This research presents a bamboo chopsticks machine with automatic control system. The production of bamboo chopsticks the current will be cut to get the machine to enter the bamboo chopsticks using the machine. The machine will hammer bamboo rotation of the electric motor. The AC motors with high power. Currently the production of chopsticks mostly small businesses. Lower costs which resulted in lack of effective security. This research presents the design and techniques with bamboo chopsticks production automation. The objective is to make the production of bamboo chopsticks are secure and compliant. To increase productivity and reduce the danger of the machine. In designing the Arduino technology is used in the development of electronic prototypes designed to be user for both hardware and software advanced application control equipment used in the production of chopsticks for safe to use on the incidence of danger from the use.

The objective of this research is a study to determine optimum forming condition of semi-finished bulk UHMWPE by using ram extruder that obtain maximum yield strength and minimum wear rate for apply to use in area of medical and mechanical application. Experimental extrusion specified forming condition consists of the temperature of 140 150 and 160 ºC and pressure of 3 5 8 and 10 MPa with the temperature exposure time of 30 minutes. After known optimum forming condition of UHMWPE powder thus forming UHMWPE filled with different kind of filler to study and determine optimum weight fraction of various types of filler that affect onto wear resistance property of UHMWPE composites. Result extruded condition at the temperature of 1 5 0 ºC under pressure of 5 MPa with exposure time of 30 minutes is an optimum extruded condition due to specimen shown a maximum yield strength ultimate tensile strength and elongation of 20.43 ± 1.21 MPa 28.33 ± 1.53 MPa and 285.60 ± 6.41 % respectively and obtain minimum wear rate of 3.02  10-9 ± 5.27  10-10 mm3/Nm. Its mechanical properties and wear resistance property higher than imported bulk UHMWPE by 23.3 % and 4.75 times respectively due to the formation of cross-linking are occurred in polymer chain and also its degree of crystallinity is high equal to 61.52%. For bulk UHMWPE IR-spectrum shown a variation peak of carbonyl group (C=O) are occurred that that it indicated the oxidation reaction at the end of polymer chain and tis degree of crystallinity is equal to 53.31% which result to decrease strength of polymer. In the case of UHMWPE filled with filler optimum weight fractions of various types of filler are as follows. 1. The filling of 0.3 wt. % Al2O3 particle size 0.05 μm shown yield strength ultimate tensile strength and elongation of 23.17 ± 3.35 MPa 34.78 ± 2.80 MPa and 229.00 ± 6.09% respectively that its mechanical properties was increased by 18.55% (increasing of ultimate tensile strength). For the wear rate is equal to 2.09  10-9 ± 3.39  10-10 mm3/Nm or its wear resistance was increased by 1.44 times in compared with initial bulk UHMWPE. 2. The filling of 0.4 wt. % Al2O3 particle size 0.3 μm shown yield strength ultimate tensile strength and elongation of 21.93 ± 0.51 MPa 25.40 ± 0.69 MPa and 267.67 ± 22.03% respectively that its mechanical properties was increased by 11.54% (increasing of ultimate tensile strength). For the wear rate is equal to 2.32  10-9 ± 4.78  10-10 mm3/Nm or its wear resistance was increased by 1.30 times in compared with initial bulk UHMWPE. ฉ 3 . The filling of 0.4 wt. % Al2O3 particle size 1 μm shown yield strength ultimate tensile strength and elongation of 23.93 ± 1.89 MPa 26.77 ± 1.20 MPa and 296.54 ± 7.51% respectively that its mechanical properties was increased by 5.83% (increasing of ultimate tensile strength). For the wear rate is equal to 2.14  10-9 ± 3.16  10-10 mm3/Nm or its wear resistance was increased by 1.41 times in compared with initial bulk UHMWPE 4 . The filling of 4 wt. % Al2O3 particle size 5 μm shown yield strength ultimate tensile strength and elongation of 22.57 ± 2.01 MPa 26.31 ± 3.11 MPa and 136.66 ± 11.67% respectively that its mechanical properties was increased by 7.68% (increasing of ultimate tensile strength). For the wear rate is equal to 2.37  10-9 ± 5.71  10-10 mm3/Nm or its wear resistance was increased by 1.27 times in compared with initial bulk UHMWPE

The old machine that is used to mix the fiber is an oval tank. Inside the tank is divided lengthwise into half and its paddle is flat and long like a file and it is installed on one side of the tank only so the flow of the fiber in the tank is not so good. There must be a person using a paddle to stir the fiber to flow inside the tank. Apart from that there was an issue that the fiber often sticks to the paddle because it is flat. The new machine is designed to have 8 circular saw blades sized Ø 10 inch to mix the leaf fiber so that it does not stick to the paddle. Apart from that the paddle is installed on both side of the tank increasing from the old machine that has only on one side in order to solve the problem about the flow of the fiber. One more issue is about bringing the fibber out from the tank. When the valve is opened the fiber cannot flow out easily because the bottom of the tank is flat. The researcher therefore design the tank to be adjustable so that it is easier to pour the fiber out from the tank. According to the experiment the boiled pineapple leaf blender can blend the boiled pineapple leaf with 30 kilograms water. The suitable speed of the blade is 500 times per minute and each blending process takes approximately 5 minutes. The time using for leaf blending also based on the paper usage e.g. for the smooth surface paper its production can follow the normal process but for the paper with rough texture the time using for fiber blending will be less.

Normally bast fibres were removed from pineapple web by wrapping the boiled pineapple leaf with nylon web and stamp on it. If using this method it will take 24 hours to stamp on 40 kilograms of boiled leaf in order to obtain clean pineapple web that is suitable for producing paper. Apart from that when pressing the fiber we need to get rid of the water as much as we can because the fiber will be weight before using to produce paper. If the water pressed out each time is not equal there may be an error about paper thickness. Moreover stamping on boiled leaf may be dangerous because the chemical substance used when boiling the leaf may burn the feet and cause wound. Currently there is a Pressing machine to separate water from pineapple fiber Rachadasak Supengcum 2010 to fix the problems mentioned above. However there is a weak point because the pressing tube is small so it can press only 1 kilogram of leaf per one time. Therefore the new machine is designed to have a pressing tube sized 30 centimeters wide and 30 centimeters long. The pressing period is 30 centimeters and the pressure for pressing is 70 bars. It takes 1 minute 15 second for one squeeze which contains 6 kilograms of pineapple leaf. It can decrease the time for pressing from the old machine by 6 times. In addition the machine is designed to have the open-close channel to bring the pressed fiber out from the machine more easily. According to the experiment to remove the bast fibres from pineapple web the most appropriate amount for each pressing is 6 kilograms. The pressing process takes 1 minute 15 second. The remaining pineapple web after the pressing is 3 kilograms

This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life machine type 6 worker group 3 and 4 working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimation