Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

Chohan, Jasgurpreet Singh; Mittal, Nitin; Kumar, Raman

doi:10.1007/s42452-020-03818-4

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…In addition to the above-discussed methods, there are also other methods used for optimizing process parameters of FDM. For instance, enabled teaching learning based algorithms [75] and particle swarm optimization [76][77][78] were employed to optimize the process parameters of FDM in order to enhance the quality of the manufactured parts.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

Batu,

Lemu,

Shimels

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Manufacturing is the foundation of any industrialized country and involves making products from raw materials using various processes. Additive manufacturing (AM) was originally created as a method for swift prototyping, allowing the visualization, testing, and validation of a design prior to final production for end-users. FDM is the most commonly used additive manufacturing process for constructing products and prototypes. It encompasses numerous process parameters that impact the quality of manufactured products. Properly selecting these process parameters is crucial for producing products at a lower cost while enhancing mechanical properties, build time, and part quality, among other factors. Therefore, in the past, researchers have optimized the process parameters to achieve the desired product outcomes. In the present study, we provide an overview of FDM process parameters and review various design optimization methods. We present several experimental designs, such as the Taguchi method, response surface methodology, and design of experiments, as well as computational approaches like artificial intelligence, and machine learning.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

Batu,

Lemu,

Shimels

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…This outcome can be attributed to the reduction in thermal stress due to the higher glass transition temperature exhibited by the printed samples compared to the filament used. Chohan [27] utilized the Learning Enthusiasm based teaching-learning algorithm (LebTLBO) to optimize four printing factors and enhance the warping deformation of ABS components. In a separate study, Messimer et al [28] examined the warping deformation of ten different materials by two types of heated plates: an aluminum-polycarbonate (AL-PC) composite and pure glass or aluminum plate.…”

Section: Introductionmentioning

confidence: 99%

Thermal Evaluation of Material Extrusion Process Parameters and Their Impact on Warping Deformation

2023

jjmie

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Warping deformation poses a significant challenge in the material extrusion process of ABS additive manufacturing, impacting dimensional accuracy and part quality. In this study, we aimed to optimize the printing factors to minimize warping deformation and enhance the overall performance of ABS-printed parts. The studied parameters included bed temperature, printing speed, chamber temperature, and printing temperature. To overcome the limitations of time-consuming and costly laboratory experiments, a simulation-based approach using numerical techniques and the Digimat-AM software's finite element model was employed to visualize and analyze warping deformation. The Taguchi technique was utilized to design a series of experiments, yielding valuable data for statistical analysis and optimization. The results highlighted the significant influence of bed temperature on warping deformation, followed by chamber temperature, printing temperature, and printing speed. Through systematic analysis, the "optimal" parameter settings were determined. These findings offer valuable insights for process optimization, enabling improved dimensional accuracy and reduced warping deformation in ABS additive manufacturing. By combining numerical simulation and the Taguchi method, this investigation gives a practical and efficient method for enhancing the performance of material extrusion processes, ensuring high-quality printed parts in the field of additive manufacturing.

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“…The significance of adeptly adjusting FDM process parameters for specific goals was underscored by Rayegani and Onwubolu [10], who effectively achieved desired tensile strength using the Differential Evolution (DE) optimization algorithm. Chohan et al [11] have employed the advanced Learning Enthusiasm-based teachinglearning algorithm (LebTLBO). The study aimed to identify optimal input parameters for minimizing dimensional accuracy deviations, warp deformations, and manufacturing time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Tensile Strength and Material Consumption of FDM Parts using Multi-Objective Symbiotic Organisms Search (MOSOS)

Saad,

Zakaria,

Baharudin

et al. 2023

Preprint

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Fused Deposition Modeling (FDM) has emerged as a prominent method for rapid prototyping in Additive Manufacturing (AM) due to its ability to construct intricate geometries. Nevertheless, optimizing FDM process parameters to attain desired part characteristics remains a challenge. This study presents comprehensive findings from an experimental investigation, comparing results obtained through simulations and practical experiments, within the framework of multi-objective optimization for FDM. The core objectives of this analysis center on material consumption and tensile strength, both pivotal in FDM applications, while exploring the efficacy of Multi-Objective Symbiotic Organisms Search (MOSOS) in addressing the trade-off between these objectives. This study utilizes advanced experimental design techniques, specifically Response Surface Methodology (RSM) in conjunction with Face-Centered Central Composite Design (FCCD), to meticulously conduct experiments. These experiments are crucial in the creation of precise regression models that serve as objective functions for the MOSOS algorithm. The significant outcome of this study is the identification of a trade-off relationship between material consumption and tensile strength in FDM. The research revealed that achieving higher tensile strength in FDM requires an increase in material consumption, while reducing material usage comes at the cost of compromised tensile strength. The study also pinpointed an optimal configuration at the fourth index, consisting of specific parameter settings such as a layer thickness of 0.25 mm, printing speed of 60 mm/s, infill density of 20%, and print temperature of 213.26°C, which strikes a satisfactory balance between material efficiency and mechanical performance.

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Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

Cited by 10 publications

References 29 publications

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

Thermal Evaluation of Material Extrusion Process Parameters and Their Impact on Warping Deformation

Multi-Objective Optimization of Tensile Strength and Material Consumption of FDM Parts using Multi-Objective Symbiotic Organisms Search (MOSOS)

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