Development of a fuzzy logic based model to elucidate the effect of FSW parameters on the ultimate tensile strength and elongation of pure copper joints

Heidarzadeh, Akbar; Testik, Özlem Müge; Güleryüz, Güldal; Barenji, Reza Vatankhah

doi:10.1016/j.jmapro.2020.02.020

Cited by 44 publications

(12 citation statements)

References 39 publications

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“…Podržaj and Simončič showed the effectiveness of using a fuzzy logicbased controller to govern resistance spot welding processes [44]. Heidarzadeh et al applied a fuzzy logic model to elucidate and optimize the friction stir welding of pure copper [45]. Fuzzy-logic control has also been proven as an effective approach for controlling the height of additively manufactured parts by laser depositions [46] and the width of thin-walled components by WAAM [47].…”

Section: Introductionmentioning

confidence: 99%

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Zhang

et al. 2021

Journal of Manufacturing Processes

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Section: Introductionmentioning

confidence: 99%

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Zhang

et al. 2021

Journal of Manufacturing Processes

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“…The fuzzy-logic-based modeling approach belongs to so-called soft-computing methods [ 36 , 37 , 38 , 39 , 40 , 41 ]. The technique was introduced in 1965 by Lofti Zadeh, who defined it as a precise logic of imprecision and approximate reasoning [ 34 , 42 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Knowledge-Driven AI System for Air Classification Process

et al. 2021

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Air classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging, the development of an efficient modeling system is of great practical significance. The paper introduces a novel, knowledge-based classification (FLClass) system of bulk materials. A wide range of operating parameters are considered in the study: the mean mass and the Sauter mean diameter of the fed material, classifier rotor speed, working air pressure, and test conducting time. The output variables are the Sauter mean diameter and the cut size of the classification product, as well as the performance of the process. The model was successfully validated against experimental data. The maximum relative error between the measured and predicted data is lower than 9%. The presented fuzzy-logic-based approach allows an optimization study of the process to be conducted. For the considered range of input parameters, the highest performance of the classification process is equal to almost 362 g/min. To the best of our knowledge, this paper is the first one available in open literature dealing with the fuzzy logic approach in modeling the air classification process of bulk materials.

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“…A fuzzy logic system is an approach of mapping input data set nonlinearly to a scalar output [9]. Broadly, it comprises of five steps.…”

Section: Fuzzy Logic Approachmentioning

confidence: 99%

A fuzzy logic approach for investigating the tribological behavior of polymer composite

Jagadeesh¹,

Kumar²,

Aneesh³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The application spectrum of natural fiber reinforced polymer composites has been increasing tremendously because of enhanced tribologicalbehavior. The present study investigates the wear and coefficient of friction of castor oil fiber reinforced composites for developing as new tribo-material. Composites with 40 vol.% with short fiber length of 5mm were fabricated by hand lay-up method. The tribology tests were performed with pin on disc tribometer at normal loads (15N, 30N & 45N) and sliding distances (1000m, 2000m & 3000m). A fuzzy logic model has been developed to predict and analyze the wear and friction characteristics at unknown test cases. Each input variable was divided into three linguistic variables and each output variable was divided into five linguistic variables. A triangular membership function was used for defining all the variables. The capabilities of fuzzy logic model were tested by confirmatory experiments. The model predicted the wear results with an error of 3.12% and coefficient of friction by 2.19%.

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Development of a fuzzy logic based model to elucidate the effect of FSW parameters on the ultimate tensile strength and elongation of pure copper joints

Cited by 44 publications

References 39 publications

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Comprehensive Knowledge-Driven AI System for Air Classification Process

A fuzzy logic approach for investigating the tribological behavior of polymer composite

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