Modeling, Design and Construction of Articulated Hand for Use in Prosthetics, with Adaptive Control in Neural Networks Based on Mathematical Model for Finger

Soto, Emilio Vargas; Oscar, Baez,; Sosa, Sergio

doi:10.1109/micai.2010.34

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…The feeding speed and motor torque can be adjusted according to an internal pressure change in the FDM nozzle and layer thickness [3,4]. For the solidification process, the filament is heated to above its melting point in 0.1 s after extrusion; thus, it combines with the previous layer by a weld.…”

Section: Introductionmentioning

confidence: 99%

Analysis of crack propagation of PLA fabricated by the additive manufacturing technique

Mohd Yusof,

Md Yusop,

Abu Kasim

2024

Eng. Res. Express

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Understanding crack propagation is crucial for evaluating the structural integrity and reliability of additive manufacturing components, as cracks can compromise mechanical properties and potentially lead to catastrophic failures. The study of crack propagation in additive manufacturing components is used to develop strategies for mitigating crack initiation and growth, improving material properties, and optimising the design and manufacturing processes. Crack propagation in additive manufacturing components can be influenced by various factors, including material properties, design considerations, manufacturing defects, and loading conditions. Due to the identified issue, the study was carried out to investigate the crack propagation of the Fused Deposition Modeling (FDM) component using compact tension fracture testing. The experimental work started with fabricating the samples using PLA material, followed by a fracture test based on the compact tension specimen test to get a response of the structure and its crack propagation under tensile. Material properties were also collected using the dog bone tensile test. The material properties of the testing were then imported to Finite Element Analysis for further investigation of fracture mechanics. It was found that the maximum force of the sample was 141.7±28N at 1.70±0.26mm displacement, and cracks initiated around the tip and propagated upward or downward based on the initial crack location. The deformation patterns of PLA material have shown it to be brittle plastic deformation and low energy absorption before fracture.

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Section: Introductionmentioning

confidence: 99%

Analysis of crack propagation of PLA fabricated by the additive manufacturing technique

Mohd Yusof,

Md Yusop,

Abu Kasim

2024

Eng. Res. Express

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“…In addition to rapid prototyping, it applies to a wide range of industries, including medical, automobile, and aviation, as the major sectors. [10][11][12][13][14][15] Among all the AM technologies, FDM is one of the most popular 3D printing methods due to its simple, compact design and economical availability. 4 In the next section, a detailed discussion has been done on the FDM process.…”

Section: Introductionmentioning

confidence: 99%

Extrusion strategies in fused deposition additive manufacturing: A review

Krishnanand

Singh

Mittal

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fused deposition or fused layer additive manufacturing is a technique in which a part is fabricated layer by layer using filament or pellets-form input material. The extruder is the central part of the fused deposition additive manufacturing (FDAM) system. The extrusion mechanism is unique to the geometry and form of the feedstock material. Extrusion of filament-form material through the nozzle needs to design of a material feeding mechanism based on various filament extrusion parameters. At the same time if the material is in the form of pellets, then the design of the feeding mechanism is very different due to the shape and size of the pellets. These two different design strategies for material extrusion namely the design of the feeding mechanism of filament and pellets form materials have been a major concern to avoid unwanted deformation of extruded materials in FDAM. These two design strategies for filament and pellet-form materials have led to the development of several material extrusion strategies. The present paper reviews various material extrusion strategies developed for FDAM of filament or pellets form input material. This article presents the review of design, development, performance evaluation, and process planning for each extrusion strategy. Based on the literature review authors have suggested a possible extrusion method that can accept both forms of materials to fulfill the need for the filament as well as pellets-based additive manufacturing.

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“…Several mathematical and Artificial Intelligence (AI) techniques have received extensive attraction for the analysis and classification of EMG signals. Among the most common classification methods are the Bayesian systems [5], artificial neural networks (ANN) [3,6], Markov maps [7], and fuzzy logic [4] [8].…”

Section: Introductionmentioning

confidence: 99%

A case study on tuning artificial neural networks to recognize signal patterns of hand motions

et al. 2015

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This paper presents the development of artificial neural networks (ANN) as pattern recognition systems to classify surface electromyography signals (sEMG) into nine select hand motions from seven subjects. Multiple networks were designed to determine how well a network could adapt to signals from different subjects. This was achieved by developing multiple networks with different combinations of the volunteers for training. Each network was tested with signals from all volunteers to determine how well they could adapt to new subjects. It was found that ANNs trained using only one or two subjects would perform exceptionally well when tested with signals from the same subjects but relatively poorly when tested with signals from new subjects. As the number of subjects used for training increased, the ability of the network to accurately classify the signals from the trainees decreased but their ability to adapt to signals from new subjects increased. Solely based on these results, it can be inferred that ANNs developed using signals from a large amount of subjects could be used to accurately classify signals from completely new subjects. Research presented in this paper has potential to be further developed as a basis for utilizing sEMG as control signals in electric devices such as myoelectric prosthesis or humanoid control. Index Terms-sEMG signal classification, Artificial Neural Networks (ANN), human-machine interaction

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Modeling, Design and Construction of Articulated Hand for Use in Prosthetics, with Adaptive Control in Neural Networks Based on Mathematical Model for Finger

Cited by 6 publications

References 4 publications

Analysis of crack propagation of PLA fabricated by the additive manufacturing technique

Analysis of crack propagation of PLA fabricated by the additive manufacturing technique

Extrusion strategies in fused deposition additive manufacturing: A review

A case study on tuning artificial neural networks to recognize signal patterns of hand motions

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