Optimization of 3D Printing Process Parameters of Polylactic Acid Filament Based on the Mechanical Test

Raja, S.; Agrawal, Anant Prakash; Patil, Pravin P.; Thimothy, P.; Capangpangan, Rey Y.; Singhal, Piyush; Wotango, Mulugeta Tadesse

doi:10.1155/2022/5830869

Cited by 30 publications

(16 citation statements)

References 11 publications

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“…The main operation in laser powder bed fusion includes input, process, and output which allows the adjustment of process parameters like scanning speed, laser, power, and other important parameters, physical phenomenon and final product quality as per the requirement [56]. Table 1 represents the processing parameters used for different grades of maraging steel in LPBF process [57][58][59][60][61][62].…”

Section: Corrosion Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Influence of in-situ process parameters, post heat treatment effects on microstructure and defects of additively manufactured maraging steel by laser powder bed fusion—A comprehensive review

2024

Phys. Scr.

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The laser powder bed fusion LPBF method in additive manufacturing for metals have proven to produce a final product with higher relative density, when compare to other metal additive manufacturing processes like WAAM, DED and it takes less time even for complex designs. Despite the use of many metal-based raw materials in the LPBF method for production of products. Maraging steel (martensitic steel) is used in aeronautical and aircraft applications in view of its advantages including low weight, high strength, long-term corrosion resistance, low cost, availability, and recyclability. A research gap concerns the selection of design, dimension, accuracy, process parameters according to different grades, and unawareness of various maraging steels other than specific maraging steels. In this comprehensive review, the research paper provides information about on LPBF maraging steel grades, their process parameters and defects, microstructure characteristics, heat treatments, and the resulting mechanical characteristics changes. In addition, detailed information about the aging properties, fatigue, residual and future scope of different maraging steel grades in LPBF for various applications are discussed.

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Section: Corrosion Studiesmentioning

confidence: 99%

“…Process parameters for different grades of maraging steel utilized in LPBF process[57][58][59][60][61][62].…”

mentioning

confidence: 99%

Influence of in-situ process parameters, post heat treatment effects on microstructure and defects of additively manufactured maraging steel by laser powder bed fusion—A comprehensive review

2024

Phys. Scr.

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“…DOE is used in many fields like agriculture [30], engineering [31], and defense [32]. Three such DOE strategies have been used by previous researchers like examining multiple factors simultaneously [33] and examining multiple factors together [34], and one factor is examined at a time [35]. Anderson and McLean [36] have classified the DOE as the factorial design (finding main effects on prices), response surface design (finding the maximum and minimum response of various factors), mixture design (finding ideal proportion in mixture processes), and optimal design (used to find sufficient details).…”

Section: Doe (Design Of Experimentsmentioning

confidence: 99%

Challenges and Opportunities in Additive Manufacturing Polymer Technology: A Review Based on Optimization Perspective

Raja

Rajan

2023

Advances in Polymer Technology

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In the emerging modern technology of additive manufacturing, the need for optimization can be found in literature in many places. Additive manufacturing (AM) is making an object layer by layer directly from digital data. Previous works of literature have classified additive manufacturing processes into seven types. However, there is a lack of comprehensive review describing the optimization challenges and opportunities in the material extrusion process (polymer technology) and also the need for FDM polymer materials application in impeller making. In this review paper, a specific optimization method called multicriteria decision-making (MCDM) from the mathematical programming technique used in additive manufacturing polymer technology (AMPT) is discussed. The other topics such as different types of optimization techniques, applications of different MCDM tools and their applications in different fields including AM, and the optimization challenges and opportunities in AMPT particularly impeller application are discussed.

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“…Singh et al 17 optimized the 3D printing process parameters for the wire‐arc additive manufacturing technology, and combined the response surface method and the central composite design method to determine the optimal. Raja et al 18 determined the best combination of process parameters by studying the effects of parameters such as printing speed, printing temperature, layer height, and filling rate on the mechanical properties of the part. Xu et al 19 used PLA to test and analyze its mechanical properties by optimizing the combination of process parameters such as printing speed, layer thickness, and filling rate in FDM, and determined the main process parameters that affect the mechanical properties.…”

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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Optimization of 3D Printing Process Parameters of Polylactic Acid Filament Based on the Mechanical Test

Cited by 30 publications

References 11 publications

Influence of in-situ process parameters, post heat treatment effects on microstructure and defects of additively manufactured maraging steel by laser powder bed fusion—A comprehensive review

Influence of in-situ process parameters, post heat treatment effects on microstructure and defects of additively manufactured maraging steel by laser powder bed fusion—A comprehensive review

Challenges and Opportunities in Additive Manufacturing Polymer Technology: A Review Based on Optimization Perspective

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

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