Combining Taguchi experimental design and fuzzy theory, this research aims to develop a multi-objective algorithm to determine the optimal design parameters for the driving springs in a slide cover hinge. The goal is to determine parameters that will meet the two basic requirements of the spring that would generate adequate reaction force but minimize maximum stress. This research applies finite element method to calculate the reaction force and maximum stress of a deformed spring. Meanwhile, the Taguchi method, which could dramatically reduce the number of experiments, is used to determine the optimal design parameters for the spring. Experimental results suggest that if the maximum stress is minimized, the reaction force will decrease, resulting in the autonomous release of the slide cover hinge; if adequate reaction force is achieved, the maximum stress would exceed the design standard. Therefore, this research employs fuzzy theory to achieve multi-objective as "minimization of maximum stress" and "sufficient reaction force", as a result of an optimal design for driving springs in a slide cover hinge.

A computer-assisted preoperative simulating system for calcaneal collapse's internal fixation surgery by integrating different CAD software's function has been developed in this paper. This system uses the full-scaled 3D reverse engineering technique in designing and developing preoperative simulation modules for the calcaneal internal fixation surgery. The simulating system presents a real-sized 3D image of the calcaneus, and provides detailed interior measurements of the calcaneus from various cutting planes. This study applied computer-assisted technology to integrate different software's function to a surgical simulation system, which includes a 3-D image model capturing, cutting, moving, rotating and measurement for relevant foot anatomy. Surgeons can utilize it as part of preoperative simulation to develop efficient operative procedures. This system also has an extendable databank which provides the clinical cases to different users for experience learning.