Design and optimization of artificial intelligence robot arm printable by a metal-based additive manufacturing process

Senthilkumar, V.; Sathies, T.

doi:10.1016/b978-0-323-91834-3.00025-9

Cited by 2 publications

(1 citation statement)

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“…To attain success, materials must meet three fundamental characteristics. Kumaran et al (Kumaran et al, 2023) suggested a structure to improve the design of an AI-based robotic arm. This framework is designed to predict a lightweight structure for the robotic arm and to study the deformation of the metal alloys which are created by the powder-based AM.…”

Section: Introductionmentioning

confidence: 99%

Topological and lattice-based AM optimization for improving the structural efficiency of robotic arms

Batista,

Agarwal,

Gurung

et al. 2024

Front. Mech. Eng.

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The robotic arm is one of the vital components of robot assembly. The purpose of the robotic arm is to transmit power and conduct the desired motion, i.e., translation or rotation. Robotic limbs are designed and constructed to execute certain tasks with a high degree of speed, accuracy, and efficiency. This research focuses on to enhancing the strength-to-weight ratio of robotic arm using certain techniques of additive manufacturing, i.e., topology optimization and lattice structure. Employing the finite element analysis, the impact of weight reduction optimization on structural parameters such as stress and deformation in the current design is assessed using ANSYS R18.1 for FE analysis and Creo parametric 7.0 design software for computer-aided design modeling. Observations reveal that the 0.5 and .4 scale lattice structure designs have deformation of 0.01453mm and 0.01453 mm respectively though the generic design has 0.01043 mm deformation. Notably, the 0.5 scale lattice of the robotic arm exhibits a 31.08% higher equivalent stress than the generic design with 29.3%. reduction in mass of the robotic arm. These findings highlight the efficacy of lattice structures for optimizing the robotic arm’s performance, contributing to advancements in power-efficient robot assembly processes.

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Section: Introductionmentioning

confidence: 99%