||The thesis combines the optimum methods of Taguchi methods, Neural Network System and Genetic Algorithm (GA), and uses the Boundary Element Method to the optimum design of muffler by the software of acoustic analysis, “SYSNOISE”.
The thesis is composed of 2 parts: (1) The performance of noise cancellation mufflers: the route is separated into two or more, and converge the entire routes to reducing noise. The setting parameters of dimension included the diameter of the straight pipe (d1), the diameter of curve pipe (d2), the distance from curve pipe to straight pipe (L1), and the distance of center of curve pipe (L2). Using the 6cm of the diameter of the straight pipe (d1) to be a fundamental muffler to investigate the effects of muffler dimensions on the performance of noise cancellation mufflers. (2) Combining the software of acoustic analysis, “SYSNOISE” with the optimum methods of Taguchi methods, Neural Network System and GA, to the optimum design of dimension of the noise cancellation mufflers. Aiming the unit frequency at the noise cancellation mufflers and divide them into three groups including 350Hz, 500Hz and 650Hz as a goal of frequency. The data of input group and output group is created by using Neural Network System, and combining the GA to search the optimum dimension and sound transmission loss (STL) of the muffler. Final, taking the optimum dimension of Neural Network System and GA to get the relative value of STL by the software of acoustics analysis, “SYSNOISE”.
The result shows using the network mode of the noise cancellation mufflers that is created by Neural Network System can efficiently simplify the mathematical mode of aimed the unit frequency at noise cancellation mufflers. For the performance of noise cancellation mufflers, that’s also increasing after gaining the optimum dimension of GA. It shows using Neural Network System collocate the GA also have a good performance on the optimum design of dimension of noise mufflers of interference type. It can also save the time of research and design, accelerate the product research, decrease the rate of error, and especially save the cost of product design for the industrial application.