Active-Learning Combined with Topology Optimization for Top-Down Design of Multi-Component Systems

Krischer, Lukas; Sureshbabu, Anand Vazhapilli; Zimmermann, Markus

doi:10.1017/pds.2022.165

Cited by 4 publications

(3 citation statements)

References 18 publications

(20 reference statements)

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“…However, the shortcoming of the design process shown in Figure 2 is that the mass and inertial properties of the components are not known in step D 1 and are only computed later in D 2 . Therefore, future work would extend the procedure to a fully automatic optimisation process that includes an informed decomposition by training meta-models for design domains, as introduced in [52]. Another potential direction is to perform mass minimisation with the minimum eigenfrequency requirement as a constraint, accounting for the dynamic stiffness of each of the OSEs, as explored in [67].…”

Section: Discussionmentioning

confidence: 99%

“…An approach based on the so-called solution space was presented in [50] as a strategy to decouple a multi-component system, enabling the independent design of the respective components. A meta-model-informed decomposition was introduced in [51,52], eliminating the need for coordination between sub-problems. The current work adopts such a distributed optimisation approach by decomposing the system-level requirements into componentlevel specifications, presenting a computationally tractable way to compute mass optimal topologies of robotic links.…”

Section: Structural Optimisation Of Robotsmentioning

confidence: 99%

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Computational Systems Design of Low-Cost Lightweight Robots

Sathuluri,

Sureshbabu,

Frank

et al. 2023

Robotics

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With the increased demand for customisation, developing task-specific robots for industrial and personal applications has become essential. Collaborative robots are often preferred over conventional industrial robots in human-centred production environments. However, fixed architecture robots lack the ability to adapt to changing user demands, while modular, reconfigurable robots provide a quick and affordable alternative. Standardised robot modules often derive their characteristics from conventional industrial robots, making them expensive and bulky and potentially limiting their wider adoption. To address this issue, the current work proposes a top-down multidisciplinary computational design strategy emphasising the low cost and lightweight attributes of modular robots within two consecutive optimisation problems. The first step employs an informed search strategy to explore the design space of robot modules to identify a low-cost robot architecture and controller. The second step employs dynamics-informed structural optimisation to reduce the robot’s net weight. The proposed methodology is demonstrated on a set of example requirements, illustrating that (1) the robot modules allow exploring non-intuitive robot architectures, (2) the structural mass of the resulting robot is 16 % lower compared to a robot designed using conventional aluminium tubes, and (3) the designed modules ensure the physical feasibility of the robots produced.

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Section: Discussionmentioning

confidence: 99%

Section: Structural Optimisation Of Robotsmentioning

confidence: 99%

Computational Systems Design of Low-Cost Lightweight Robots

Sathuluri,

Sureshbabu,

Frank

et al. 2023

Robotics

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show abstract

“…with 𝑠 (𝑖) being the length of the 𝑖th link. The procedure of decomposing compliance requirements from the system level on to component level is called uninformed decomposition as it only requires the geometrical information of the system in order to derive requirements compared to the informed decomposition with trained meta models presented by Krischer et al 2022 𝑤𝑖𝑡ℎ 𝑗 = 1 … 𝑛 dofs .…”

Section: Problem Setup and System Decompositionmentioning

confidence: 99%

Topology optimisation of multiple robot links considering screw connections

Wanninger,

Frank,

Zimmermann

2024

Proc. Des. Soc.

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This paper presents a method for the lightweight design of robotic links subject to dynamic loads and requirements on the overall system stiffness. It includes (1) a decomposition scheme to enable separate component optimization and (2) an approach based on topology optimization for optimal load path design of screw connections. The approach reduces computing cost and mass of designs with screw connections.

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Distributed design optimization of multi-component systems using meta models and topology optimization

Krischer,

Endress,

Wanninger

et al. 2024

Struct Multidisc Optim

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Distributed optimization architectures decompose large monolithic optimization problems into sets of smaller and more manageable optimization subproblems. To ensure consistency and convergence towards a global optimum, however, cumbersome coordination is necessary and often not sufficient. A distributed optimization architecture was previously proposed that does not require coordination. This so-called Informed Decomposition is based on two types of optimization problems: (1) one for system optimization to produce stiffness requirements on components using pre-trained meta models and (2) one for the optimization of components with two interfaces to produce detailed geometries that satisfy the stiffness requirements. Each component optimization problem can be carried out independently and in parallel. This paper extends the approach to three-dimensional structures consisting of components with six degrees of freedom per interface, thus significantly increasing the applicability to practical problems. For this, an 8-dimensional representation of the general 12 x 12 interface stiffness matrix for components is derived. Meta models for mass estimation and physical feasibility of stiffness targets are trained using an active-learning strategy. A simple two-component structure and a large robot structure consisting of four components subject to constraints for 100 different loading scenarios are optimized. The example results are at most 12.9% heavier than those of a monolithic optimization.

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Active-Learning Combined with Topology Optimization for Top-Down Design of Multi-Component Systems

Cited by 4 publications

References 18 publications

Computational Systems Design of Low-Cost Lightweight Robots

Computational Systems Design of Low-Cost Lightweight Robots

Topology optimisation of multiple robot links considering screw connections

Distributed design optimization of multi-component systems using meta models and topology optimization

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