Search

This paper presents an optimization algorithm for tuning the interpolation parameters of computer numerical control (CNC) controllers; it operates by considering multiple objective functions, namely, contour errors, the machining time (MT), and vibrations. The position commands, position errors, and vibration signals from 1024 experiments were considered in the designed trajectory. The experimental data—the maximum contour error (MCoE), MT, and corner vibration (CVib)—were analyzed to compute the performance index. A backpropagation neural network (BPNN) with 20 hidden layers was applied to predict the performance index. The correlation coefficients for the predicted values and experimental results for the MCoE, MT, and CVib based on the validation data were 0.9984, 0.9998, and 0.9354, respectively. The high correlation coefficients highlight the accuracy of the model for designing the interpolation parameter. After the BPNN model was developed, a genetic algorithm (GA) was adopted to determine the optimized parameters of the interpolation under different weighting of the performance index. A weighted sum approach involving the objective function was employed to determine the optimized interpolation parameters in the GA. Thus, operators can judge the feasibility of the interpolation parameter for various weighting settings. Finally, a mixed path was selected to verify the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This research uses spiral microstructures to measure both the residual mean normal compressive/tensile stress and gradient stress of the thin films for MEMS devices. The relation between these two residual stresses and the resultant geometric or dimensional deformations is first established using the analytical solutions. Then based on this relation the deformations are measured and utilized to determine these two residual stresses. ANSYS simulation and experiments are conducted to verify the measurement. The deformations from ANSYS simulation for a microspiral subjected to the measured mean and gradient residual stresses agree well with those from experiments (less than 6% errors). Therefore, it can be concluded that the proposed measurement method is validated by the experimental results. [Copyright &y& Elsevier]

Abstract: Micro-injection molding, with advantages of easy mass production and low cost, is a key technology for producing micro components. Nevertheless, a low yield rate of high-quality molded parts is common due to problems associated with geometric precision, molecular orientation, and optical properties. Solutions to such problems must consider the machine, mold design, and process parameter settings. However, optimal performance becomes relatively less attainable when process parameters deviate due to inevitable process tolerances and change in an operation environment. This study has two goals: (1) fabrication of high-precision mold inserts using UV-LIGA; and, (2) identification of robust parameters that ensure production quality. Two gear mold-inserts with outside diameters of approximate 6mm and 4mm, named as the 6mm and 4mm gears, are introduced. First, lithography of thick photoresist SU8-50 is utilized as the initial structure seed layer to electroform the Ni gear mold insert. Second, this study investigates two key geometrical dimensions-the outside diameter and tooth thickness of the molded gears. The robust optimization of multiple objectives is introduced to identify robust parameter settings with high dimensional accuracy and high yield rates for molded gears despite process parameter deviations. Experimental results indicate that electroformed Ni mold inserts have good quality and are successfully utilized in the subsequent micro-injection molding process, demonstrating the feasibility of the mold inserts fabricated using UV-LIGA. The micro-injection molding experiments suggest that mold temperature, holding pressure, and injection speed have significant effects on dimensional quality characteristics of molded gears. The robust parameters derived from the proposed method increase yield rates of the 6mm gear from 10% to 91%, and those of the 4mm gear from 38% to 93% in comparison with the initial point, thereby demonstrating application effectiveness. [Copyright &y& Elsevier]

In this research, a new developed optimization method for light guide plate design is proposed in order to achieve its exiting-light uniformity by varying the light-scattering microstructure sizes or pitches at different areas of a liquid crystal display (LCD) light guide plate. The light-scattering hemispheric microstructures on a light guide plate are studied in this paper due to less light absorption compared to the conventional V-cut pattern design. The results show success in uniformity. To validate the effectiveness of this design method on the improvement of exiting-light uniformity, the “advance system analysis program” (ASAP) simulation is performed. Based on the proposed design method, it is illustrated that the exiting-light uniformity significantly improves by more than 13% for the case study. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization method for designing a prism-pattern LCD light guide plate (LGP) using a neural-network optical model and a real-valued genetic algorithm to achieve excellent luminance uniformity in the exiting light. This newly developed method is proposed as a way of solving the complicated optimization work for non-image optics due to the numbers of ray tracing. First, a neural-network optical model is based on a back-propagation neural network. Then the neural-network optical model is incorporated into a real-valued genetic algorithm to optimize the distribution density of the prism pattern. The results show that the 13-point luminance uniformity reaches an outstanding 92.09%.