Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method

Li, Kai; Yu, Yan-Yun; He, Jingyi; Zhao, Decai; Lin, Yan

doi:10.1007/s12204-018-1975-0

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Researchers at Xi’an Jiaotong University in China have designed a new multisegment continuous structure exoskeleton finger rehabilitation robot, 93 as shown in Figure 7 , which is composed of spring plates, linear motors, and multiple continuous structures. The mechanism uses a single linear motor to apply driving force, bend the spring plate, and drive the deformation of a multisegment continuous structure, enhancing the flexibility of muscles and joints through finger bending/stretching training, thereby improving finger movement, Figure 8 shows the structure and working principle of the mechanism in detail.…”

Section: Mechanical Classification Of Finger Rehabilitationmentioning

confidence: 99%

Research Status and Prospect of Finger Rehabilitation Machinery

Zhang,

Calderon,

Huang

et al. 2024

MDER

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About 80% of stroke patients have hand motor dysfunction, and wearing finger rehabilitation machinery can enable patients to carry out efficient passive rehabilitation training independently. At present, many typical finger rehabilitation machines have been developed, and clinical experiments have confirmed the effectiveness of mechanically assisted finger rehabilitation. In this paper, the finger rehabilitation machinery will be classified in the actuation mode, and the terminal traction drive/motor drive/spring drive/rope drive/memory alloy drive/electroactive material drive/hydraulic drive/pneumatic drive technology and its typical applications are analyzed. Study the structure, control methods, overlap between mechanical bending nodes and finger joints, training modes, response speed, and driving force of various types of finger rehabilitation machinery. The advantages and disadvantages of various actuation methods of finger rehabilitation machinery are summarized. Finally, the difficulties and opportunities faced by the future development of finger rehabilitation machinery are prospected. In general, with the continuous improvement of quality of life, stroke patients need flexible, segmented control, accurate bending, multi-training mode, fast response, and good driving force finger rehabilitation machinery. This will also be a future hot research direction.

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Section: Mechanical Classification Of Finger Rehabilitationmentioning

confidence: 99%

Research Status and Prospect of Finger Rehabilitation Machinery

Zhang,

Calderon,

Huang

et al. 2024

MDER

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“…From the perspective of technological implementation, they can be further divided into virtual reality finger training devices (to enhance the training experience) and electric finger training devices (using sensing technology [14] and feedback [15] systems to help patients master movement techniques). From the perspective of design pathways, current rehabilitation training devices focus on optimizing drivers, controls [16], structures and trajectory lines [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

AHP-based Design of a Finger Training Device for Stroke

Wei,

Luh,

et al. 2023

IJACSA

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This study aims to develop a stroke finger training device specifically for office hand scenes which exercises the small muscles of the fingertips and improves the hand strength of stroke patients. The device has a real-time recording function for muscle strength changes during finger muscle training and enhances interaction through the feedback of training device data, thereby improving training effectiveness. This research involves analyzing hand postures and muscle movements in computer office scenes, designing questionnaires to obtain user requirements, and using the Delphi analysis method to screen key indicators and form standards and program layers. The Analytic Hierarchy Process (AHP) evaluates and ranks the core design elements. According to the design elements, the structure and training system design are guided, and a prototype is built for experimental testing. The results show that the training device effectively improves participants' hand strength, stability, and coordination and helps restore hand function. The AHP method allows for evaluating and ranking the device's design elements, making the device design more reasonable and comprehensive. Overall, the training device significantly improves the finger muscle strength of participants.

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“…Additionally, finite element models of complex structures require many parameters to be updated, the computational workload is large, and the traditional method is difficult to implement [8]. The method [9,10] based on surrogate models replaces the structural finite element model with a surrogate model with strong fitting ability, simple structure, and broad applicability and is combined with an optimization algorithm with strong global search ability and high efficiency to iteratively optimize the updating parameters. Thus, this method simplifies the model updating process and improves the efficiency of the updating work, and it is the most widely used method in the field of model updating [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization

He,

Zeng,

Zhou

et al. 2023

Sensors

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Aging, corrosive environments, and inadequate maintenance may result in performance deterioration of civil infrastructures, and finite element model updating is a commonly employed structural health monitoring procedure in civil engineering to reflect the current situation and to ensure the safety and serviceability of structures. Using the finite element model updating process to obtain the relationship between the structural responses and updating parameters, this paper proposes a method of using the wavelet neural network (WNN) as the surrogate model combined with the wind-driven optimization (WDO) algorithm to update the structural finite element model. The method was applied to finite element model updating of a continuous beam structure of three equal spans to verify its feasibility, the results show that the WNN can reflect the nonlinear relationship between structural responses and the parameters and has an outstanding simulation performance; the WDO has an excellent ability for optimization and can effectively improve the efficiency of model updating. Finally, the method was applied to update a real bridge model, and the results show that the finite element model update based on WDO and WNN is applicable to the updating of a multi-parameter bridge model, which has practical significance in engineering and high efficiency in finite element model updating. The differences between the updated values and measured values are all within the range of 5%, while the maximum difference was reduced from −10.9% to −3.6%. The proposed finite element model updating method is applicable and practical for multi-parameter bridge model updating and has the advantages of high updating efficiency, reliability, and practical significance.

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Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method

Cited by 5 publications

References 19 publications

Research Status and Prospect of Finger Rehabilitation Machinery

Research Status and Prospect of Finger Rehabilitation Machinery

AHP-based Design of a Finger Training Device for Stroke

Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization

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