• Journal of Bio-Environment Control
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• v.22 no.3
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• pp.284-290
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• 2013

This study analyzed the geometric, compressive, cutting and friction properties of oriental melons in order to design a gripper capable of soft handling and a cutter for cutting oriental melon vine among the end effector of oriental melon as a preliminary step for developing the end effector of the robot capable of harvesting oriental melons in protected cultivation. As a result, the average length, diameter at the midpoint, weight, volume and roundness of the oriental melons were 108 mm, 70 mm, 188 g, 333 mL and 3.8 mm. Nonlinear regression analysis was performed on the equation $W=L^a{\times}D_2^b$ with variation of the length (L) and diameter (D2) of the weight (W) of the oriental melons. As a result, it was shown that there was a correlation between a of 2.0279 and b of -0.9998 as a constant value. The average diameter of the oriental melon vine was 3.8 mm, and most vines were distributed within a radius of 5 mm from the center. The average yield value, compressive strength and hardness of the oriental melons were $36.5N/cm^2$, $185.7N/cm^2$ and $636.7N/cm^2$, respectively. The average cutting force and shear strength of the oriental melon vines were $2.87{\times}10^{-2}\;N$ and $5.60N/cm^2$, respectively. The maximum friction coefficient of the oriental melons was rubber of 0.609, followed by aluminium of 0.393, stainless steel of 0.177 and teflon of 0.079. It was considered possible to apply it to the size of the gripper and cutter, turning radius, dynamics of drive motor and selection of materials and their quality in light of the position error and safety factor according to the movement when designing end effector based on the analyzed data.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.62-68
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• 2016

Purpose In a cyclosporine experiment using a robotic liquid handing system has found a deviation of its standard curve and low reproducibility of patients's results. The difference of the test is that methanol is mixed with samples and the extractions are used for the test. Therefore, we assumed that the abnormal test results came from using methanol and conducted this test. In a manual of a robotic liquid handling system mentions that we can choose several setting parameters depending on the viscosity of the liquids being used, the size of the sampling tips and the motor speeds that you elect to use but there's no exact order. This study was undertaken to confirm pipetting ability depending on types of liquids and investigate proper setting parameters for the optimum dispensing ability. Materials and Methods 4types of liquids(water, serum, methanol, PEG 6000(25%)) and $TSH^{125}I$ tracer(515 kBq) are used to confirm pipetting ability. 29 specimens for Cyclosporine test are used to compare results. Prepare 8 plastic tubes for each of the liquids and with multi pipette $400{\mu}l$ of each liquid is dispensed to 8 tubes and $100{\mu}l$ of $TSH^{125}I$ tracer are dispensed to all of the tubes. From the prepared samples, $100{\mu}l$ of liquids are dispensed using a robotic liquid handing system, counted and calculated its CV(%) depending on types of liquids. And then by adjusting several setting parameters(air gap, dispense time, delay time) the change of the CV(%)are calcutated and finds optimum setting parameters. 29 specimens are tested with 3 methods. The first(A) is manual method and the second(B) is used robotic liquid handling system with existing parameters. The third(C) is used robotic liquid handling system with adjusted parameters. Pipetting ability depending on types of liquids is assessed with CV(%). On the basis of (A), patients's test results are compared (A)and(B), (A)and(C) and they are assessed with %RE(%Relative error) and %Diff(%Difference). Results The CV(%) of the CPM depending on liquid types were water 0.88, serum 0.95, methanol 10.22 and PEG 0.68. As expected dispensing of methanol using a liquid handling system was the problem and others were good. The methanol's dispensing were conducted by adjusting several setting parameters. When transport air gap 0 was adjusted to 2 and 5, CV(%) were 20.16, 12.54 and when system air gap 0 was adjusted to 2 and 5, CV(%) were 8.94, 1.36. When adjusted to system air gap 2, transport air gap 2 was 12.96 and adjusted to system air gap 5, Transport air gap 5 was 1.33. When dispense speed was adjusted 300 to 100, CV(%) was 13.32 and when dispense delay was adjusted 200 to 100 was 13.55. When compared (B) to (A), the result increased 99.44% and %RE was 93.59%. When compared (C-system air gap was adjusted 0 to 5) to (A), the result increased 6.75% and %RE was 5.10%. Conclusion Adjusting speed and delay time of aspiration and dispense was meaningless but changing system air gap was effective. By adjusting several parameters proper value was found and it affected the practical result of the experiment. To optimize the system active efforts are needed through the test and in case of dispensing new types of liquids proper test is required to check the liquid is suitable for using the equipment.