Topology optimization of rigid-links for industrial manipulator considering dynamic loading conditions

Srinivas, G. Lakshmi; Javed, Arshad

doi:10.1016/j.mechmachtheory.2020.103979

Cited by 20 publications

(9 citation statements)

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“…Here, the uncertainty band of volume fraction is considered positive only because, after applying filters and removal of greyscale techniques, volume fraction generally increases slightly. 1 Also, the uncertainty of the force is selected in a positive direction only to signify the worst-case scenario. The negative values in the force uncertainties are neglected; it would not add value to the design rather increase the number of experiments and computation cost.…”

Section: Methodsmentioning

confidence: 99%

“…Thus a minimum of maximum performance value is selected as the best performed optimized topology for compliance, deflection, and Stress. 1,30 The obtained topology based on each combination also calculates the energy consumption using the 1-DOF dynamic model as detailed in 1-DOF manipulator-link dynamic model . Each combination, along with the mean performance values of manipulator-link, is provided in Table 3.…”

Section: Simulation Of the Performancementioning

confidence: 99%

“…The manipulator-link is essential in supporting and structuring the complex industrial robots that suffer from various mechanical loadings in the operating range. 1 The stiffness of the manipulator-link is important to overcome mechanical vibrations, reduce noise, speed, and improve accuracy. Topology optimization technique improves the dynamic performance and overall stability of the mechanical member.…”

Section: Introductionmentioning

confidence: 99%

“…16–19 However, for better design concerns, dynamic loads are needed; recently, limited trials are made to optimize the structural components topologically by considering dynamic loads. 1,20–22 For a detailed explanation, the reader can also refer to the review article related to topology optimization of serial manipulators in the static and dynamic analysis. 23 The equivalent static load method (ESLM) is other approach to deal with dynamic topology problems using time step values.…”

Section: Introductionmentioning

confidence: 99%

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Parametric analysis of topologically optimized mechanical member considering dynamic loading

Srinivas

Javed

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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The optimal topology and its performance in dynamic loading situations result in discontinue function corresponding to the input factors such as volume fraction, thickness, material property, and loading conditions. In a realist scenario, the performance prediction becomes erroneous and challenging for the components under dynamic loading conditions with uncertainties. The conventional closed-form deterministic approaches are complicated for these problems. Here, a method is presented to establish the relative influence and function relationship of the input factors with the performance values, including controllable and non-controllable uncertainties. The design of experiment approach is used to apply full factorial design with Taguchi’s orthogonal array; performances of the optimal topologies are considered responses. The non-uniform topology generation method is applied based on the deflection threshold value to generate topologies for dynamic conditions. A dynamic model of the manipulator-link is developed to apply boundary conditions and provide performance values: compliance, deflection, Stress, and energy consumption values. Statistical techniques such as the analysis-of-mean (ANOM), analysis-of-variance (ANOVA), signal-to-noise-ratio (SNR), and mean performance values are employed to observe the significance of input factors and generate equivalent preformation relation. From ANOM and ANOVA, all input parameters show mutual interaction; force is observed as the most significant factor. From SNR values, experimental combination number 9,9,6,1 is observed as the most robust for compliance (21.13), deflection (43.93), Stress (−16.64), and energy consumption (12.05). Similarly, at the same combinations, the mean performance values are minimum and coefficient of determination (R2) percentages of the model are 94.64%, 96.93%, 73.69%, and 95.14%.

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Section: Methodsmentioning

confidence: 99%

Section: Simulation Of the Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%