Grasp Point Optimization and Leakage-Compliant Dimensioning of Energy-Efficient Vacuum-Based Gripping Systems

Gabriel, Felix; Baars, Susanna; Römer, Martin; Dröder, Klaus

doi:10.3390/machines9080149

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…[21,22] Advancements in dynamic simulation technology have allowed the consideration of workpiece surface roughness, curvature, as well as the number and diameter of SCs in grasp planning optimization. [23,24] Genetic algorithms and deep learning optimization methods have been utilized to predict suction effects and optimize grasping locations, leading to improved vacuum force and reduced energy consumption. [25,26] By employing computer vision and intelligent algorithms, energy consumption in SC deployment has been effectively reduced.…”

Section: Introductionmentioning

confidence: 99%

Self‐Closing and Self‐Healing Multi‐Material Suction Cups for Energy‐Efficient Vacuum Grippers

Wang,

Terryn,

Wang

et al. 2023

Advanced Intelligent Systems

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While vacuum grippers offer an economical solution, the environmental impact of energy waste through their suction cups (SCs) cannot be overlooked. This waste stems from three key factors: i) air losses from idle SCs arranged in arrays, ii) inadequate sealing on target surfaces leading to air leaks, and iii) damage from sharp objects resulting in leaking perforations. To overcome these challenges, in this article, a comprehensive approach is presented that involves the development of a i) self‐closing, ii) multi‐material, and iii) self‐healing system based on reversible elastomers cross‐linked via the Diels–Alder (DA) reaction. The system incorporates a fully autonomous self‐closing mechanism to prevent energy waste in SC arrays during periods of non‐contact. Fluid–structure interaction simulations are utilized to analyze the design. Versatility and stability are achieved by incorporating hyper‐flexible and stiff elastomers in a multi‐material design, supported by covalent DA cross‐links that ensure robustness through high‐strength multi‐material interfaces. These DA cross‐links also enable self‐healing capabilities, allowing the SCs to recover from macroscopic damages within 1 day at ambient conditions or in a single hour with mild heating (80–90 °C), restoring full performance. Additionally, in the article, a recycling method is introduced for multi‐material SCs based on the mechanical separation of reversible polymers.

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Section: Introductionmentioning

confidence: 99%

Self‐Closing and Self‐Healing Multi‐Material Suction Cups for Energy‐Efficient Vacuum Grippers

Wang,

Terryn,

Wang

et al. 2023

Advanced Intelligent Systems

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show abstract

“…The manufacturing industry plays an indispensable role in the economic development and social construction of a country [9,10]. At the same time, the manufacturing industry is also an area where a country invests a tremendous amount of energy consumption [11,12]. According to the international energy outlook 2021 reference case, after a period of decline in coal consumption through 2030, consumption of all major fuels will grow from 2030 to 2050.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Population NSGA-III with Dual Control Strategy for Flexible Job Shop Scheduling Problem with the Consideration of Energy Consumption and Weight

Yang

Zhou

et al. 2021

Machines

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The flexible job shop scheduling problem has always been the focus of research in the manufacturing field. However, most of the previous studies focused more on efficiency and ignored energy consumption. Energy, especially non-renewable energy, is an essential factor affecting the sustainable development of a country. To this end, this paper designs a flexible job shop scheduling problem model with energy consideration more in line with the production field. Except for the processing stage, the energy consumption of the transport, set up, unload, and idle stage are also included in our model. The weight property of jobs is also considered in our model. The heavier the job, the more energy it consumes during the transport, set up, and unload stage. Meanwhile, this paper invents an adaptive population non-dominated sorting genetic algorithm III (APNSGA-III) that combines the dual control strategy with the non-dominated sorting genetic algorithm III (NSGA-III) to solve our flexible job shop scheduling problem model. Four flexible job shop scheduling problem instances are formulated to examine the performance of our algorithm. The results achieved by the APNSGA-III method are compared with five classic multi-objective optimization algorithms. The results show that our proposed algorithm is efficient and powerful when dealing with the multi-objective flexible job shop scheduling problem model that includes energy consumption.

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Combining finite element analysis and reinforcement learning for optimal grip point planning of flexible components

Römer,

Demircan,

Hürkamp

et al. 2024

Prod. Eng. Res. Devel.

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Handling large flexible components is still a challenge in many industries. Examples include the handling of fibre-reinforced plastics or the assembly of industrial-scale electrolytic cells. Difficulties often arise in the design of suitable endeffectors. Inefficient gripping point design, i.e. the total number and positioning of grippers, can lead to increased stress and deflection of the component being handled. To counteract this, endeffectors are often oversized resulting in the use of more grippers than needed. Correspondingly, heavier moving masses imply longer handling times as well as higher energy consumption. This paper presents a process for planning and optimising gripping points for large flexible components. In addition to the shape of the component, actual dynamic loads of the handling path are also taken into account. The key element to the process is an optimisation algorithm based on reinforcement learning and trained using an finite element method (FEM) simulation. After computing a desirable starting configuration, the algorithm optimises the placement of gripper positions while aiming for a reduced total number. In addition, the optimisation has prescribed handling limits, such as physical and geometric constraints, that must not be exceeded. It was shown that the algorithm satisfactorily optimises the gripping points for dynamic loads for different materials and shapes. Furthermore, it has been shown that the computation of an initial configuration yields preferable results for simple components, yet requiring optimisation in the case of more complex shapes.

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Grasp Point Optimization and Leakage-Compliant Dimensioning of Energy-Efficient Vacuum-Based Gripping Systems

Cited by 3 publications

References 20 publications

Self‐Closing and Self‐Healing Multi‐Material Suction Cups for Energy‐Efficient Vacuum Grippers

Self‐Closing and Self‐Healing Multi‐Material Suction Cups for Energy‐Efficient Vacuum Grippers

Adaptive Population NSGA-III with Dual Control Strategy for Flexible Job Shop Scheduling Problem with the Consideration of Energy Consumption and Weight

Combining finite element analysis and reinforcement learning for optimal grip point planning of flexible components

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