The three-dimensional tube (or pipe) is manufactured by CNC tube bending machine. The key techniques are determined by tube diameter, wall thickness, material, and bending radius. The obtained technique through experience and the trial and error method is unreliable. Finite element method (FEM) simulation for the tube bending process before production can avoid wasting manpower and raw materials. The computer-aided engineering (CAE) software ABAQUS 6.12 is applied to simulate bending characteristics and to explore the maximum stress and strain conditions. The Taguchi method is used to find the optimal parameters of bending. The confirmation experiment is performed according to optimal parameters. Results indicate that the strain error between CAE simulation and bending experiments is within 6.39%.
The unibody of LED (light-emitting diodes) lampshades is fabricated by injection mold; the forming technique is complicated, especially for multi-cavity molds. This study applies a finite element analysis to explore the influences of the shrinkage of LED lampshades. The effect of selected injection parameters and their levels on shrinkage size, and the subsequent design of experiments were accomplished using the Taguchi method. The results were confirmed by experiments, which indicated that the selected injection parameters effectively reduce the shrinkage. The error between optimal estimated value and verified value is within 3.82%.
This study analyzes a variety of significant drilling conditions on aluminum oxide (withL18orthogonal array) using a diamond drill. The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size. An orthogonal array, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to analyze the effects of these drilling parameters. The results were confirmed by experiments, which indicated that the selected drilling parameters effectively reduce the crack. The neural network is applied to establish a model based on the relationship between input parameters (spindle speed, feed rate, depth of cut, and diamond abrasive size) and output parameter (cracking area percentage). The neural network can predict individual crack in terms of input parameters, which provides faster and more automated model synthesis. Accurate prediction of crack ensures that poor drilling parameters are not suitable for machining products, avoiding the fabrication of poor-quality products. Confirmation experiments showed that neural network precisely predicted the cracking area percentage in drilling of alumina.
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