• 한국분말재료학회지
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• 제28권3호
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• pp.208-215
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• 2021

Metal three-dimensional (3D) printing is an important emerging processing method in powder metallurgy. There are many successful applications of additive manufacturing. However, processing parameters such as laser power and scan speed must be manually optimized despite the development of artificial intelligence. Automatic calibration using information in an additive manufacturing database is desirable. In this study, 15 commercial pure titanium samples are processed under different conditions, and the 3D pore structures are characterized by X-ray tomography. These samples are easily classified into three categories, unmelted, well melted, or overmelted, depending on the laser energy density. Using more than 10,000 projected images for each category, convolutional neural networks are applied, and almost perfect classification of these samples is obtained. This result demonstrates that machine learning methods based on X-ray tomography can be helpful to automatically identify more suitable processing parameters.

• 한국인쇄학회지
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• 제23권2호
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• pp.25-43
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• 2005

Generally, machine calendering are used change of paper surface properties. During machine calendering, dry paper passes between the rolls under pressure, thereby improving the surface smoothness and gloss. These improvements make the paper better suited for printing and decreasing problems during the printing, such as delayed dry and set-off. Then we investigated newspaper properties by the changing of machine calendering condition, and relationship printability. Properties of each samples were examined in accordance with KS and TAPPI standard test method i.e, basic weight, bulk density, thickness, porosity, opacity, brightness, smoothness and roughness. IGT printability tester was used to obtain ink requirement of newspaper, printed density and set-off. Results of in this study, we have proposed the optimizes range of newspaper calendering condition. Useful optimize calendering condition was pressure 55 kN/m, temperature $130^{\circ}C$.

• 한국분말재료학회지
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• 제31권2호
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• pp.137-145
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• 2024

In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.

• 실천공학교육논문지
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• 제15권2호
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• pp.419-427
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• 2023

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2007년도 동계학술발표대회 논문집
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• pp.203-208
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• 2007

Drying characteristics are confirmed by experiment to a printing machine which use Gravure ink or metal ink for an optimum design of direct radiation drying system room using NIR. As a result, Drying is easily accomplished in short distance and low moving speed in Gravure ink, but drying is dropped in metal ink because of oil. This confirmed that the development of water metal ink had to be proceeded to accomplish a perfect drying condition.

• 한국재료학회:학술대회논문집
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• 한국재료학회 2009년도 춘계학술발표대회
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• pp.15.2-15.2
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• 2009

Manufacturing of printed electronics using printing technology has begun to get into the hot issue in many ways due to the low cost effectiveness to existing semi-conductor process. This technology with both low cost and high productivity, can be applied in the production of organic thin film transistor (OTFT), solar cell, radio frequency identification (RFID) tag, printed battery, E-paper, touch screen panel, black matrix for liquid crystal display (LCD), flexible display, and so forth. The emerging technology to manufacture the products in mass production is roll-to-roll printing technology which is a manufacturing method by printings of multi-layered patterns composed of semi-conductive, dielectric and conductive layers. In contrary to the conventional printing machines in which printing precision is about $50~100{\mu}m$, the printing machines for printed electronics should have a precision under $30{\mu}m$. In general, in order to implement printed electronics, narrow width and gap printing, register of multi-layer printing by several printing units, and printing accuracy of under $30{\mu}m$ are all required. We developed the roll-to-roll printing equipment used for printed electronics, which is composed of un-winder, re-winder, tension measurement system, feeding units, dancer systems, guide unit, printing unit, vision system, dryer units, and various auxiliary devices. The equipment is designed based on cantilever type in which all rollers except printing ones have cantilever types, which could give more accurate machine precision as well as convenience for changing rollers and observing the process.

• 산업경영시스템학회지
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• 제46권1호
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• 펄프종이기술
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• 제37권4호통권112호
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• pp.52-59
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• 2005

Most of printing paper market today have been occupied by western paper and some machine-made Hanji used don't show the characteristic advantages for traditional hand-made Hanji. Although hand-made Hanji has an aesthetic and qualitative value, it has limited uses because of poor printability as printing paper. Unlike western paper, conventional Hanji cannot be used as Printing paper because it is impossible to make the clear formation of printed dot and to develop a high level of sizing and picking problem occurs during printing. Improvements of physical and optical properties such as roughness, smoothness, air permeability, contact angle, opacity, and paper gloss as well as sizing level were accomplished through internal and surface sizing and calendering, which made the paper better suited for printing and decreased problems during printing.

• 한국정밀공학회지
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• 제27권9호
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• pp.16-19
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• 2010

This paper presents how to design and fabricate the gravure offset printing system for enhancement of register precision. Factors of precision error are caused by imprecision of gravure plate, deformation of substrate, printing quality change due to the change of ink viscosity, Imprecision of printing machine, and so on. This study suggests concept design of gravure offset printing system which is able to minimize or remove these error factors.

• 소성∙가공
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• 제31권2호
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• pp.89-95
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• 2022

3D printing products manufactured by material extrusion are used in many industrial fields recently. However, these products are difficult to use in the field due to their low tensile strengths. In order to solve this problem, research on improving the tensile strength of the output using a 3D printer has been continuously conducted. In this study, we performed a tensile test using Universal Testing Machine according to infill pattern, nozzle temperature, bed temperature, and printing speed conditions. Results revealed that tensile specimen of concentric shape had the highest tensile strength in infill pattern condition and that the tensile strength increased linearly with increasing nozzle and bed temperatures. However, the tensile strength decreased with increasing printing speed. Consequently, we confirmed that tensile strength could be increased and decreased depending on output conditions of 3D printing.