The SwarmItFix Pilot

Sagar, Keerthi; Leonardo, Luis de; Molfino, Rezia; Zielińska, Teresa; Zieliński, Cezary; Zlatanov, Dimiter; Zoppi, Matteo

doi:10.1016/j.promfg.2017.07.128

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…As far as the ability to guarantee the stability of the machining process is concerned, IBF can reliably support the cutting area, thus guaranteeing the stability of the machining process and ultimately improving the machining quality. Mechanical fixtures have a certain volume of space, which reduces their clamping accessibility and makes it difficult to reliably support some machining areas [29,30]. In addition, the rigidity of IBF is higher than other phase change fixtures, which makes the support stability of IBF better [21,31].…”

Section: Principle and Characteristics Of Ibfmentioning

confidence: 99%

3D ice-based fixturing system for thin-walled parts machining

Liu,

Wang,

Xiong

et al. 2023

J. Phys.: Conf. Ser.

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Fixtures have an important influence on the machining process of thin-walled parts (TWP) and directly determine their final quality. Aiming at the problem of machining deformation caused by weak rigidity of TWP, an ice-based fixturing (IBF) is designed by taking advantage of the rigidity and viscosity of ice. The feasibility of IBF is then demonstrated based on the published papers of the authors of this paper. Afterwards, the structure and refrigeration process of IBF are described in detail, and the refrigeration capacity of the IBF system is analyzed, and the experimental results show that the IBF can achieve uniform and reliable icing. Finally, the process steps are formulated. This paper can provide a valuable reference for the development of advanced fixtures.

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Section: Principle and Characteristics Of Ibfmentioning

confidence: 99%

3D ice-based fixturing system for thin-walled parts machining

Liu,

Wang,

Xiong

et al. 2023

J. Phys.: Conf. Ser.

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“…Similarly, the elastoplastic component of the strain is amplitude dependent and contributes to the system damping as considered in the Eq. (6).…”

Section: Roughness Modelmentioning

confidence: 99%

“…2 However, these solutions lack the accuracy or the stiffness to guarantee the part acceptance criteria. 6 For instance, in order to increase the stiffness of the part support, Youcef-Toumi and Buitrago 7 proposed a conformable fixture with a superelastic NiTi alloy. Nevertheless, these adaptable elements can only be employed as a support, not a clamping surface.…”

Section: Introductionmentioning

confidence: 99%

Vibrations characterization in milling of low stiffness parts with a rubber-based vacuum fixture

Rubio-Mateos

Casuso

Rivero

et al. 2021

Chinese Journal of Aeronautics

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“…11,12 SwarmItFIX (Figure 1) is one such patented mobile RFA setup that supports sheet metals at the bottom when machining happens on top of the metal sheet. 13,14 It follows the five-step sequence to traverse between any two support locations. These steps ensure coordination among head, PKM and mobile base agents of the SwarmItFIX, and each step corresponds to the sequence of locomotion of one agent.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Veeramani

Muthuswamy

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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SwarmItFIX (self-reconfigurable intelligent swarm fixtures) is a multi-agent setup mainly used as a robotic fixture for large Sheet metal machining operations. A Constraint Satisfaction Problem (CSP) based planning model is utilized currently for computing the locomotion sequence of multiple agents of the SwarmItFIX. But the SwarmItFIX faces several challenges with the current planner as it fails on several occasions. Moreover, the current planner computes only the goal positions of the base agent, not the path. To overcome these issues, a novel hierarchical planner is proposed, which employs Monte Carlo and SARSA TD based model-free Reinforcement Learning (RL) algorithms for the computation of locomotion sequences of head and base agents, respectively. These methods hold two distinct features when compared with the existing methods (i) the transition model is not required for obtaining the locomotion sequence of the computational agent, and (ii) the state-space of the computational agent become scalable. The obtained results show that the proposed planner is capable of delivering optimal makespan for effective fixturing during the sheet metal milling process.

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The SwarmItFix Pilot

Cited by 10 publications

References 14 publications

3D ice-based fixturing system for thin-walled parts machining

3D ice-based fixturing system for thin-walled parts machining

Vibrations characterization in milling of low stiffness parts with a rubber-based vacuum fixture

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

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