This study aimed to use Poka-Yoke techniques to help prevent errors and reduce loss in Front Side Fender Model assembly process. The researchers used four tools in the 7QC tools which were Check Sheet Cause-and-Effect Diagram and Graphs to search for causes and ways to solve problems and Cause and Effect Matrix and ECRS Techniques. The study revealed that the main cause of the problems was that the incorrect assembled side fenders were submitted to another department. This mistake had bad effects on time and workforce spent for correcting work including more expenses. The mistakes occurred because there was no tool for detecting errors in the work piece. Therefore the researchers use Poka-Yoke techniques in order to decrease errors by designing a control system for target detection while it was moving through the process. After collecting data the study showed that before the improvement during January to August 2019 the number of problems caused by the process were 0.093. However the number of problems after the improvement during September to December 2019 reduced to 0. It was obvious that this improvement reduced the loss from the front side fender assembly process up to 100.

Nowadays the automotive parts industry greatly grows up in Thailand. Brake disc is one of products that play a role in safety area. As a result the various designs such as front and back-vented brake discs are occurred to improve the performance of usability. Consequently this study aims to develop the ability of braking dynamometer from 9.7 kg-m.1 kg-m. Moreover the influence of the front and the back-vented brake disc effecting on braking distance was investigated by single dynamometer referenced JASO C406 for testing condition. Therefore both of discs were operated under 100 200 and 300 °C of braking temperature and 2 4 and 6 MPa of line hydraulic pressure of braking. 2 to 1272 of moment of inertial From experimental works the test results reflected that the back-vented brake disc created braking distance shorter than front-vented brake disc ostensibly. Besides there are also diversity of factors as temperature pressure and initial speed of braking. Especially the higher temperature caused an increase of deceleration and a decrease of braking distance. Moreover the higher pressure produced a deduction of braking distance and a rise of deceleration. Finally the initial speed of braking was directly various the braking distances in same condition.

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

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.