Enhancement of Anisotropic Strength of Fused Deposited ABS Parts by Genetic Algorithm

Chockalingam, K.; Jawahar, N.; Praveen, J. Peter

doi:10.1080/10426914.2015.1127949

Cited by 47 publications

(23 citation statements)

References 17 publications

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“…Based on previous studies [2,4,12,14,21,22] and equipment manufacturer recommendation (Stratasys), the process parameters at different levels (see Table 1) were selected as input parameters to investigate their influences on the mechanical characteristics of parts fabricated by FDM. The variable 'number of contours' has not been examined in the literature for its impact on the mechanical performance.…”

Section: Selection Of Parameters and Their Working Rangementioning

confidence: 99%

“…It was reported that the build direction is the most significant factor that contributes to enhance the surface roughness and mechanical performance. A study performed by Chockalingam et al [4] on the effect of operating conditions on the prototype strength and density of FDM built parts using face centered central composite design concluded that the anisotropic strength of FDM fabricated parts depends heavily on process parameters. The work of Gurrala et al [5] revealed that the build orientation plays an important role in improving the mechanical property and part shrinkage of the FDM build parts.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to static mechanical loads as studied in [3][4][5][6][7][8][9][10][11][12][13][14], FDM parts also operate under cyclic and dynamic loading conditions and millions of stress cycles in various industries such as in vibrating machinery and transportation machines. It is vital to understand the dynamic mechanical response of these materials to produce reliable, functional and economical products.…”

Section: Introductionmentioning

confidence: 99%

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Characterization and dynamic mechanical analysis of PC-ABS material processed by fused deposition modelling: An investigation through I-optimal response surface methodology

2017

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Section: Selection Of Parameters and Their Working Rangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterization and dynamic mechanical analysis of PC-ABS material processed by fused deposition modelling: An investigation through I-optimal response surface methodology

2017

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“…Thrimurthulu et al [19] used GA to determine the optimum part orientation in AM to minimize surface roughness and build time. Chockalingam et al [20] optimized stereolithography (SL) process parameters to achieve maximum part strength by applying design of experiments. Furthermore, a correlation between part strength and process parameters was established using DoE.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm

Sai

Pathak

Srivastava

2020

J Braz. Soc. Mech. Sci. Eng.

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Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technique for developing complex features and geometries within the shortest possible time as per customer needs. Nowadays, the customization of biomedical parts is becoming possible due to the increasing accuracy and enhanced ability of FDM machines to control the process parameters. The control on surface quality of parts produced by FDM process is of prime concern for the researchers, which are induced due to the stair steps on sloping surfaces, and needs addressing. In addition, to meet customer demands rapidly, the FDM part build time must be reduced without much compromising the strength of the parts necessary for specific applications. In the same context, this paper presents the effect and control of FDM process parameters, i.e., layer thickness, raster angle, infill density and internal structure, on surface roughness, build time and compressive strength of developed biomedical implant parts. The face-centered central composite design is employed to consummate the experimental trials, and experimental data are used to establish an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the surface roughness, build time and compressive strength with respect to changes in FDM process parameters. At the end, whale optimization algorithm (WOA) has been applied to minimize surface roughness, minimize build time and maximize compressive strength simultaneously. Then, the optimal solutions obtained from WOA methodology have been compared with ANFIS predicted results. The results reveal that ANFIS-WOA methodology provides optimal combination of FDM process parameters accurately.

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“…It was found that part orientation, layer thickness, and feed rate have a great effect on the mechanical properties. Therefore, a good choice of part orientation and process parameters is important to overcome or reduce the effect of these problems [123,124] .…”

Section: Challengesmentioning

confidence: 99%

Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A Review

et al. 2017

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Additive Manufacturing (AM) is a game changing production technology for aerospace applications. Fused deposition modelling is one of the most widely used AM technologies and recently has gained much attention in the advancement of many products. This paper introduces an extensive review of fused deposition modelling and its application in the development of high performance unmanned aerial vehicles. The process methodology, materials, post processing, and properties of its products are discussed in details. Successful examples of using this technology for making functional, lightweight and high endurance unmanned aerial vehicles are also highlighted. In addition, major opportunities, limitations, and outlook of fused deposition modelling are also explored. The paper shows that the emerge of fused deposition modelling as a robust technique for unmanned aerial vehicles represents a good opportunity to produce compact, strong, lightweight structures, and functional parts with embedded electronic.2

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Enhancement of Anisotropic Strength of Fused Deposited ABS Parts by Genetic Algorithm

Cited by 47 publications

References 17 publications

Characterization and dynamic mechanical analysis of PC-ABS material processed by fused deposition modelling: An investigation through I-optimal response surface methodology

Characterization and dynamic mechanical analysis of PC-ABS material processed by fused deposition modelling: An investigation through I-optimal response surface methodology

Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm

Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A Review

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