f$ = 0.2 due to pyroelectric effect. In-contrast, the displacements and stresses are not much affected.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.966-978
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• 1993

A power operated 90.5 hp electric motor) grain flour separator was designed and developed for separation of grain (wheat, corn, chickpea and soybean) flour into various fractions based on the size of the particles of the product. The separator agitating mechanism, feed control, cylindrical separator unit and an eccentric mechanism. The machine was tested for wheat ( variety ; Sujata) flour separation into four fractions, viz ; semolina, Gr-I and II, flour (coarse) and white (fine) flour. Wheat samples (6.8% m.c., db) were first pearled by CIAE pearler for 15.8% bran removal . The pearled wheat grains were then milled for semolina by a burre mill. The product and machine characteristics were determined at different capacities varying from 24 kg/h to 143 kg/h. It was found that 76 kg/h capacity gave reasonably best results in terms of purity and recovery of semolina vis-a-vis the market product. The energy requirement of the machine at no-load was found to be 230 W and at load c nditions, it varied between 36.3-6.4 KJ per kg of fead seperation. The macine could be used by small flour millers small/medium size traders and retailers and other processors for making available various flour products of different particle size in the market for ready use of the consumers.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2002년도 금형가공 심포지엄
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• pp.3-10
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• 2002

In this study on machine measuring system based on solid feature was developed. This system was applied with injection mold using 3 dimensional solid modeler for verification. Developed program include pre-processor, main processor, and post processor. In pre-processor there are functions which check intersection, simulate motion of probe and calculate measuring time. Main processor generates measuring path and output NC code in Unigraphics. In post-processor functions that include evaluation of undercut or overcut and display of measuring procedure are offered. In addition analysis module for quality control of measured data on manufactured product was developed with geometric and dimensional tolerance concept. As the result developed program could get stability of system, precision of product, rapidity and cost down of manufacturing process compared with before measuring process.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• 한국산업융합학회 논문집
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• 제27권1호
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• pp.71-76
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• 2024

Automatic production systems are constantly advancing technologies to improve productivity and safety. Specifically, liquid filling machines are primarily utilized to package products into drums after manufacturing process in the hazardous chemical industry. Most existing filling machines allow the operator to open the drum cap and inject the product directly or semi-automation. In this study, we have developed a cap opening and closing mechanism onto the existing drum filling machine, enabling automatic and safe cap manipulation while filling the product in the IBC tank. By applying the appropriate torque value through numerical analysis, we confirmed that the system worked without any problems during the process of opening and closing the cap. Therefore, it is expected that the developed machine will give more production and reduce human efforts without risk in the chemical packaging industry.

• 산업경영시스템학회지
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• 제44권3호
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.