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A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters

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Abstract

Fused deposition modeling (FDM), a well known 3D printing technology is widely used in various sorts of industrial applications because of its ability to manufacture complex objects in the stipulated time. However, the proper selection of input process parameters in FDM is a tedious task that directly affects the part performance. Here, in this work, the research efforts have been made to optimize the FDM process parameters in order to find out the best parameter setting as per the mechanical and surface quality perspectives by using hybrid particle swarm and bacterial foraging optimization (PSO–BFO) evolutionary algorithm. Taguchi L18 orthogonal array was used for the development of acro-nitrile butadiene styrene based 3D components by considering layer thickness, support material, model interior and orientation as a process parameters. Further, the relationships among selected FDM process parameters and output responses such as hardness, flexural modulus, tensile strength and surface roughness were established by using linear multiple regression. Then, the effects of individual process parameters on selected response parameters were examined by signal to noise ratio plots. Finally, a multi-objective optimization of process parameters has been performed with hybrid PSO–BFO, general PSO and BFO algorithm, respectively. The overall results reveal that the layer thickness of 0.007 mm, support material type sparse, part orientation of 60\({^\circ }\) and model interior of high density helps in achieving desired performance level.

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Abbreviations

FDM:

Fused deposition modeling

BFO:

Bacterial foraging optimization

S/N:

Signal to noise

FM:

Flexural modulus

Ra:

Surface roughness

MI:

Model interior

SM:

Support material

n:

No. of bacteria in population

Nr:

No. of reproduction steps

Ns:

No. of swim

Pbest:

Particle best position (PSO)

\(w_{max}\) :

Maximum inertia weight (PSO)

\(iter_{curr}\) :

Current iteration (PSO)

c(I):

Length of unit walk (BFO)

\(Jcc \left( \theta ,P\left( {j,k,l} \right) \right) \) :

Cost function value (BFO)

PSO:

Particle swarm optimization

ABS:

Acro-nitrile butadiene styrene

H:

Hardness

TS:

Tensile strength

LT:

Layer thickness

PO:

Part orientation

AM:

Additive manufacturing

Ned:

No. of elimination–dispersion

Nc:

No. of chemo-tactic steps

Pcd:

Dispersion probability

Gbest:

Global best position (PSO)

\(w_{min} \) :

Minimum inertia weight (PSO)

\(iter_{total}\) :

Total number of iteration (PSO)

\(\phi \left( i \right) \) :

Direction angle of the jth step (BFO)

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Acknowledgements

The authors acknowledge the “Institute for Auto Parts and Hand Tools Technology, Ludhiana” and “Central Institute of Plastics Engineering and Technology, Amritsar” for helping in carrying out the experiments.

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Correspondence to Munish Kumar Gupta.

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Raju, M., Gupta, M.K., Bhanot, N. et al. A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters. J Intell Manuf 30, 2743–2758 (2019). https://doi.org/10.1007/s10845-018-1420-0

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  • DOI: https://doi.org/10.1007/s10845-018-1420-0

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