Analysis on Dimensional Accuracy of 3D Printed Parts by Taguchi Approach

Ahmad, Mohd Nazri; Mohamad, Abdul Rashid

doi:10.1007/978-981-15-7309-5_22

Cited by 11 publications

(10 citation statements)

References 14 publications

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“…Akande (2015) used Taguchi design for the assessment of DA and surface finish, and for their optimization, Desirability Function Analysis (DFA) was adopted. Ahmad and Mohamad (2021) used Taguchi’s approach to detect a relationship between the FDM process parameters and the accuracy of the printed parts. The results showed that the Taguchi method is promising for optimizing and predicting the accuracy of parts made on FDM machines.…”

Section: Methodsmentioning

confidence: 99%

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

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Purpose Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

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Section: Methodsmentioning

confidence: 99%

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

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“…According to Kumbhar et al, [10], post-processing procedures are typically utilized to increase surface smoothness. Ahmad et al, [11][12] has discovered the optimum parameters of FDM using ABS material and used PolyJet 3D printer to print 30 samples of molar teeth. The primary advantages of this FDM method are ease of access, lower equipment costs, and multicolored product printing; when compared to other RP processes, this approach is less expensive and more cost-effective.…”

Section: Fdm Technologymentioning

confidence: 99%

Rheological Properties of Natural Fiber Reinforced Thermoplastic Composite for Fused Deposition Modeling (FDM): A Short Review

Ahmad

Ishak

Taha

et al. 2022

ARFMTS

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In the development and manufacturing industries, fused deposition modeling (FDM) receives the greatest attention. It is the most important additive manufacturing (AM) technique, which refers to the process of depositing multiple layers of material in a computer-controlled environment to form a three-dimensional product. Research is presently focusing on the development of 3D printed bio-composite polymers with improved performance. Many studies on the development of new composite materials using natural fiber as a feedstock filament for FDM have recently been published. As a result, conducting a rheology characteristics analysis of new composite materials made from natural resources is required. Its major purpose is to describe the flow behavior of the fiber composite material and determine the optimal melting temperature for the extrusion process of producing wire filament. Thus, this paper focuses on rheological properties of fiber-reinforced thermoplastic composite for FDM.

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“…The FDM process employs various processing parameters including layer thickness, infill density, printing speed, raster angle, raster width, build orientation, infill pattern, and bed temperature. The effect of FDM process parameters on the mechanical behavior of printed parts has been explored by multiple researchers [28][29][30][31]. Parts made using FDM materials are more easily shaped because of their thermal and rheological qualities.…”

Section: Introductionmentioning

confidence: 99%

Advances in fused deposition modeling on process, process parameters, and multifaceted industrial application: a review

Enyan,

Amu-Darko,

Issaka

et al. 2024

Eng. Res. Express

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In the current era of intense competition, optimizing the design and manufacturing processes through time reduction measures can prove to be advantageous. The objective of profit maximization is a crucial consideration across diverse industries. The utilization of 3D printing technology has been crucial in producing personalized items at a reduced expense and with increased efficiency across various industrial domains. Fused deposition modeling (FDM) is an effective and cost-efficient Additive Manufacturing (AM) technique utilized in diverse engineering applications. The introduction of the FDM process occurred in the early 1990s by Stratasys Inc., a company based in the United States. Since then, FDM technology has become a prevalent AM process widely utilized for producing functional prototypes with precision and safety. To advance the technology, it necessitates a comprehensive understanding of the process, involved parameters, applications, and trends, as well as identifying challenges and prospects. This review explores the fundamental underpinnings of the FDM process, detailing production stages, parameters, applications, and challenges and prospects. It critically evaluates process parameters and their influence on the FDM process, drawing insights from a synthesis of relevant studies. The paper also provides an in-depth examination of FDM applications across various domains, evaluating and summarizing associated studies. The study draws upon prior research and highlights the evolving landscape of FDM application trends and current trends, providing a solid foundation for readers to grasp the topic.

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Analysis on Dimensional Accuracy of 3D Printed Parts by Taguchi Approach

Cited by 11 publications

References 14 publications

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Rheological Properties of Natural Fiber Reinforced Thermoplastic Composite for Fused Deposition Modeling (FDM): A Short Review

Advances in fused deposition modeling on process, process parameters, and multifaceted industrial application: a review

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