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A Solid Freeform Fabrication (SFF) system using Selective Laser Sintering (SLS) is currently recognized as a leading process and SLS extends the applications to machinery and automobiles due to the various materials employed. Especially, accuracy and processing time are very important factors when the desired shape is fabricated with Selective Laser Sintering (SLS), one of Solid Freeform Fabrication (SFF) system. In the convectional SLS process, laser spot size is fixed during laser exposing on the sliced figure. Therefore, it is difficult to accuracy and rapidly fabricates the desired shape. In this paper, to deal with those problems a SFF system having ability of changing spot size is developed. The system provides high accuracy and optimal processing time. Specifically, a variable beam expander is employed to adjust spot size for different figures on a sliced shape. Therefore, design and performance estimation of the SFF system employing a variable beam expander are achieved and the mechanism will be addressed to measure the real spot size generated from the variable beam expander. Also, the reduction of total processing time is an important issue in SFF system. A digital mirror system (DMS) is a system which scans the laser beam with different spot size. The spot size is selected based on the slicing section to decrease and accuracy of the process time and improve the processing efficiency. In this study, the optimal scan path generation for DMS will be addressed, and this development will improve the whole processing efficiency and accuracy through the scan efficiency by considering the existing scan path algorithm and heat energy distribution.

Analyzing eye movement data to evaluate learning status has become crucial in intelligent education. The eye movement scanning path can directly or indirectly reflect changes in thinking patterns and psychological states. By analyzing the scanning path, we can explore the commonality and differences in learners’ eye movement behaviors and provide essential references for improving visual content and giving guidance. This paper first studies the time series representation and clustering of the learner’s scanning path under the same task. Then, the three learning states of concentration, mind-wandering, and information wandering are evaluated through the clustering results. Specifically, the improved DBA algorithm (iDBA) is proposed to extract group eye movement patterns, combined with the dynamic time warping (DTW) algorithm to calculate the similarity of scanning paths and determine the clustering seeds, while the distance density clustering (DDC) algorithm is used for clustering. Experiments show that time series-based eye movement pattern mining can identify group viewing behaviors. Meanwhile, clustering reveals different reading strategies and provides the ability to assess learning status.