The success of a tube hydroforming (THF) process is highly dependent on the loading paths (axial feed versus pressure) used. Finite element (FE)-based simulation was used to determine optimum loading paths for hydroforming of structural parts with different tubular materials. Experimental and simulation results have demonstrated that FE-based loading paths can significantly reduce trial and error, enhance productivity and expand the THF capability in forming complex parts. The test results also demonstrated that the reliability of the FE-based loading paths is highly dependent on the accuracy of the material properties of the blank, interface friction, and how close the properties of the welding zone are to the base material of the tubular blank.

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Micromilling is a straightforward approach to the manufacture of polymer microfluidic devices for applications in chemistry and biology. This fabrication process reduces costs, provides a relatively simple user interface, and enables the fabrication of complex structures, which makes it ideal for the development of prototypes. In this study, we investigated the influence of micromilling parameters on the surface roughness of a cyclic olefin copolymer (COC) substrate. We then employed factor analysis to determine the optimal cutting conditions. The parameters used in all experiments were the spindle speed, the feed rate, and the depth of cut. Roughness was measured using a stylus profilometer. The lowest roughness was 0.173 μm at a spindle speed of 20,000 rpm, feed rate of 300 mm/min, and cut depth of 20 μm. Factor analysis revealed that the feed rate has the greatest impact on surface quality, whereas the depth of cut has the least impact.

a b s t r a c tIn this study, T-Shape friction test was redesigned to make it more suitable for application to microforming processes. Workpiece with aspect ratio (length/diameter) of 5 was proposed in order to ease workpiece handling. The die geometry was also modified from the original test to improve friction sensitivity especially within the range of friction factors commonly observed in metal forming. Geometric deviation of the die was simulated using Deform-2D to establish the acceptable tolerance for the fabrication. The effect of variation in workpiece mechanical properties on the test behavior was also investigated through Deform-2D simulation. Based on simulations on a 1 mm diameter copper workpiece, a tolerance of 0.01 mm (1% of workpiece diameter) was found to be the most suitable for the die fabrication. In addition, it was shown that variations in workpiece mechanical properties of up to 10% do not significantly influence the friction test results. Ultimately, T-Shape test experiment was conducted using copper workpieces to examine how the test complied with the friction behavior observed in the experiment.

Successful fuel filler tube hydroforming largely depends on proper loading paths, that is, application of internal pressure and axial feeding during the forming time duration. Generally, two part quality criteria are considered in selecting the feasible loading paths: (a) minimum part wall thinning and (b) part wrinkle free. Due to the highly nonlinear nature of the tube hydroforming process, iterative finite element analyses with adjustments based on forming experience are typically conducted to design the loading paths. In this research, genetic algorithm was integrated into the finite element analysis–based optimization, resulting in enhanced determination of the feasible loading paths. Genetic algorithm is a heuristic search based on mechanics of natural selection. A pair of pressure and axial feeding profiles was represented by connecting genes making up to be a chromosome. In each search, mutation and crossover operations generated a new generation of chromosomes. Fitness functions were formulated to assess performance of the chromosomes reflecting the part quality. Generations after generations, the optimal chromosomes are found only when the evaluated fitness function value falls within a user-defined tolerance. Unlike the typical iterative finite element analysis approach, it was shown that the iterative finite element analysis augmented with genetic algorithm was able to determine feasible pressure and axial feeding paths autonomously. The current approach still requires a lot of simulation runs, which must be offset by high-performance computing resources.