A new boundary interlock geometry design pattern to strengthen FDM part multi-material interface

Kakaraparthi, Shivaram; Tatara, R. A.; Chen, Niechen

doi:10.1016/j.mfglet.2022.07.082

Cited by 5 publications

(2 citation statements)

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“…Similarly, Kakaraparthi et al studied the mechanical properties of multi-material 3D printed objects by focusing on the interface zone formed at the geometric boundaries where different materials meet. They compared the mechanical performance of four different test samples and demonstrated the positive impact of inserting boundary interlock geometry, which resulted in improved tensile strength [38].…”

Section: Geometry Analysis In Ammentioning

confidence: 99%

Additive Manufacturing Toolpath Planning Optimization for Energy Sustainability Improvement

Somade,

Chen

2023

Preprint

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As the application of Additive Manufacturing (AM) expands beyond prototyping, the issues of energy consumption in AM becomes increasingly significant, particularly when it is adopted at an industrial production scale or used under resource-restricted conditions. The energy consumption of an AM process is influenced by various factors. In material extrusion type AM, for example, it includes bed heating, filament extrusion, material infill, component cooling, etc. All these factors are further determined by the equipment and the toolpath for a specific printing task. Although, build orientation and tool-path direction are frequently used to optimize part and process attributes, there is a need for more comprehensive research to understand how the choice of toolpath pattern affects the energy attributes of an AM process. This research aims to develop a toolpath creation strategy for AM tasks operating under limited energy supply conditions. In an AM process, factors such as motor axis acceleration/deceleration and the total number and length of line segments on a path have significant impacts in determining on the amount of energy used to perform the printing task. To study the impact of such factors, we will develop a model that computes the energy consumption of an AM process based on the toolpath, then analyze the impacts of part design geometry on the toolpath generation. Finally, we will create a strategy to guide the generation of toolpath for specific part geometries to effectively control the total energy requirement during the AM process.

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Section: Geometry Analysis In Ammentioning

confidence: 99%

Additive Manufacturing Toolpath Planning Optimization for Energy Sustainability Improvement

Somade,

Chen

2023

Preprint

Self Cite

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“…Yadav et al 23 using genetic algorithm‐artificial neural network to train and optimize the filling rate, filling pattern, printing temperature process parameters, and combined with experiments for verification. Kakaraparthi et al 24 started with the molding process of 3D printing products and analyzed the influence of filling density, support structure and other parameters on the mechanical properties of products. 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.…”

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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Cooperative enhancement of multi-material interface strength by mechanical interlocking structures and FDM path planning

Liu,

Xu,

Cao

et al. 2024

Int J Adv Manuf Technol

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A new boundary interlock geometry design pattern to strengthen FDM part multi-material interface

Cited by 5 publications

References 0 publications

Additive Manufacturing Toolpath Planning Optimization for Energy Sustainability Improvement

Additive Manufacturing Toolpath Planning Optimization for Energy Sustainability Improvement

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

Cooperative enhancement of multi-material interface strength by mechanical interlocking structures and FDM path planning

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