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Selection of Process Parameters for Near-Net Shape Deposition in Wire Arc Additive Manufacturing by Genetic Algorithm

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Abstract

In wire arc additive manufacturing (WAAM), deposition of multiple beads in multiple layers is required for fabricating any component. This article presents ways to optimize the selection of parameters for near-net shape deposition to minimize void and excess material in WAAM by GA. Initially single-bead geometry model was developed utilizing response surface methodology and experiments were conducted utilizing Box-Behenken design of experiments for deposition of 304L stainless steel in WAAM. Bead geometry parameters, i.e., bead width, bead height and bead cross-sectional area, etc., were expressed in terms of the process parameters like voltage, wire feed rate, torch speed and gas flow rate. Optimal processing conditions and deposition planning were determined utilizing a GA to minimize void and maximize material yield. The proposed approach was validated through deposition of three shapes, i.e., slice, wall and block. The three shapes fabricated with optimal parameters were found to have minimum void and maximum material yield. It has been revealed that optimal bead sizes and degree of overlapping are different for fabricating different geometries. Mechanical testing and metallurgical characterization of the deposited materials exhibited comparable properties of the deposited material to that of the base material. Moreover, double wire feed deposition was seen to deliver higher deposition rate and superior mechanical properties of the deposited material compared to the single wire feed deposition.

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Abbreviations

β :

Regression coefficients

\(\eta\) :

The efficiency of the heat source

a 0 :

The number of center points

B:

Actual width of CAD model

C d :

Center distance (mm)

F :

Wire feed rate (m/min)

G :

Gas flow rate (lpm)

H:

Actual height of CAD model

I :

Current (Amp)

K :

Number of factors

L:

Actual length of CAD model

L B :

The length of each bead (mm)

N :

Number of experiments

N p :

Population size

n :

The number of deposited beads

p c :

Probability of crossover

p m :

Probability of mutation

O l :

Percentage of overlapping (%)

R 2 :

The coefficients of determination

S :

Torch speed (mm/min)

V:

Voltage (V)

V A :

Actual volume of single slice (mm3)

V B :

The volume of each bead (mm3)

V D :

Total volume of a single slice (mm3)

V PP :

Total post-processed volume

V PPH :

Post-processed volume in the deposition direction

V PPV :

Post-processed volume in the built direction

y :

Response

AISI:

American iron and steel institute

AM:

Additive manufacturing

ANN:

Artificial neural network

ANOVA:

Analysis of variance

BA:

Bead cross-sectional area

BH:

Bead height

BW:

Bead width

CAD:

Computer aided design

CNC:

Computer numerical control

CMT:

Cold metal transfer

DWF:

Double wire feed

EDS:

Energy-dispersive spectroscopy

GA:

Genetic algorithm

GMAW:

Gas metal arc welding

GTAW:

Gas tungsten arc welding

MAT:

Medial axis transformation

MAPE:

Mean absolute percentage error

MBML:

Multi-bead multilayer

MBSL:

Multi-bead single layer

MIG:

Metal inert gas welding

PP:

Post-processing

RSM:

Response surface methodology

SEM:

Scanning electron microscopy

SBML:

Single-bead multilayer

SWF:

Single wire feed

TOM:

Tangent overlapping model

VP:

Void percentage

WAAM:

Wire arc additive manufacturing

WEDM:

Wire-cut electrical discharge machining

WH:

Wall height

WW:

Wall width

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Acknowledgments

The authors gratefully acknowledge the support of Mechanical Engineering Department of NIT Patna, TRTC Patna and IIT Kharagpur for providing the experimental facilities for successful completion of this research work.

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  1. Mechanical Engineering Department, National Institute of Technology Patna, Patna, Bihar, 800 005, India

    Ashish Kumar & Kuntal Maji

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Correspondence to Kuntal Maji.

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Kumar, A., Maji, K. Selection of Process Parameters for Near-Net Shape Deposition in Wire Arc Additive Manufacturing by Genetic Algorithm. J. of Materi Eng and Perform 29, 3334–3352 (2020). https://doi.org/10.1007/s11665-020-04847-1

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  • DOI: https://doi.org/10.1007/s11665-020-04847-1

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