Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool

Chhabra, Deepak; Deswal, Sandeep; Kaushik, Ashish; Garg, Ramesh Kumar; András, Kovács; Khargotra, Rohit; Singh, Tej

doi:10.1016/j.polymertesting.2022.107910

Cited by 31 publications

(6 citation statements)

References 32 publications

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“…The Adaptive Neuro Fuzzy Inference System (ANFIS) is a sophisticated computational tool that integrates fuzzy systems and neural networks to approximate the correlations between input and output variables, with a special focus on intricate and non-linear situations [42]. Chhabra et al [43] conducted a study to investigate the effects of various parameters in the process of 3D printing on carbon-reinforced polyamide composites. The researchers employed a hybrid approach, specifically the integration of genetic algorithms with ANFIS, referred to as GA-ANFIS, in order to forecast the wear characteristics of 3D printed composites by considering the printing factors.…”

Section: Introductionmentioning

confidence: 99%

Developing Artificial Intelligence Models for Predicting the Tribo-Mechanical Properties of HDPE Nanocomposite Used in Artificial Hip Joints

Fouly,

Taha,

Albahkali

et al. 2024

IEEE Access

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Hip joint fractures are a serious and prevalent health concern, especially among the elderly. The forces that the lower limbs must withstand are quite strong and can exceed many times the person body weight, especially in the femur. The selection of materials for joint replacements depends heavily on mechanical and tribological properties like strength, hardness, and wear rate. HDPE has been effective as an external prosthetic foot, but its mechanical and tribological constraints have made it difficult to employ as a part of an artificial implant. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) models are utilized to forecast HDPE nanocomposites mechanical and tribological characteristics. The model incorporates various weight fractions of graphene and relies on data obtained from experimental tests. These nanocomposites are prepared with various compositions, and their structure and morphology are evaluated using physiochemical characterization. Compared to pure HDPE, adding graphene enhanced the hardness, strength, and elastic modulus of the HDPE. The nanocomposite with a composition of 2 wt.% graphene showed better mechanical properties compared with pure HDPE. In addition, a finite element model is created to simulate the hip joint and assess the HDPE nanocomposite load-bearing capacity under actual loading. The outcomes of the simulation analysis are consistent with the experiment findings. In addition, in comparison to pure HDPE, the nanocomposite containing 0.5 wt.% graphene showed better tribological performances. Finally, the microscopic examination demonstrated how the additives weight fraction affects the HDPE nanocomposites wear mechanism. Eventually, the ANFIS models proved its ability to predict the tribomechanical properties of HDPE nanocomposite with an error that didn't exceed 3%.

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Section: Introductionmentioning

confidence: 99%

Developing Artificial Intelligence Models for Predicting the Tribo-Mechanical Properties of HDPE Nanocomposite Used in Artificial Hip Joints

Fouly,

Taha,

Albahkali

et al. 2024

IEEE Access

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“…Pin-on-disk equipment is used in this method. It has been put to use in the past to conduct tests on a variety of 3D printing materials [ 50 ]. Utilizing the Instron 8801 Universal Testing Machine (UTM), Norwood, USA, the tensile and flexural tests were conducted.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Printing Process Variables and the Effect of Post-Heat Treatments on the Mechanical Properties of Extruded Polylactic Acid–Aluminum Composites

Ganapathy,

Sakthivel,

Kandasamy

et al. 2023

Polymers

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Polymer extrusions are employed in the fabrication of crucial parts for automotive, aerospace, and other mechanical applications. The use of fillers and microfibers is contributing to the advancement of material extrusion polymers. In order to enhance their mechanical characteristics, printed materials undergo a post-heating process utilizing microwaves. Specimens were fabricated using polylactic acid filaments containing 2 wt% aluminum. Two sets of specimens were fabricated and subjected to testing in order to evaluate the features of extruded specimens and specimens that underwent post-heating. In terms of mechanical performance, specimens subjected to post-heating exhibited superior results compared to specimens merely subjected to extrusion. The tensile, flexural, and Shore D hardness properties of the specimens exhibited improvements of 5.07, 6.16, and 1.32%, respectively, after being subjected to heating. Furthermore, the wear rate decreased by 13.58 percent. The results of the regression analysis indicate that the print angle and the air gap exhibit the greatest influence on the observed responses. The validation test outcomes exhibit a high level of concordance with the predicted findings. The mechanical and surface properties of components extruded with filler-added material are enhanced by subsequent heating.

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“…They affect the fatigue strength of products and can spread in 3D printed object during operation and under load (Fig. 14) [62,[117][118][119][120][121]. Cracks propagate in the material along the weakest points.…”

Section: Cracksmentioning

confidence: 99%

Defects in 3D Printing and Strategies to Enhance Quality of FFF Additive Manufacturing. A Review

Erokhin,

Naumov,

Ananikov

2023

Preprint

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Additive manufacturing technologies (or 3D printing) have emerged as potent tools in the creation of a diverse array of objects, promising a paradigm shift in production methodologies across industries. However, the benefits of these technologies can be diminished by the use of suboptimal parameters or inferior materials, leading to defects that significantly degrade the quality and functionality of the resulting products. An incomplete understanding of defect formation keeps under the formulation of effective preventative strategies. In light of this, our review provides a comprehensive exploration of defects that arise during the Fused Filament Fabrication (FFF)—one of the most prevalent 3D printing methods. The defects are systematically classified according to several key characteristics, including size, type, mode of occurrence, and location. Each common defect is extensively discussed, detailing its external manifestation, root causes, the impact on the properties of printed parts, and potential preventative measures. Our findings unveil the complex interplay of material properties, printing parameters, and cooling dynamics in the defect formation process. This classification holds significant practical relevance, providing a solid foundation for the development of strategies for defect minimization and quality improvement in 3D printed products. It offers valuable insights for a broad audience, including researchers exploring additive manufacturing technologies, 3D printing engineers, 3D printer operators, and quality assurance (QA) engineers involved in production quality control. Furthermore, our review delineates the path for future research in this domain. There is a crucial need for the development of advanced machine learning and artificial intelligence models that can predict defect formation based on given printing parameters and material properties. Future investigations should also focus on the discovery of novel materials and refining of printing parameters to achieve superior quality of FFF 3D printed products. This review serves as a cornerstone for these future advancements, promoting a deeper understanding of defect formation and prevention in additive manufacturing.

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Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool

Cited by 31 publications

References 32 publications

Developing Artificial Intelligence Models for Predicting the Tribo-Mechanical Properties of HDPE Nanocomposite Used in Artificial Hip Joints

Developing Artificial Intelligence Models for Predicting the Tribo-Mechanical Properties of HDPE Nanocomposite Used in Artificial Hip Joints

Optimization of Printing Process Variables and the Effect of Post-Heat Treatments on the Mechanical Properties of Extruded Polylactic Acid–Aluminum Composites

Defects in 3D Printing and Strategies to Enhance Quality of FFF Additive Manufacturing. A Review

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