Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology

Waseem, Muhammad; Salah, Bashir; Habib, Tufail; Saleem, Waqas; Abas, Muhammad; Khan, Razaullah; Ghani, Usman; Siddiqi, Muftooh Ur Rehman

doi:10.3390/polym12122962

Cited by 40 publications

(16 citation statements)

References 48 publications

(53 reference statements)

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“…It is rare in the literature for the creep performance of 3D-printed FRP materials to be investigated. Waseem et al [ 70 ] performed a study where creep performance of 3D printed PLA was investigated using multiple response optimization. The study utilized different performance parameters such as infill pattern, layer height and infill percentages.…”

Section: Mechanical Properties Of Fiber-reinforced Polymer Compositesmentioning

confidence: 99%

3D Printing of Fiber-Reinforced Plastic Composites Using Fused Deposition Modeling: A Status Review

et al. 2021

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Composite materials are a combination of two or more types of materials used to enhance the mechanical and structural properties of engineering products. When fibers are mixed in the polymeric matrix, the composite material is known as fiber-reinforced polymer (FRP). FRP materials are widely used in structural applications related to defense, automotive, aerospace, and sports-based industries. These materials are used in producing lightweight components with high tensile strength and rigidity. The fiber component in fiber-reinforced polymers provides the desired strength-to-weight ratio; however, the polymer portion costs less, and the process of making the matrix is quite straightforward. There is a high demand in industrial sectors, such as defense and military, aerospace, automotive, biomedical and sports, to manufacture these fiber-reinforced polymers using 3D printing and additive manufacturing technologies. FRP composites are used in diversified applications such as military vehicles, shelters, war fighting safety equipment, fighter aircrafts, naval ships, and submarine structures. Techniques to fabricate composite materials, degrade the weight-to-strength ratio and the tensile strength of the components, and they can play a critical role towards the service life of the components. Fused deposition modeling (FDM) is a technique for 3D printing that allows layered fabrication of parts using thermoplastic composites. Complex shape and geometry with enhanced mechanical properties can be obtained using this technique. This paper highlights the limitations in the development of FRPs and challenges associated with their mechanical properties. The future prospects of carbon fiber (CF) and polymeric matrixes are also mentioned in this study. The study also highlights different areas requiring further investigation in FDM-assisted 3D printing. The available literature on FRP composites is focused only on describing the properties of the product and the potential applications for it. It has been observed that scientific knowledge has gaps when it comes to predicting the performance of FRP composite parts fabricated under 3D printing (FDM) techniques. The mechanical properties of 3D-printed FRPs were studied so that a correlation between the 3D printing method could be established. This review paper will be helpful for researchers, scientists, manufacturers, etc., working in the area of FDM-assisted 3D printing of FRPs.

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Section: Mechanical Properties Of Fiber-reinforced Polymer Compositesmentioning

confidence: 99%

3D Printing of Fiber-Reinforced Plastic Composites Using Fused Deposition Modeling: A Status Review

et al. 2021

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“…Instead of linear regression, the response surface method (RSM) uncovers the interconnectivity between various controllable factors and several response variables using nonlinear modeling. It should be acknowledged that RSM serves as an approximation method that provides a relatively easy method for modeling, estimating, and optimizing based on target parameters [ 52 , 53 , 54 ]. By use of mathematical and statistical techniques, an empirical model is created from experimental data and is used to evaluate the fit to a statistical model (linear, quadratic, cubic or two-factor Interaction), as described in Equation (4) below.…”

Section: Methodsmentioning

confidence: 99%

“…Same as above, the independent variables A, B, and C represent sulfur content, paraffin oil content, and void content, respectively. The coefficients (

) determined by the model within the linear and quadratic sections dictate the influence each respective variable has on the output, Y, while the Two-Factor coefficients (

) of the quadratic model above quantify the level of influence that interactions between two variables have on the output [ 52 , 53 , 54 , 55 ]. As mentioned above, formulating is a balance between various additives and RSM allows for the user to determine, to some degree, the interaction effects between two controllable variables.…”

Section: Methodsmentioning

confidence: 99%

Natural Rubber Blend Optimization via Data-Driven Modeling: The Implementation for Reverse Engineering

et al. 2022

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Natural rubber formulation methodologies implemented within industry primarily implicate a high dependence on the formulator’s experience as it involves an educated guess-and-check process. The formulator must leverage their experience to ensure that the number of iterations to the final blend composition is minimized. The study presented in this paper includes the implementation of blend formulation methodology that targets material properties relevant to the application in which the product will be used by incorporating predictive models, including linear regression, response surface method (RSM), artificial neural networks (ANNs), and Gaussian process regression (GPR). Training of such models requires data, which is equal to financial resources in industry. To ensure minimum experimental effort, the dataset is kept small, and the model complexity is kept simple, and as a proof of concept, the predictive models are used to reverse engineer a current material used in the footwear industry based on target viscoelastic properties (relaxation behavior, tan and hardness), which all depend on the amount of crosslinker, plasticizer, and the quantity of voids used to create the lightweight high-performance material. RSM, ANN, and GPR result in prediction accuracy of 90%, 97%, and 100%, respectively. It is evident that the testing accuracy increases with algorithm complexity; therefore, these methodologies provide a wide range of tools capable of predicting compound formulation based on specified target properties, and with a wide range of complexity.

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“…PLA, a partially crystalline polymer, is biodegradable (compostable) and has low melting temperature but has limited thermal stability [14]. The performance of these two materials for 3D printing has been assessed [15][16][17][18], particularly in terms of dimensional accuracy [19,20], surface roughness [21], mechanical performance [22] and emission of volatile organic compounds (VOC) [23,24]. For the 3D printing of a compact omnidirectional wheel, Rubies and Palacín [25] observed that when using 100% infill density, PLA yielded a better mechanical performance than ABS.…”

Section: Related Workmentioning

confidence: 99%

The Effect of a Phase Change on the Temperature Evolution during the Deposition Stage in Fused Filament Fabrication

2021

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Additive Manufacturing Techniques such as Fused Filament Fabrication (FFF) produce 3D parts with complex geometries directly from a computer model without the need of using molds and tools, by gradually depositing material(s), usually in layers. Due to the rapid growth of these techniques, researchers have been increasingly interested in the availability of strategies, models or data that may assist process optimization. In fact, 3D printed parts often exhibit limited mechanical performance, which is usually the result of poor bonding between adjacent filaments. In turn, the latter is influenced by the temperature field history during deposition. This study aims at evaluating the influence of the phase change from the melt to the solid state undergone by semi-crystalline polymers such as Polylactic Acid (PLA), on the heat transfer during the deposition stage. The energy equation considering solidification is solved analytically and then inserted into a MatLab® code to model cooling in FFF. The deposition and cooling of simple geometries is studied first, in order to assess the differences in cooling of amorphous and semi-crystalline polymers. Acrylonitrile Butadiene Styrene (ABS) was taken as representing an amorphous material. Then, the deposition and cooling of a realistic 3D part is investigated, and the influence of the build orientation is discussed.

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Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology

Cited by 40 publications

References 48 publications

3D Printing of Fiber-Reinforced Plastic Composites Using Fused Deposition Modeling: A Status Review

3D Printing of Fiber-Reinforced Plastic Composites Using Fused Deposition Modeling: A Status Review

Natural Rubber Blend Optimization via Data-Driven Modeling: The Implementation for Reverse Engineering

The Effect of a Phase Change on the Temperature Evolution during the Deposition Stage in Fused Filament Fabrication

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