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Research Papers

J. Manuf. Sci. Eng. 2018;140(8):081001-081001-8. doi:10.1115/1.4039652.

Parts made via polymer extrusion are currently limited to a constant cross section. Additionally, the process is difficult to control, so desired final part dimensions are often achieved via a manual trial-and-error approach to parameter adjustment. This work seeks to increase the capability of polymer extrusion by using iterative learning control (ILC) to regulate the final width of a rectangular part through changing the width of a simple variable-geometry die. Simulation results determine the appropriateness of the learning algorithm and gains to be used in experiment. A prototype die on a production extruder was used to demonstrate the effectiveness of the approach. These experiments achieved automated control over both gross change in shape and final part dimension when the puller speed was held constant, which has not been seen previously in the literature.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(8):081002-081002-12. doi:10.1115/1.4039856.

In this paper, a novel fixture mechanism with combining a mobility of the legged robot and advantages of parallel mechanism is designed to hold the different size and shape, large-scale workpiece. The proposed mobile fixture mechanism holds the workpiece as a parallel manipulator while it walks as a legged robot. This kind of robotized fixtures can possess high self-configurable ability to accommodate a wider variety of products. In order to obtain the best kinematic dexterity and accuracy characteristics, comprehensive performance optimization is performed by non-dominated-genetic algorithm (NSGA-II). In the optimization procedure, a conventional kinematic transformation matrix (Jacobian matrix) and error propagation matrix are obtained through derivation and differential motion operations. The singular values and condition number based on velocity Jacobians and error amplification factors based on error propagation matrix are derived; in addition, relative pose error range of end effector is also derived. On the basis of the above measure indices, three kinds of nonlinear optimization problems are defined to obtain the optimal architecture parameters for better kinematic accuracy and dexterity in workspace. Comparison analyses of the optimized results are performed.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(8):081003-081003-8. doi:10.1115/1.4039979.

Carbon fiber reinforced composites have received growing attention because of their superior performance and high potential for lightweight systems. An economic method to manufacture the parts made of these composites is a sequence of forming followed by a compression molding. The first step in this sequence is called preforming that forms the prepreg, which is the fabric impregnated with the uncured resin, to the product geometry, while the molding process cures the resin. Slip between different prepreg layers is observed in the preforming step, and it is believed to have a non-negligible impact on the resulting geometry. This paper reports a method to characterize the interaction between different prepreg layers, which should be valuable for future predictive modeling and design optimization. An experimental device was built to evaluate the interactions with respect to various industrial production conditions. The experimental results were analyzed for an in-depth understanding about how temperature, relative sliding speed, and fiber orientation affect the tangential interaction between two prepreg layers. Moreover, a hydro-lubricant model was introduced to study the relative motion mechanism of this fabric-resin-fabric system, and the results agreed well with the experiment data. The interaction factors obtained from this research will be implemented in a preforming process finite element simulation model.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(8):081004-081004-12. doi:10.1115/1.4040157.

This paper described the effects of prestraining and annealing on plastic anisotropy (r-value) of aluminum alloy 5182-O sheets including two prestrain paths and two annealing conditions. During the prestraining and annealing processes, r-value changed depending on prestrain paths and annealing conditions. Although there were slight changes of the normal anisotropy coefficient, r¯, during prestraining and annealing processes, the planar anisotropy coefficient, Δr, increased significantly, especially for the uniaxial prestrain condition. This could accelerate the development of earing during a sheet forming operation. Also, the corresponding sheet textures in rolling direction (RD)/TD plane after prestraining and annealing processes were observed through electron backscatter diffraction (EBSD) analysis to explain the r-value changes, where the viscoplastic self-consistent (VPSC) model was used to correlate the determined texture to measured r-values. It is found that the sheet texture also had significant changes relating to the prestrain paths and annealing conditions resulting in varied r-values.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Manuf. Sci. Eng. 2018;140(8):084501-084501-6. doi:10.1115/1.4039766.

Recent advances in fused filament fabrication (FFF), such as five-axis printing, patching existing parts, and certain hybrid manufacturing processes, involve printing atop a previously manufactured polymer substrate. The success of these technologies depends upon the bond strength between the substrate and the newly added geometry. ANOVA and response surface methods were used to determine the effect of three process parameters on bond tensile strength: surface roughness, layer thickness, and raster angle. Experimental results indicate that the process–property relationships are not identical to those found in single, continuous FFF operations, and that the physical bonding mechanisms may also be different. Bond strength was found to be highly sensitive to surface roughness and layer thickness, and distinct optimal parameter settings exist. These results represent a first step toward understanding bond strength in such circumstances, allowing manufacturers to intelligently select process parameters for the production of both the substrate and the secondary geometry.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(8):084502-084502-7. doi:10.1115/1.4039917.

The evolution of the manufacturing industry has favored the use of new technologies that increase the level of autonomy in production systems. The work presented shows a methodology that allows for online estimation of cutting parameters based on the analysis of the cutting force signal pattern. The dynamic response of the tool is taken into account through a function that relates the response time to the input variables in the process. The force signal is obtained with a dynamometric platform based on piezoelectric sensors. The final section of the paper shows the experimental validation where machining tests with variable machining conditions were carried out. The results reveal high precision in the estimation of depths of cut in end milling.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(8):084503-084503-6. doi:10.1115/1.4040026.

Zinc (Zn) is an important material for numerous applications since it has pre-eminent ductility and high ultimate tensile strain, as well high corrosion resistivity and good biocompatibility. However, since Zn suffers from low mechanical strengths, most of the applications would use Zn as a coating or alloying element. In this study, a new class of Zn-based material with a significantly enhanced mechanical property is developed. The zinc-10 vol % tungsten carbide (Zn-10WC) nanocomposite was fabricated by cold compaction followed by a melting process. The Zn-10WC nanocomposites offer a uniform nanoparticle dispersion with little agglomeration, exhibiting significantly enhanced mechanical properties by micropillar compression tests and microwire tensile testing. The nanocomposites offer an over 200% and 180% increase in yield strength and ultimate tensile strength (UTS), respectively. The strengthening effect could be attributed to Orowan strengthening and grain refinement induced by nanoparticles.

Commentary by Dr. Valentin Fuster

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