Twist Springback Measurement of Autonomous Underwater Vehicle Propeller Blade Based on Profile Deviation

Abdullah, .

doi:10.3844/ajassp.2013.515.524

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The spring-back is recompensed through this punch uprooting. (Abdullah et al, 2013) have described that the spring-back in air twisting of metallic sheets are forecasted through the improved ANN. The spring-back and punch travel, pass on the hole to the section of bite the dust sweep proportion, two yields and strain solidifying type, punch range/thickness proportion and an edge of twist are the five sources of information in engineering.…”

Section: Introductionmentioning

confidence: 99%

Experimental Prediction of Spring back in U Bending Profile Process Modeling using Artificial Neural Network

Saravanan*,

Saravanan,

Jeyasimman

et al. 2020

IJITEE

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An ANN or Artificial Neural Network is prototypical that is employed to connect the variety of parameter space. The air bend contains curve force and spring-back. These are predicted through the numerical and semi-logical model. A number of researchers examine these models. A collection of information is fitted by Artificial Neural Network which has high flexibility, the capacity to delineate non-straight connections and parallel usage, collaborations of process parameters, Vigor and the adaptation to non-critical failure are the main characteristics of ANN. Due to these characteristics, and the device of ANN successfully monitors the problems. The significant quality of ANN is that the "U" shaped profile of bending among the information that is involved in the associations of parameters and mind-boggling and the sheet metals bend researchers.

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Section: Introductionmentioning

confidence: 99%

Experimental Prediction of Spring back in U Bending Profile Process Modeling using Artificial Neural Network

Saravanan*,

Saravanan,

Jeyasimman

et al. 2020

IJITEE

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“…This can be concluded that the validation is reliable since the percentage difference are quite low for 2.2 mm thickness. From research by [12], the deviation below 10% is considered low in springback cases. For 3.2 mm thickness, the deviation is quite large because of the twisting moment goes beyond the plastic region of the section.…”

Section: Resultsmentioning

confidence: 99%

Finite element simulation of twist forming process to study twist springback pattern

Nashrudin

Abdullah

2016

MATEC Web Conf.

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Abstract. Springback is one of the most common defects found in the metal forming of automotive parts. There are three conditions which can be considered as springback i.e. flange angle change, sidewall curl and twist springback and among them, twist springback is the most complicated problem. This study will focuses on the development of finite element simulation model of the twist forming process. The main aim of this project is to investigate the parameters that may affect the twist springback. Few parameters including twist angle, hardening constant and thickness are explored using finite element (FE) software ANSYS Workbench (16.0). The rectangular mild strips are used to form the twist forming. The standard material properties and stress-strain curve of mild steel had been used to get the springback prediction. The results of springback were measured by the difference of the bending angles before and after unloading process. The results were then be validated with the research made of Dwivedi et al., (2002). The results show that the springback angle reduces as the thickness of strips are increased and also as the angle of twist increases.

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“…Focus variation, a technique mainly used for measuring geometrical defects such as surface waviness and roughness, gave 3D depth readings. As proposed by Abdullah et al [4], this method could also be used to evaluate geometric and dimensional defects of small propeller blades. Cavada and Fadón [5] gave a thorough assessment of various laser-based techniques for measuring marine propellers.…”

Section: Introductionmentioning

confidence: 99%

Identification of the geometric design parameters of propeller blades from 3D scanning

2022

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This paper presents a new method for the identification of geometric design parameters of ISO 484 class propeller blades from scanned point cloud data. The method can be used for tolerance inspection and in-line measurement of manufactured blades, and for wear assessment and reverse engineering of propellers in service. The geometry of the propeller blades is specified by a set of blade sections stacked radially on concentric cylindrical surfaces. For each of these sections, the chord line and mean camber line is determined, and geometric design parameters such as the pitch, skew, chord length, camber, and thickness distributions are identified. The proposed method is a complete procedure for identifying the design parameters of marine propeller blades from point cloud data, and includes a novel method for the precise identification of the mean camber line based on Voronoi diagrams and Delaunay triangulation. The paper includes validation of the proposed method in experiments where reverse engineering is applied to a propeller blade of the KVLCC2 propeller, and in 3D scanning of a large, high-skew thruster blade where the results were compared to CMM measurements.

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Twist Springback Measurement of Autonomous Underwater Vehicle Propeller Blade Based on Profile Deviation

Cited by 8 publications

References 13 publications

Experimental Prediction of Spring back in U Bending Profile Process Modeling using Artificial Neural Network

Experimental Prediction of Spring back in U Bending Profile Process Modeling using Artificial Neural Network

Finite element simulation of twist forming process to study twist springback pattern

Identification of the geometric design parameters of propeller blades from 3D scanning

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