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Electron Beam Welding Investigation of Inconel 825 and Optimize Energy Consumption Using Integrated Fuzzy Logic-Particle Swarm Optimization Approach

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Abstract

Energy is an inseparable aspect of the twenty-first century; its utilization and availability have become highly relevant to the issues of climate change and growing demands. Welding is another vital manufacturing process that requires special attention due to its energy-intensive nature and environmental burdens. Thus, a study addressing energy consumption in welding processes without compromising product quality is the need of the hour. This study looks into the energy consumption of the seldom reported electron beam welding (EBW) process while welding a popular nickel-based superalloy (Inconel 825) without compromising quality by maintaining optimal weld strength of the weldments. Four EBW parameters viz., beam current (I), accelerating voltage (V), welding speed (S), and beam oscillation (O) are selected, and their influence on the net input energy (Enet) and ultimate tensile strength (UTS) is studied. The V is found as the dominant process parameter that has a direct influence on the Enet and UTS of the weldments. A Study on sensitivity analysis inferred that the value of UTS of the weldments is highly sensitive to the change in accelerating voltage sensitivity (0–300 MPa/kV). A weldment produced with an intermediate or higher Enet has determined to have the highest achievable UTS. Furthermore, a modified integrated optimization methodology combining fuzzy logic (FL) modeling with the meta-heuristic-based particle swarm optimization (PSO) algorithm is proposed to satisfy both objectives, i.e., minimize net input energy (Enet) consumption while achieving the desired ultimate tensile strength (UTS). The methodology yields a minimum Enet of 0.1243 kJ/mm, at parameter settings of V = 60 kV, I = 38 mA, S = 1100.4 mm/min, O = 200 Hz, achieving UTS of 701.8196 MPa. The validation at the optimal parameter setting yields UTS of 711.3214 MPa with a deviation of 1.354% and is observed to be highly consistent with a faster convergence rate in the consistency test suggesting the superiority and robustness of the FL-PSO algorithm.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

EBW:

Electron beam welding

FL:

Fuzzy logic

PSO:

Particle swarm optimization

SMAW:

Shielded metal arc welding

SMSs:

Sustainable manufacturing strategies

PSI:

Process sustainability index

FSW:

Friction stir welding

LBW:

Laser beam welding

GTAW:

Gas tungsten arc welding

LCA:

Life cycle analysis

PAW:

Plasma arc welding

GMAW:

Gas metal arc welding

NSO:

Neutrosophic optimization

ABC:

Artificial bee colony

ANFIS:

Adaptive neuro-fuzzy interface system

ANN:

Artificial neural network

TLBO:

Teaching learning-based optimization

GA:

Genetic algorithm

NSGA-II:

Non dominated sorting genetic algorithm

DEMO:

Differential evolution for multi-objective

ELM:

Extreme learning machine

AHP:

Analytical hierarchy process

RSM:

Response surface methodology

MCDM:

Multi-criteria decision making

GRA:

Grey relational analysis

DoE:

Design of experiments

CCD:

Central composite design

EWDM:

Wire cut electro-discharge machining

FIS:

Fuzzy inference system

(ANOVA).:

Analysis of variance

COG:

Centre of gravity

LSA:

Local sensitivity analysis

GSA:

Global sensitivity analysis

APE:

Average percentage error

PA:

Prediction accuracy

MA:

Model accuracy

I :

Beam current

V :

Accelerating voltage

S :

Welding speed

O :

Beam oscillation

E ne t :

Net input energy

UTS :

Ultimate tensile strength

P w :

Beam power

Y :

Response characteristics

ξ :

Error term

ρ b :

Coefficients of linear terms

ρ bb :

Coefficients of square terms

ρ bc :

Coefficients of interaction terms

R 2 :

Coefficient of determination

R adj 2 :

Adjusted coefficient of determination

p best :

Particles individual best solution

g best :

Population's overall best solution

d :

Number of process variables

c 1 ,c 2 :

Learning factor

r 1, r 2 :

Random numbers

w :

Inertia weight

v ij t :

Updated velocity of particles

\({\widehat{UTS}}_{a}\).:

Achievable or actual (required)UTS

UTS a * :

Highest permitted UTS

ζ I :

Beam current sensitivity

ζ S :

Welding speed sensitivity

ζ V :

Accelerating voltage sensitivity

ζ O :

Beam oscillation sensitivity

\(\overline{Y }\) :

Model predicted value

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Acknowledgements

The authors acknowledge the Director, NERIST for providing the requisite funding, and authorities of IIT, Kharagpur as well as IIT, Guwahati, for facilitating experimental and testing facilities to conduct the research.

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  1. Mechanical Engineering Department, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh, India

    Bishub Choudhury & M. Chandrasekaran

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Choudhury, B., Chandrasekaran, M. Electron Beam Welding Investigation of Inconel 825 and Optimize Energy Consumption Using Integrated Fuzzy Logic-Particle Swarm Optimization Approach. Int. J. Fuzzy Syst. 25, 1377–1399 (2023). https://doi.org/10.1007/s40815-022-01431-8

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