This research report describes the study of the development of finite element modeling for magnetoacoustic wave generation for non-destructive tomography. In the study the two-dimensional finite element model is used for the generation of magnetoacoustic wave in the mimic biological tissue model. The Gaussian pulse is applied as the excitation pulse in this research. The result of signal capturing at the boundary of mimic tissue model is used for the reconstruction cross-section image which is agreed to the original conductivity distribution.

Nowadays it is rapid technology advancement. The faculty of engineering Rajamangala University of Technology Phra Nakhon needs to update the bachelor degree programs and produces graduates that meet the requirements of the target markets. Work integrated learning education is one of the most effective education that is suitable for this situation and the future. This research is a study of faculty of engineering’s strategy base on work integrated learning for professional graduate production. The research results acknowledge the work integrated learning education type that is implemented in the faculty of engineering Rajamangala University of Technology Phra Nakhon. In addition the results can be applied to the modification of the engineering curriculum or the new engineering curriculum design.

Since the industries are developing to Industry 4.0 in the near future. Therefore a study of faculty of engineering Rajamangala University of Technology Phra Nakhon target market with Industry 4.0 for sustainability is the necessity. In order to collect the data build the knowledge about the target market in the Industry 4.0 era. Moreover a survey of the requirement of graduates’ characteristic that can support the revolution from Industry 3.0 to Industry 4.0 is very important. Therefore the faculty of engineering Rajamangala University of Technology Phra Nakhon can well prepare the graduates production for supporting the Industry 4.0 for sustainability.

Differential evolution is one of the most efficient optimization algorithms for solving complication problems including single objective multiobjective and many-objective optimization. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity robustness and high speed of convergence. The Advanced Fuzzy-based Multiobjective Differential Evolution (AFMDE) that exploits three performance metrics specifically hypervolume spacing and maximum spread to measure the state of the evolution process. The fuzzy inference rules are applied to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in AFMDE. The optimization algorithm will stop the evolution process if the number of iterations reaches the stopping criteria which usually is the maximum number of iterations. Then the optimization algorithm delivers the optimal solution founded. However sometimes if the maximum number of iterations is not appropriately defined the found solutions may not be the optimal ones. In case of the optimization algorithm has found the optimal solutions but it must continue the evolution process because the stopping criteria are not met. This can cause unnecessary using of high computational resources and time-consuming. Therefore this research study proposed the stopping criteria based on performance metrics feedback for AFMDE. The efficiency of the proposed criteria combined with AFMDE is evaluated on the well-known ZDT benchmark test suites.