Korean melon (Cucumis melo L.) is an environment in which most farming work can affect the increase in musculoskeletal diseases, and the stems are attracted to the ground in order to grow no-heating cultivation. In this study, growth and productivity were compared according to the type of high-bed. The narrower the surface area at the bottom of the high-bed, the faster the initial growth, which was advantageous. The bed is which the height if 70 cm, the surface temperature has risen due to the increase in direct solar radiation inflow since April, requiring side light blocking to block the inflow of solar radiation. In terms of fruit quality, the 200 cm width treatment had higher fruit sugar content and better hardness than the 160 cm treatment. From April to September, the total yield was 6.8 kg/plant of treatment A, 8.7 kg/plant of treatment B, 5.8 kg/plant of treatment C, treatment B mainly 50% higher than treatment C, and 27% higher than treatment A. Therefore, the bed form suitable for Korean melon high bed is 200 cm wide, 40 cm high between the surface and the bed, and the surface of the passage between the beds is 30cm high from the ground to the bed.

In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

Recently 3d printer industry has two demands. first is color 3d printing. second is mass production using 3d printer that has large bed. According to previous studies, 3D printed objects have different weights depending on filament colors. 3D printed tensile specimens with filaments of various colors were checked to see they had the same weight. If so, we wanted to see it was statistically significant. As a result, we found that the weight of 3D printed objects was statistically significantly different depending on the filament color. The average weight of 3d printed objects is: Black(8.63g), Blue(8.58g), Yellow(8.53g), White(8.48g), Natural(8.46g), Green (8.45g), Red(8.42g).

The SLA 3d printer is the first of the commercial 3D printer. The 3D printed output is printed hanging on the bed that move to the upper position. Sandblasted bed is used to prevent layer shift. If sandblasting is wrong, the 3D printed output is layer shifted. For this reason, 3D printer manufacturing companies inspect the bed surface. However, the sandblasted surface has variety of irregular shapes and craters, so it is difficult to establish a quality control standard. To solve problems, this paper presents a standardized sandblasting histogram and threshold. We present a filter that can increase the classification rate.

본 연구는 딸기 온실 내부의 방대한 환경인자를 활용하여 판별분석을 실시하고 딸기의 재배 베드 단수에 따른 온실 내부의 환경인자를 분석함으로 써, 딸기 분야에서 계측된 데이터의 활용성을 높이기 위한 기초자료로 활용할 목적으로 수행하였다. 그 결과는 다음과 같다. 환경인자별(온도, 습도, 이산화탄소 농도, EC 및 pH) 동질성 검정의 유의확률은 각각 0.0001, 8.2788E-38, 4.3310E-85, 1.3001E-16 및 0.0001로서 설정한 유의수준 0.05보다 낮게 나타났다. 그리고 분석결과 판별함수식은 F(x)1 = –60.5664 -0.1339×Temperature –0.0087×Humidity +0.0018×CO2 +0.1014×EC +8.3860×pH, F(x)2 = –12.4928 +0.1963×Temperature –0.0024×Humidity +0.0254×CO2 –0.0187×EC –0.3651×pH로 도출되었다. 판별함수식의 정확도는 대상 온실 A (81.1%) 및 B (96.1%)보다 대상 온실 C (100.0%)에서 높은 것으로 나타났다. 예측 가능한 대상 온실별(A, B 및 C) 분류함수는 각각 – 1836.7035 – 2.8733×Temperature + 0.1355×Humidity + 0.4186×CO2 + 7.4351×EC + 484.5901×pH, – 1710.8369 – 2.7701×Temperature + 0.1550×Humidity + 0.3937×CO2 + 7.2482×EC + 468.1477×pH, – 2291.7125 - 3.9756×Temperature + 0.0723× Humidity + 0.4177×CO2 + 8.1961×EC + 546.8476×pH로 나타났다. 특히 판별함수식을 근거로 환경인자별 새로운 측정값이나 자료를 입력하였을 때, 특정 그룹으로 분류가 가능함으로써 데이터의 특징을 파악할 수 있다. 이러한 환경인자는 식별을 용이하게 함으로써 환경인자 측정값의 활용도를 높여주는 방법이라고 판단된다.

Fused Deposition Modeling (FDM), also known as Fused Deposition Modeling (FFF), is the most widely used type of 3D printing at the consumer level. The FDM 3D printer extracts thermoplastic filaments such as ABS (Acrylonitrile Butadiene Styrene) and Polyactive Acid (PLA) through heated nozzles to dissolve the material. It works by applying layers of plastic to build platforms. Various demands for 3D printers increased, and among these demands, there was also a demand for various filament colors. ABS, one of the main filamentous materials for 3D printers, is easy to color in a variety of colors and has been studied to meet the needs of these users. Through quantitative measurements in this work, we confirm that color differences remain depending on the difference in placement on the 3D printer bad. In addition, the temperature of the specimen was measured at the start of 3D printing, during manufacturing, and at the completion of manufacturing, and the inner and central sides remained similar, but the outer sides were 5 degrees lower. These temperature differences accumulate as layers pile up, resulting in differences in weight or color, which in turn meet consumer and producer needs in the 3D printer industry.

FDM 3D Printer is used in maker space for mass production by the maker. Makers desire to manufacture products in a variety of colors using ABS Filaments. The purpose of this study is to identify the relation between the color of resin and each position on the bed. So, when printed using the 3D Printer, we found out the difference in the colors that it appears depending on the position of the bed. To see the difference in color, basic, blue, yellow, white, and black were selected and the bed plate was divided into three sections. Specimens were measured to obtain delta E values between each sections by the chromatic differential system. Obtained delta values were analyzed by the NBS system. As a result, the delta E value of black was found to correspond to “Appreciable”. In most cases, delta E values between the middle and the outer or the inner and the outer was greater than values between the middle and the inner. Using Infrared Thermal Camera, We found that the color difference relates 3D printing positions and temperatures. As a result, the 3D printing bed positions should be considered when 3D printing mass production.

In the agriculture of strawberry cultivation, it is necessary to loosening and breaking up of the intertwined coco peat and strawberry root in order to cultivate strawberry again. The main objective of this study is to design and analyze the structure of strawberry rotary device for using breaking up of coco peat and strawberry root. In order to perform crushing coco peat on the bed, the rotary device was designed under the weigh 20kg with the speed 11.75m/min, and it can operate on bed width from 250~310mm. Due to different depth of bed, the body of device also was designed screw holder to adjust the hight from 0~120mm. To evaluate the safety of structure, body device was analyzed in static and free vibration state by using Abaqus program. The device was applied maximum load 1177.2N, and the maximum equivalent stress was reached 41.9MPa. The free vibration analysis of two rotating cutter showed minimum natural frequency mode 338.58Hz and 339.9Hz. The results indicate that the designed rotary device was satisfied and has enough strength under design and simulation conditions.

본 연구의 목적은 2단 베드 벤치 시스템에서 딸기를 재배하는 동안 상단베드에 의한 차광으로 광량 부족한 하단 베드에서 자란 딸기의 생산량 및 과일 품질에 LED 보광의 영향을 확인하기 위한 것이다. 딸기 전용상 토로 충진된 2단 베드 벤치에 2015년 10월부터 2016년 1월까지 점적 관수로 딸기를 재배하였다. LED 광이 처리되지 않은 상단과 하단 베드를 대조구로 이용하였고, LED 광 처리를 위해서 오전 10시부터 오후 4시까지 하 단 베드에 각각 청색, 적색, 그리고 청색과 적색을 혼합 한 LED 광을 100μmol·m-2·s-1의 광량으로 보광 하였다. 딸기의 수확량에 있어서, 하단 베드의 청색 LED 보광된 처리에서 자란 딸기는 하단 부분 대조구와 비교하여 유의하게 증가되었으며, 상단 베드 대조구에서 자란 딸기 생산량의 90% 수준까지 증가되었다. 청색 및 혼합 LED와 상단베드에서 생육된 딸기 과일의 유리당 함량은 적색 LED와 하단 베드 부위 대조구에 비하여 높았다. 안토시아닌의 함량은 자연 광을 많이 받는 상단 베드에서 생육된 딸기 과일이 가장 높았지만, 하단베드 처리만을 비교하할 때, LED를 보광한 모든 딸기과일이 보광하지 않은 하단 부분의 대구조의 딸기 과일보다 높았다. 따라서 딸기 2단 베드 재배 시 하단 베드에 청색 LED 보광이 생산 증대 및 품질 향상에 유리할 것으로 판단된다.

This study aimed to develop the movable bed cultivating system for strawberry cultivation that enables to increase crop yield and save labor through high-density cultivation. In comparison with a conventional raised-bed system, the best feature of the system is a culture bed moving freely, left, right, up and down. This system consists of four devices; longitudinally- and laterally-moving device, nutrient solution supply device, and a control device. A comparative study showed that there was no significant difference on strawberry growth between a conventional raised bed and the movable bed cultivation systems. In addition, the change of growing environment to high-density cultivation did not influence strawberry growth, which proved the applicability of the movable bed cultivation system. Additional finding of this study showed that about 1.7 times more plants can be planted in movable beds. High-density cultivating using movable beds enables to plant more strawberry within the same cultivated area, which eventually contribute to reduce energy consumption per strawberry.