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Experimental investigation and optimization of the additive manufacturing process through AI-based hybrid statistical approaches

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Abstract

This study investigates and optimizes the tensile, flexural and compressive strengths of polymer parts for local and small industrial components. Extrusion-based additive manufacturing, i.e., fused deposition modeling (FDM) technology is used to manufacture the parts from acrylic butadiene styrene (ABS) material. The samples are produced according to selected process parameters such as road angle, filling percentage, layer size, printing temperature and printing speed, which are varied at three levels. The experiments are designed using a central composite design based on response surface methodology (RSM) in MINITAB software. Mechanical testing is performed using universal testing machines and data are collected for statistical analysis. In addition, hybrid approaches based on artificial intelligence are used for parameter optimization to achieve maximum tensile, bending and compressive strengths. Tensile and bending samples are also subjected to fracture mechanism investigation using scanning electron microscopy (SEM). The results show that the first technique, i.e., response surface methodology and genetic algorithm, resulted in an improvement in tensile, flexural and compressive strength by 2.5%, 7.58% and 8.86%, respectively, compared to the highest values ​​of all experiments. The second approach, i.e., the genetic algorithm for artificial neural networks, provides the tensile, bending and compressive strengths with an improvement of 3.74%, 2.04% and 5.49%, respectively. Similarly, the third technique, adaptive neuro-fuzzy inference system genetic algorithm, yields 4.97%, 6.16%, and 2.62% improvement in tensile, flexural, and compressive strength, respectively. Overall, the results show that the hybrid optimization techniques can provide the desired mechanical strengths of FDM parts for selected applications. The optimized results are confirmed by experiments and scientific methods.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

RSM:

Response surface methodology

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

GA:

Genetic algorithm

TS/σt :

Tensile strength

FS/σf :

Flexural strength

CS/\({\upsigma }_{{\text{c}}}\) :

Compressive strength

UTS:

Ultimate tensile strength

IS:

Impact strength

AG:

Air gap

RA:

Raster angle

RW:

Raster width

NT:

Nozzle temperature

LT:

Layer thickness

FP:

Filling pattern

BO:

Build orientation

CN:

Contour number

ID:

Infill density

PHS:

Print head speed

DOE:

Design of experiment

FEA:

Finite element analysis

CCD:

Central composite design

ANOVA:

Analysis of variance

FFF:

Fused filament fabrication

FDM:

Fused deposition modeling

AM:

Additive manufacturing

ABS:

Acrylic butadiene styrene

AI:

Artificial Intelligence

FCCD:

Face central composite design

CAD:

Computer aided design

UTM:

Universal tensile machine

ASTM:

American society for testing & materials

CW:

Contour width

S:

Speed

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Acknowledgements

We are grateful to the Director MNNIT Allahabad, Prayagraj, Uttar Pradesh, India, for providing necessary laboratory facilities.

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This research did not obtain any grant from funding agencies in the commercial, public, or not-for-profit divisions.

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Authors and Affiliations

  1. Department of Mechanical Engineering, SRMS College of Engineering and Technology, Bareilly, 243202, UP, India

    Saty Dev

  2. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, UP, India

    Rajeev Srivastava

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Rajeev Srivastava: designed the study and guided to perform experiments; Saty Dev: performed the experiments, analyzed the results, writing—original draft preparation. All authors read and commented on the manuscript.

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Dev, S., Srivastava, R. Experimental investigation and optimization of the additive manufacturing process through AI-based hybrid statistical approaches. Prog Addit Manuf (2024). https://doi.org/10.1007/s40964-024-00606-z

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