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Ameliorating Surface Roughness and Tensile Strength of ASA Fabricated Parts by Analyzing Significant FDM Printing Parameters Using Response Surface Methodology

Pardeep Sharma, Sumit Gahletia, Kunal Bhardwaj

Abstract


The constantly growing demand for acrylonitrile styrene acrylate (ASA) polymer in the automotive market to its use for fabricating different components such as dashboards, the interior, lights, and electrical components challenges manufacturers to continuously optimize its mechanical properties to meet consumer demand. Despite the widespread availability of three-dimensional (3D) printing techniques, research on the most efficient and cost-effective methods of producing ASA-printed components is still indispensable. Therefore, the purpose of this study is to discuss the impact of changing a few key process parameters of ASA fused deposition modeling 3D printing for enhanced mechanical properties. Tensile strength and surface roughness of fabricated parts were analyzed to examine the effect of significant process factors like infill density, infill pattern, and layer height. For the tensile strength and surface roughness testing, a total of 39 specimens were fabricated using the design of experiments software and ASTM D-638 Type-IV specimens. A total of 13 samples representing each infill pattern were fabricated with varying layer heights and infill densities for testing purposes. For the sparse infill pattern, the best results can be achieved with a layer thickness of 0.229 mm and an infill density of 40.88%. Layer thicknesses of 0.206 mm and 0.178 mm, with corresponding infill densities of 51.94% and 50.17%, yield the optimal parameters in the case of double sprause and hexagram infill patterns, respectively. Using a hexagram infill pattern, the maximum tensile strength of 8.197 MPa and surface roughness of 4.90 microns is achieved and the same has been validated experimentally.

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References


Vaidya N, Awachar S, Nagi HS. Use of additive manufacturing in product design & development. SAE Technical Paper 2023-01-0892, 2023. doi: 10.4271/2023-01-0892.

Ranganathan S, Pradeep C. The effect of print orientation and infill density for 3D printing on mechanical and tribological properties. SAE Technical Paper 2020-28-0411, 2020. doi: 10.4271/2020-28-0411.

Garcia J, Harper R, Bradley C, Schmidt J, Lu YC. Anisotropic material behavior and design optimization of 3D printed structures. SAE Technical Paper 2020-01-0228, 2020. doi: 10.4271/2020-01-0228.

Sharma A, Chhabra D, Sahdev R, Kaushik A, Punia U. Investigation of wear rate of FDM printed TPU, ASA and multi-material parts using heuristic GANN tool. Mater Today Proc. 2022; 63: 559– 565.

Chhabra D, Deswal S, Kaushik A, Garg RK, Kovács A, Khargotra R, Singh T. Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool. Polym Test. 2023; 118: 107910.

Gahletia S, Kaushik A, Garg RK, Chhabra D, Kovács A, Khargotra R, Singh T. Fabrication and analysis of micro carbon fiber filled nylon filament reinforced with Kevlar, fiberglass, and HSHT fiberglass using dual extrusion system. Mater Today Commun. 2023; 35: 106075.

Yadav M, Kaushik A, Garg RK, Yadav M, Chhabra D, Rohilla S, Sharma H. Enhancing dimensional accuracy of small parts through modelling and parametric optimization of the FDM

D printing process using GA-ANN. In: 2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), Pathum Thani, Thailand, December 23–25, 2022. pp. 89–94.

Srivastava M, Rathee S. Optimisation of FDM process parameters by Taguchi method for imparting customised properties to components. Virtual Phys Prototyp. 2018; 13 (3): 203–210.

Deswal S, Narang R, Chhabra D. Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness. Int J Interact Des Manuf.

; 13: 1197–1214.

Yadav D, Chhabra D, Gupta RK, Phogat A, Ahlawat A. Modeling and analysis of significant process parameters of FDM 3D printer using ANFIS. Mater Today Proc. 2020; 21: 1592–1604.

Phogat A, Chhabra D, Sindhu V, Ahlawat A. Analysis of wear assessment of FDM printed specimens with PLA, multi-material and ABS via hybrid algorithms. Mater Today Proc. 2022; 62: 37–43.

Solomon IJ, Sevvel P, Gunasekaran J. A review on the various processing parameters in FDM. Mater Today Proceed. 2021; 37: 509–514.

Kumar SR, Sridhar S, Venkatraman R, Venkatesan M. Polymer additive manufacturing of ASA structure: influence of printing parameters on mechanical properties. Mater Today Proc. 2021; 39:

–1319.

Nuñez PJ, Rivas A, García-Plaza E, Beamud E, Sanz-Lobera A. Dimensional and surface texture characterization in fused deposition modelling (FDM) with ABS plus. Procedia Eng. 2015; 132:

–863.

Vishwas M, Basavaraj CK. Studies on optimizing process parameters of fused deposition modelling technology for ABS. Mater Today Proc. 2017; 4 (10): 10994–11003.

Lluch-Cerezo J, Benavente R, Meseguer MD, Gutiérrez SC. Study of samples geometry to analyze mechanical properties in fused deposition modeling process (FDM). Procedia Manuf. 2019; 41:

–897.

Qattawi A. Investigating the effect of fused deposition modeling processing parameters using Taguchi design of experiment method. J Manuf Process. 2018; 36: 164–174.

Camposeco-Negrete C. Optimization of printing parameters in fused deposition modeling for improving part quality and process sustainability. Int J Adv Manuf Technol. 2020; 108: 2131–2147.

El Magri A, Ouassil SE, Vaudreuil S. Effects of printing parameters on the tensile behavior of 3D‐ printed acrylonitrile styrene acrylate (ASA) material in Z direction. Polym Eng Sci. 2022; 62 (3):

–860.

Guessasma S, Belhabib S, Nouri H. Microstructure, thermal and mechanical behavior of 3D printed acrylonitrile styrene acrylate. Macromol Mater Eng. 2019; 304 (7): 1800793.

Samykano M, Selvamani SK, Kadirgama K, Ngui WK, Kanagaraj G, Sudhakar K. Mechanical property of FDM printed ABS: influence of printing parameters. Int J Adv Manuf Technol. 2019;

: 2779–2796.

Moorthy ES, Balasubramanian A, Kumaran PK, Baig MB, Alagappan S, Moorthy M. Effect of process parameters on tensile strength and surface quality of PLA-ABS part produced by fused

deposition modeling. Indian J Eng Mater Sci. 2021; 28 (3): 300–310.

Rossi S, Puglisi A, Benaglia M. Additive manufacturing technologies: 3D printing in organic synthesis. ChemCatChem. 2018; 10 (7): 1512–1525.

Klippstein H, Diaz De Cerio Sanchez A, Hassanin H, Zweiri Y, Seneviratne L. Fused deposition modeling for unmanned aerial vehicles (UAVs): a review. Adv Eng Mater. 2018; 20 (2): 1700552.

Sandhu GS, Boparai KS, Sandhu KS. Influence of slicing parameters on selected mechanical properties of fused deposition modeling prints. Mater Today Proc. 2022;48: 1378–1382.

Sharma P, Sahoo BB. An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine. Int J Hydrogen Energy. 2022; 47 (44): 19298–19318.

Yadav M, Yadav D, Garg RK, Gupta RK, Kumar S, Chhabra D. Modeling and optimization of piezoelectric energy harvesting system under dynamic loading. In: Sikarwar BS, Sundén B, Wang

Q, editors. Advances in Fluid and Thermal Engineering: Select Proceedings of FLAME 2020. Singapore: Springer; 2021. pp. 339–353.

Yadav M, Kumar S, Kaushik A, Chhabra D. Piezo-beam structure in a pipe with turbulent flow as energy harvester: mathematical modeling and simulation. J Inst Eng (India) Series D. 2022: 1–4.

doi: 10.1007/s40033-022-00440-z.

Sharma P. Prediction-optimization of the effects of di-tert butyl peroxide-biodiesel blends on engine performance and emissions using multi-objective response surface methodology. J Energy Resour

Technol. 2022; 144 (7): 072301.

Ghadai RK, Kalita K, Gao XZ. Symbolic regression metamodel based multi-response optimization of EDM process. FME Trans. 2020; 48 (2): 404–410.

Rahman SM, Sharma P, Said Z. Application of response surface methodology based D-optimal design for modeling and optimisation of osmotic dehydration of zucchini. Digital Chem Eng. 2022; 4: 100039.

Kumar L, Kumar K, Chhabra D. Experimental investigations of electrical discharge micro-drilling for Mg-alloy and multi-response optimization using MOGA-ANN. CIRP J Manuf Sci Technol.

;38 : 774–786.


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