Accuracy Investigation of Fused Deposition Modelling (FDM) Processed ABS and ULTRAT Parts

Bahnini, Insaf; Anghel, Daniel-Constantin; Rizea, A.; Zaman, Uzair Khaleeq uz; Siadat, Ali

doi:10.4018/ijmmme.293225

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Marșavina et al 7 discussed in detail the influence of FDM process molding direction, printer type, layer thickness, sample thickness, and filling density, and the influence of manufacturing parameters on the tensile and fracture properties of PLA specimens was analyzed and studied. After creating the experimental plan and manufacturing samples, Bahnini et al 25 analyzed the gray relational degree and used statistical techniques such as variance analysis and signal‐to‐noise ratio analysis are used to find the optimal parameter combination for two materials that achieve the minimum at all sizes. Murariu et al 26 established a mathematical relationship between the objective function, controllable factors and their interactions through planning experiments and statistical processing of the results, and the tensile strength of 3D printed parts.…”

Section: Introductionmentioning

confidence: 99%

Optimization of polylactic acid 3D printing parameters based on support vector regression and cuckoo search

Yang

Xilin

Yang

et al. 2023

Polymer Engineering & Sci

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Abstract3D printing, also known as additive manufacturing (AM), is evolving from a rapid prototyping tool to a pillar of the Industry 4.0 revolution and widely used in various industries, since it can quickly and efficiently create workpieces with complex structures and integrated functions. After analyzing the 3D printing principle of fused deposition modeling (FDM), this paper proposes a deformation control‐oriented optimization method for process parameters of FDM of the polylactic acid (PLA) materials, based on support vector regression (SVR) and cuckoo search (CS). First, FDM printing principle and its main process parameters were analyzed. Two key parameters, printing temperature and printing speed, were selected for research, with the maximum shrinkage deformation as the workpiece contour accuracy index. Combining Latin Hypercube Sampling (LHS) with Finite Element Analysis (FEA), finite sample sets were generated. Second, learning from the sample data, the SVR surrogate model was built to predict the workpiece contour accuracy, and the nonlinear relationship between FDM process parameters and PLA shrinkage deformation was obtained. Finally, taking the combination of printing temperature and speed as the design variable and minimizing the maximum shrinkage deformation as the optimization objective, the FDM process parameters optimization model was established, and CS was used to search for the optimal parameters combination. Experimental comparison showed that the FEA results and SVR model in this method are correct and effective, and the PLA workpiece shrinkage deformation is smaller under the optimal parameters combination, which effectively improves the shape accuracy of FDM workpieces.Highlights Combining support vector regression (SVR) and cuckoo search (CS) can optimize polymer 3D printing process parameters. Effective finite element model was built for polymer 3D printing. SVR model established by simulation data can predict workpiece deformation. The optimal process parameters were found using CS algorithm.

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Section: Introductionmentioning

confidence: 99%

Optimization of polylactic acid 3D printing parameters based on support vector regression and cuckoo search

Yang

Xilin

Yang

et al. 2023

Polymer Engineering & Sci

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Process parameters optimization for 3d printing of continuous carbon fiber reinforced composite

Xie,

Zhou,

Liu

et al. 2024

Matéria (Rio J.)

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In order to enhance the quality and mechanical properties of 3D printing carbon fiber reinforced composite (CFRP) workpiece, this paper prepares 3D printing CFRP laminates by proposing a layered coherent composite scanning path, and carries out orthogonal tests and single factor tests to disclose the effects of different process parameters (i.e., layer thickness, first layer thickness, nozzle temperature and printing speed) on the mechanical performance of the CFRP workpieces. Moreover, The grey relation analysis method is used to analyze the multi-objective parameters of the orthogonal tests results and the process parameters of 3D printing CFRP are optimized based on the tests results. The research results show that the process parameters of 3D printed CFRP can be ranked as delamination thickness, nozzle temperature, first layer thickness and printing speed, in descending order of the impact on the mechanical property of the CFRP laminates. the optimal process parameters for 3D printing include a layer thickness of 0.25 mm, a first layer thickness e of 0.2 mm, a nozzle temperature of 180°C, and printing speed of 45 mm/s. Under this parameter combination, the tensile strength, bending strength, and the interlaminar shear strength (ILSS) of the samples are 52.77 MPa, 276.63 MPa and 38.56 Mpa respectively, and the grey correlation degree increased to 0.845.

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Accuracy Investigation of Fused Deposition Modelling (FDM) Processed ABS and ULTRAT Parts

Cited by 2 publications

References 23 publications

Optimization of polylactic acid 3D printing parameters based on support vector regression and cuckoo search

Optimization of polylactic acid 3D printing parameters based on support vector regression and cuckoo search

Process parameters optimization for 3d printing of continuous carbon fiber reinforced composite

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