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

Admittedly a disc brake plays an important role in the auto part industry since it concerns directly with safety. Therefore the wide range of disc brake has been designed in order to develop its heat ventilation. Thus this project has designed a back-vented brake disc which is difference the current one (front-vented) and tried to create the test to find out the temperature diffusion of the disc brake with the hope to evaluate the coefficient of the heat convection varied by the temperature changing in both kinds of brakes under the forced heat convection in the steady condition. However the steady heat conduction and radiation value will not be counted because it is insignificant. The JASO C406: 2000 Standard is adopted as a guideline in the test. The outcome coefficient of heat conduction will be used in the simulation via the Finite Element Method (FEM) in order to study the temperature diffusion and heat ventilation of both kinds of brakes at each temperature level. Consequently the experimental work reveals then the heat convection coefficients of both kinds are unidirectional. The heat convection coefficient of the front-vented disc brake is 4.40 % in average higher than that of the back-vented disc brake. From the simulation the front-vented disc brake can ventilate the heat better than the back-vented disc brake at 3.02 % in average. It can be summarized that the front-vented disc brake has more temperature difference than the back-vented disc brake. Hence the front-vented disc brake likely gets more stress from the temperature than the back-vented disc brake and might be damaged during the operation more than the another type. Therefore this is tendency that the back-vented is safer than the front-vented brake disc for usage.

This paper describes an aluminum casting jewelry manufacturing because of the current manufacturing costs are higher. Material or metal that is commonly used to cast jewelry is silver which aluminum Alternatives to metal. Melting is There is a close color and surface finish can be beautiful. But aluminum is a material that is cheap has studied and tested. Replacement aluminum casting body jewelry. The temperature in the baking mold before casting to test all five temperatures are 550 500 450 400 350 degrees Celsius then casting body jewelry in the next stage of the research process casting jewelry with the. aluminum to Experimental temperatures used in baking mold casting plaster for the mold temperature of 400 is appropriate. Best of casting jewelry with aluminum. The results showed that there is minimal waste and a 102 pieces of 48 pieces and has a smooth surface. A similar performance to the other materials available. Similar properties

Nowadays the majority of environment problem is pollution of vehicle including the noise of engine combustion. For this reason this thesis aims to compare and study the parameters which affect noise reduction on exhaust pipe. Not only reduce the noise pollution but also maintain the efficiency of the engine. These two substances must be considered. Therefore the researcher emphasized to study the parameters on 3 types of muffler with an equal volume: direct reverse and offset type of mufflers. Initiated by data collection the pressure and the velocity in muffler at the speed of engine at 1000 RPM (slow-running engine mode) and 4000 RPM (maximum running engine mode) were investigated. The data were applied into the program that called “SolidWorks” in order to study parameters. In this step the relation between the pressure and velocity of exhaust were represented. In addition the muffler models were selected to manufacture and test by the standard of The Department of Land Transport. As a result there are two major parameters of the muffler. Firstly the metamorphism of the exhaust gas flow direction pleasurably causes the lower noise at the speed of engine at 1000 RPM. Besides an increase of the flow gas distance can diminish noise at the speed of engine at 4000 RPM as well. However this muffler models were tested under the working mode of catalytic device that is standard equipment for automotive vehicles.

This research proposes reliability analysis in order to find the probability that a machine will work properly the failure rates of a machine and the factors related to the failure rates. The data were collected from 8 types of machines in an autoparts manufacturing factory. They consist of the times measured from the first day of the study until the machine produces defective product workers and production steps. The study duration is 90 days. The results show that the probability that the machine type 4 will work properly more than days is 0.40 machine type 5 more than 68 days is 0.32 machine type 6 more than 72 days is 0.31 machine type 8 more than 85 days is 0.26 machine type 9 more than 109 days is 0.80 machine type 10 at more than 52 days is 0.38 machine type 11 more than 71 days is 0.32 and the probability that the machine type 12 will work properly more than 85 days is 0.26. The failure rate of the machine type 4 at day 51 is 6.61 machine type 5 at day 68 is 1.61 machine type 6 at day 72 is 0.77 machine type 8 at day 85 is 1.26 machine type 9 at day 109 is 1.31 machine type 10 at day 52 is 0.92 machine type 11 at day 71 is 1.24 and the failure rate of the machine type 12 at day 85 is 2.15. The factors influencing on the failure rates are worker group 2 and production step 3 at the significance of 0.05.