Abstract
“Anti-Global Warming” is a hot topic around the world. A growing number of countries have participated in the research of alternative energies. Industrial Technology Research Institute of Taiwan (ITRT) has already developed the technology in solar energy selective absorption film continuous sputtering process. The institute established the first Roll-to-Roll continuous sputtering process machine in Taiwan based on the batch sputtering process technology. ITRT attempts to combine the technology and the industrial engineering knowledge to optimize manufacturing process parameter settings. For the extremely complicated solar energy selective absorption film continuous sputtering process, plenty of parameters would affect the output. If we only rely on engineer’s experience to determine the values, the defect rate may increase owing to the unstable manufacturing process. This study proposes a systematic procedure for parameter optimization of solar energy selective absorption film continuous sputtering process. First, historical data and engineering knowledge are used to determine the significant factors. Second, Taguchi methods are employed to find the optimal combination of parameters. Finally, back-propagation neural network (BPN), desirability function, and genetic algorithms (GAs) are utilized to obtain the optimal parameter level combination. The experimental results present that the proposed method can control the core of the problem efficiently. By simply employing 18 data and adjusting 7 factors, this research can enhance performance metrics to an international high quality standard. Our proposed method can restrain 27,553 tons of carbon dioxide every year with respect to beneficial energy conservation, which is 11 times less emissions than what the traditional paint process produces. From the perspective of industry, this result is considerably impressive.