Optimization of FFF Processing Parameters to Improve Geometrical Accuracy and  Mechanical  Behavior of Polyamide 6 Using  Grey Relational Analysis (GRA)

Shakeria, Zohreh; Benfriha, Khaled; Zirak, Nader; Shirinbayan, Mohammadali

doi:10.21203/rs.3.rs-1118150/v1

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Grey relational analysis (GRA) is a multi-response optimization technique that draws on the principles of the Taguchi technique. Studies [ 44 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ] have been conducted recently using GRA to enhance various responses by optimizing processing parameters for 3D printing materials.…”

Section: Introductionmentioning

confidence: 99%

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

et al. 2023

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In recent years, there has been a growing interest in the field of 3D printing technology. Among the various technologies available, fused deposition modeling (FDM) has emerged as the most popular and widely used method. However, achieving optimal results with FDM presents a significant challenge due to the selection of appropriate process parameters. Therefore, the objective of this research was to investigate the impact of process parameters on the tribological and frictional behavior of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) 3D-printed parts. The design of experiments (DOE) technique was used considering the input design parameters (infill percentage and layer thickness) as variables. The friction coefficient values and the wear were determined by experimental testing of the polymers on a universal tribometer employing plane friction coupling. Multi-response optimization methodology and analysis of variance (ANOVA) were used to highlight the dependency between the coefficient of friction, surface roughness parameters, and wear on the process parameters. The optimization analysis revealed that the optimal 3D printing input parameters for achieving the minimum coefficient of friction and linear wear were found to be an infill percentage of 50% and layer thickness of 0.1 mm (for ABS material), and an infill percentage of 50%, layer thickness of 0.15 mm (for PLA material). The suggested optimization methodology (which involves minimizing the coefficient of friction and cumulative linear wear) through the optimized parameter obtained provides the opportunity to select the most favorable design conditions contributing to a more sustainable approach to manufacturing by reducing overall material consumption.

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Section: Introductionmentioning

confidence: 99%

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

et al. 2023

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Multi-response Optimization of FDM Process Parameters Using Taguchi Based Grey Relational Analysis Method

Muhamedagic,

Cekic,

Begic-Hajdarevic

et al. 2023

Lecture Notes in Networks and Systems

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Machine learning in polymer additive manufacturing: a review

Nikooharf,

Shirinbayan,

Arabkoohi

et al. 2024

Int J Mater Form

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Additive manufacturing (AM) has emerged as a commonly utilized technique in the manufacturing process of a wide range of materials. Recent advances in AM technology provide precise control over processing parameters, enabling the creation of complex geometries and enhancing the quality of the final product. Moreover, Machine Learning (ML) has become widely used to make systems work better by using materials and processes more intelligently and controlling their resulting properties. In industrial settings, implementing ML not only reduces the lead time of manufacturing processes but also enhances the quality and properties of produced parts through optimization of process parameters. Also, ML techniques have facilitated the advancement of cyber manufacturing in AM systems, thereby revolutionizing Industry 4.0. The current review explores the application of ML techniques across different aspects of AM including material and technology selection, optimization and control of process parameters, defect detection, and evaluation of properties results in the printed objects, as well as integration with Industry 4.0 paradigms. The progressive phases of utilizing ML in the context of AM, including data gathering, data preparation, feature engineering, model selection, training, and validation, have been discussed. Finally, certain challenges associated with the use of ML in the AM and some of the best-practice solutions have been presented.

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Optimization of FFF Processing Parameters to Improve Geometrical Accuracy and Mechanical Behavior of Polyamide 6 Using Grey Relational Analysis (GRA)

Cited by 5 publications

References 29 publications

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

Multi-response Optimization of FDM Process Parameters Using Taguchi Based Grey Relational Analysis Method

Machine learning in polymer additive manufacturing: a review

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