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Analyzing Parameters of Pure and Reinforced 3D Printed PLA and ABS Samples

If you want high-quality 3D printed parts, then you need to choose the right print parameters. Research on this topic is ongoing, and the latest comes from the University of Manchester. Chamil Abeykoon, Pimpisut Sri-Amphorn, and Anura Fernando, with the Northwest Composite Centre in the Aerospace Research Institute, published “Optimization of fused deposition modeling parameters for improved PLA and ABS 3D printed structures,” about their work studying various properties and processing conditions of 3D printed specimens made with different materials.

There are multiple variables involved in 3D printing, and changing just one parameter could cause “consequential changes in several other parameters” at the same time. Additionally, the most commonly used FDM printing materials are thermoplastic polymers with low melting points – not ideal for “some high performance applications.”

“Therefore, attempts have been made to improve the properties of printing filaments by adding particles such as short-fibres, nanoparticles and other suitable additives [18]. Thanks to these extensive researches and developments in the area of FDM, fibre-reinforced filaments are becoming popular and are currently available for practical applications,” they explained.

In order to optimize parameters and settings for these new reinforced materials, the team says we need more 3D printing research and development. In their study, they investigated the process using seven infill patterns, five print speeds, and four set nozzle temperatures, and observed and analyzed the mechanical, thermal, and morphological properties.

They used five commercially available materials, with 1.75 mm diameters:

  • Polylactic acid (PLA)
  • Acrylonitrile butadiene styrene (ABS)
  • Carbon fiber-reinforced PLA (CFR-PLA)
  • Carbon fiber-reinforced ABS (CFR-ABS)
  • Carbon nanotube-reinforced ABS (CNT-ABS)

The samples were designed with SOLIDWORKS and printed on a MakerBot Replicator 2, MakerBot Replicator 2X, and MakerBot Replicator Z18.

3D CAD images of test specimens: (a) Tensile, (b) Bending, and (c) Compression.

The team studied seven infill patterns – catfill, diamond, hexagonal, Hilbert, linear, moroccocanstar, and sharkfill –  and infill densities of 25%, 30%, 40%, 50%, 70%, 90%, and 100%. Two shell layers were used for all samples, and the print bed temperature was between 23-70° for CFR-PLA, and 110°C for the three types of ABS material, to help reduce shrinkage and warping.

“At each test condition of all the types of tests (mechanical, rheological and thermal), 3 test specimens were prepared and tested, and then the average value was taken for the data analysis to improve the accuracy and reliability of the experimental data,” the team wrote.

Appearance of the printed compression test specimens: (a) PLA, (b) ABS, (c) CFR-PLA, (d) CFR-ABS, and (e) CNT-ABS.

First, the 3D printed samples underwent mechanical testing to determine tensile modulus, flexural modulus, and compression properties. Using differential scanning calorimetry (DSC), the researchers measured melting and crystallization behaviors in a liquid nitrogen atmosphere, and found “the volume fractions of the reinforcement and matrix of the composite filaments” with the help of thermal gravimetric analysis (TGA).

Appearance of printed tensile test specimens: (a) PLA, (b) ABS, (c) enlarged view of PLA, and (d) enlarged view of ABS.

Using a thermal imaging camera, they detected how much heat was released as the figure above was printed with 100% infill density, 20 mm/s infill speed, and 215°C set nozzle temperature. Finally, they used scanning electron microscopy (SEM) to observe and perform morphological testing on the surfaces of the 3D printed specimens that were broken during mechanical testing.

Infill density affects the strength of 3D printed parts. By increasing infill density, you then increase the tensile modulus and decrease porosity, which increases the “strength of the mechanical bonding between layers.”

Relationship between tensile modulus and infill density for PLA.

“For pure PLA, parts with 100% infill density obtained the highest Young’s modulus of 1538.05 MPa,” the researchers note.

But, structure gaps can occur more frequently with low infill densities, which reduces part strength. In the figure below, you can see “the changes in porosity of the structure with the infill density.”

3D printed specimens with infill densities: (a) 25% (b) 50% and (c) 100%.

“Of the tested infill speeds from 70 to 110 mm/s; 90 mm/s infill speed gave the highest Young’s modulus for pure PLA,” they wrote.

Print speeds over 90 mm/s could cause polymer filament to melt, and result in poor adhesion and lower strength. To avoid this, the print speed must be compatible with the set nozzle temperature, and an appropriate combination of speed and set nozzle temperature “can reduce the shrinkage of the parts being printed.”

Relationship between tensile modulus and infill speed for PLA.

3D printed PLA samples were tested with the different infill patterns at 50% infill density, 90 mm/s speed, and 215°C set nozzle temperature.

3D printed samples with infill patterns: (a) Linear, (b) Hexagonal, (c) Moroccanstar, (d) Catfill, (e) Sharkfill, (f) Diamond, and (g) Hilbert.

“Among these seven patterns, the linear pattern gave the highest tensile modulus of 990.5 MPa. This can be justified as the linear pattern should have the best layer arrangement (in terms of the bonding between the layers) with the lowest porous structure,” the team explained.

They found that the print temperature has “a significant effect on the tensile modulus.” 215°C provided the best tensile performance, as lower temperatures might cause poor melting, and thus weak bonding. The set nozzle temperature and print speed correlate, and “should be chosen carefully based on the material being used and the part geometry being printed.”

To study the effect on tensile properties, they were printed with the following parameters: 90 mm/s infill speed, linear pattern, 10% infill density, and 215°C set nozzle temperature for PLA, and 230°C for ABS. The researchers found that the tensile modulus of pure PLA (1538.05 MPa) was far higher than for pure ABS.

“In this study, CFR-PLA gave the largest tensile modulus of 2637.29 MPa while pure ABS (919.52 MPs) was the weakest in tensile strength,” they wrote.

Tensile modulus of the five printing materials.

Reinforcing ABS and PLA with fiber causes higher tensile modulus, though pure PLA was stronger than the CNT-ABS.

Even at 90° of bending, the PLA and ABS samples only had a small crack in the middle, and did not break.

3D printed specimen in bending test.

At 1253.62 MPa, the CFR-PLA had the highest bending modulus, while pure PLA was the lowest at 550.16 MPa.

During compression tests, none of the materials were crushed or broken, and pure ABS was found to be the toughest.

“As evident, pure PLA gave the highest compressive strength while the compressive modulus of CFR-PLA (1290.24 MPa) is slightly higher than that of pure PLA (1260.71 MPa) (higher gradient of the liner region). CFR-ABS and CNT-ABS follow the same trend but CNT-ABS is slightly tougher than CFR-ABS,” the team explained. “Pure ABS shows the lowest compressive strength and modulus (478.2 MPa) but shows the most ductile behavior of the five materials.”

Compressive stress-strain curves of test materials.

Finite element analysis (FEA) by ANSYS was used to visualize stress distribution for the tensile, bending, and compression testing of PLA.

Equivalent stress distribution for tensile test.

“The stress distribution shows that a uniform stress is created in the gauge length of the test piece,” they explained.

“Higher compressive loading will cause the material to have internal crack initiations thereby allowing the PLA to buckle excessively.”

The team concluded through DSC analysis that “the strength of the 3D printed samples is dependent upon the set printing parameters and the printing materials more than the crystallisation.” While the infill speeds differ, the glass transition temperature (Tg) of the samples were similar.

“In this study, cooling of 3D printed parts occurred naturally by releasing heat to the surroundings while printing without any control on the cooling rate,” they stated.

DSC curves of PLA parts printed at different set nozzle temperatures.

As expected, the set nozzle temperature did not significantly effect the Tg, and material crystallization at different temperatures didn’t really affect part strength. But, the tensile modulus did change with the temperature.

TGA was used to analyze the weight loss variation of the composite materials against increased print temperature.

TGA diagrams of short fiber-reinforced composite filaments.

“Degradation temperatures (Td) of these materials can be determined from the mid-point of the descending part of each curve, which is approximately 331.85 °C for PLA. This value showed some sort of agreement to the value reported in commercial PLA data sheets – 353 °C,” they wrote.

Pure PLA typically has a higher Young’s modulus than pure ABS, so it can help to add “a higher volume fraction of reinforcement into the ABS matrix.” Brittle CFR-PLA and CFR-ABS filaments could have their flexibility affected if more carbon fiber is added, which can cause filament feed issues.

Thermal image during 3D printing.

An infrared thermal camera was used to observe 3D printing. The yellow area is the brightest, and hottest: this is where the polymer was extruded from the nozzle. The color changes to orange where the material starts to solidify, and the “red, pink, purple, and blue areas are at lower temperatures, respectively.” The red circle marks the temperature at the printer wall – less than the sample actually being printed.

“SEM images showed that the strength of the printed samples was dependent upon the arrangement of their layers,” the team noted.

Normal and SEM images of fracture surfaces of PLA samples: (a) 25% and (b) 100% infill density.

Observing the fracture surfaces of broken PLA samples with SEM showed that “the air gaps of 25% infill density sample is larger than that of 100% infill density.”

Looking at infill speed with SEM, the team noted that “the best orderliness” comes from 90 mm/s infill speed.

Incompatibility between the material matrix and the reinforcement can cause porosity in the 3D printed samples, but the latter can “contribute in increasing the mechanical properties by bearing the load.” You can see below that the pure PLA has a more regular layer alignment when compared to pure ABS.

SEM images of 3D printed parts at 19X magnification: (a) PLA, (b) ABS, (c) CFR-PLA, (d) CFR-ABS, and (e) CNT-ABS.

CFR-ABS is more porous than CFR-PLA, and both are rougher than the materials in their pure forms.

“Meantime, CNT-ABS shows a better arrangement of individual layers than the other two carbon fibre reinforced materials and also than the pure ABS as well,” they explained.

The last SEM images compare the size of the carbon fiber and carbon nanotube reinforcements. The fracture surface of the CNT-ABS shows some small holes, “due to the embedded carbon nanotubes in the matrix.”

“Compared to the matrix-reinforcement compatibility, both materials show some sort of incompatibility by having cracks and voids between the fibre and matrix,” they wrote.

“On the other hand, although the overall strength of CNT-ABS is improved by CNT particles, the flexibility of this material was decreased compared to the pure ABS as CNT-ABS being more brittle.”

SEM images of fracture surfaces at 1.00 KX magnification: (a) CFR-PLA and (b) CNT-ABS.

They found that the optimal settings to improve the performance of the five 3D printing materials were 100% infill density, 90 mm/s infill speed, 215 °C of set nozzle temperature, and linear infill. Of the five materials, CFR-PLA had the strongest tension, bending, and compression, with the highest modulus.

“Overall, it is obvious that the set printing parameters can significantly influence the mechanical properties of 3D printed parts. It can be claimed that the printing speed and set nozzle temperature should be matched to ensure proper melting of filaments and also to control the material solidification process,” the researchers concluded.

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Reducing 3D Printing Collisions with Toolpath Optimization Methodology

While many industries are using 3D printing to manufacture products, the technology has not been largely adopted in large-scale production. According to researchers from the University of Arkansas Department of Industrial Engineering, this is mainly due to cycle time. However, while it’s possible to print different parts of one object at the same time thanks to multiple collaborating printheads, this isn’t yet widely supported by research. Hieu Bui, Harry A. Pierson, Sarah G. Nurre, and Kelly M. Sullivan published a paper, titled “Tool Path Planning Optimization for Multi-Tool Additive Manufacturing,” that lays out their proposed toolpath optimization methodology.

The abstract states, “The objectives are to create a collision-free infill toolpath for each printhead while maintaining the mechanical performance and geometric accuracy of the printed object. The methodology utilizes the combination of tabu search and novel collision detection and resolution algorithms, TS-CCR. The performance of the TS-CCR is analyzed and compared with the current industry standard.”

The FFF 3D printing process is limited by how fast the printhead is able to move, melt, and dispense filament. The parallel processing method, which lets multiple toolheads work together at the same time to fabricate different parts of the same object, is used by the Autodesk Netfabb software function for Project Escher 3D printers. This can obviously speed up printing time, but also increases the chance for collisions.

Netfabb uses an algorithm to make sure that all the printheads are synchronized, so they can’t collide with each other.

Summary of the result from the case study of Netfabb’s performance and toolpath illustrations (30% infill) of the Netfabb method and proposed method.

 The goal of this methodology is to consider collision constraints for 2-gantry 3D printers, while also minimizing the single layer makespan (printing time). 

“The shortcomings of current methods, the lack of published research on concurrent FFF, and the need for an alternative path-planning method for multi-gantry FFF 3D printers inspired the development of a new method,” the researchers explained. “Although the multi-gantry system is one of several kinematic configurations of concurrent FFF 3D printing, increased understanding it can provide insights into the development of generalized multi-tool path planning problems for AM processes.”

A Tabu Search (TS) heuristic (practical method of problem solving), which uses a memory mechanism to store information to help guide future searches, was used to optimize the single layer makespan in the methodology by adjusting the toolpath for the infill. The TS incorporates three main operators:

  1. The local swap operator swaps two raster segments printed by the same printhead to reduce the rapid movement distance
  2. The global swap operator exchanges two raster segments that have been printed from different printheads
  3. The rebalancing operator allocates one raster segment from the printhead with a higher makespan to the other printhead

a) trajectory plot produced by the collision checking algorithm (tested layer A with 1% infill) showing 4 possible collisions (i.e. vertical gray bars); b) trajectory plot after adding 3 seconds’ delay to resolve the first collision (note that it also resolves the following collisions); c) toolpath representations of solution in 2b. The arrows indicate the two gantries are moving in the opposite directions toward each other when printing the associated raster segments. By adding 3 seconds delay at the dwell location, the two gantries synchronized and avoided the potential collision.

“At the beginning of the algorithm, with a randomized initial solution list, the global swap operator is favored. Due to the high degree of randomization of the sequence and the high number of collisions, adding delays might not be able to resolve the collisions, in which case the two gantries will work in sequential order. The goal is to segment the appropriate raster segments into two groups, one group for each printhead. The number of collisions begins to decrease as a result. Later on, the local swap slowly becomes more attractive.”

Two complementary algorithms work with the TS: a collision checking algorithm, which detects any potential collisions, and a collision response algorithm, which finds points in the toolpaths where a collision can be avoided by adding a delay.

The researchers explained, “An efficient collision checking algorithm should be able to quickly detect the collisions for a large number of raster segments and identify the corresponding movements that caused them. By utilizing a unique characteristic of the multi-gantry FFF machine, the process of identifying the collisions can be simplified. In such configuration, the collisions happen every time the gantries collide in the x-direction. In other words, a collision happens when the two gantries share the same workspace at any moment in time. A safety distance between two gantries was added when constructing the trajectory plot as a way to keep the gantries away from each other even though the collision is detected.”

Flowchart of collision checking algorithm

“The motivation of the collision response algorithm is to identify an opportunity for resolving the collision by adding a delay. It is worth mentioning that each vertex on the trajectory plot represents a potential place to insert the delay.”

This algorithm has 4 steps, the first being to identify a set of line segments that are associated with the first collision, and then figuring out whether a delay could fix the collision. Third, the delay is inserted and all future trajectory segments are adjusted, and finally, you move up in time to find the next collision; then, lather, rinse, repeat until the collisions are gone.

The team’s methodology for avoiding 3D printing collisions was thus named Tabu Search with collision checking and response, or TS-CCR.

“The TS-CCR outputs a solution represented as a combined list of sequences of raster segments that must be printed for each printhead,” the researchers wrote. “To get the infill makespan of the solution, an infill toolpath for each printhead is constructed from the aforementioned solution. The collision-checking algorithm then searches for any potential collisions and passes the information to the collision-response algorithm, which introduces delays in order to prevent potential collisions.”

a) tested layer A; b) turbine blade layer; c) engine block layer; d) wheel rim layer. The wheel rim layer is considered a special case since Netfabb did not produce a solution.

To test the TS-CCR’s performance, the team chose four objects, then sliced a selected layer of 0.3 mm from each and computed the results from the theoretical minimum makespan, slicing the layer with the Netfabb Multi-Gantry FFF Engine and the 2018.1.0 Escher plugin, and the TS-CCR.

They collected information, such as build volume and print speed, about the multi gantry 3D printer from the Titan Cronus profile in Netfabb.

“For the TS heuristic, the value for the size of the candidate list and tabu tenure were chosen as 10 and 4, respectively. The algorithm terminates if it has been running for 2 minutes since the last improvement,” the researchers explained.

Then, they compared the makespan for three solutions – the theoretical minimum, proposed methodology, and Netfabb for 2 printheads – in a trajectory plot, which shows how the algorithms performed. 55 seconds of delays were added at different points, but because most of these were introduced in the printhead with a shorter makespan, only three total seconds were added to the overall makespan. This plot also shows how important the rebalancing operator is in TS – the gantries completed their work at almost the same time.

Trajectory plot of the result obtained from the TS-CCR (engine block layer with 30% infill). The printing time of the two gantries are 1272 and 1269 seconds, respectively.

“The performance of the methodology varies depending on the complexity of the layer. It can reduce the makespan of the “tested layer A” by 14.48% as compared to Netfabb, while the improvement reduces to 10.18% for the “engine block” layer. Since only one printhead is utilized to print the perimeter shells, the time spent on printing the shells likely offsets the improvement of the proposed methodology for any complex layer. Since this work focuses on only optimizing the infill, the method of allowing multiple printheads to print the perimeter shell at the same time can be implemented to reduce the makespan further,” the researchers wrote.

While there are only about 11 minutes of makespan reduction for the tested layer over the single printhead, this kind of improvement can accumulate across all layers and reduce the overall time.

a) makespan comparison for 3 layers (tested layer A, engine block, turbine blade) at 30% infill, where the proposed method can yield a solution with a shorter makespan than the solution obtained from Netfabb; b) makespan comparison for the “wheel rim” layer, where Netfabb did not produce a solution. The result from the methodology is compared to the makespan if the same layer is printed by the single printhead and the theoretical minimum.

The team’s proposed TS-CCR methodology can solve major issues of using multi-gantry FFF 3D printing, such as carefully planning to avoid mutual collisions while also not compromising the strength of the final print.

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DTU and TU Delft: Stress Adapted Orthotropic Infill for 3D Printing

A team of researchers at the Technical University of Denmark (DTU) and Delft University of Technology (TU Delft) teamed up recently to improve functionality with infill in orthotropic materials as well as studying how they could further optimize performance and overall quality in such objects. Authors Jeroen P.Groen, JunWu, and Ole Sigmund detail their findings further in ‘Homogenization-based stiffness optimization and projection of 2D coated structures with orthotropic infill.’

Overview of the proposed methodology to obtain high-resolution coated designs, with composite orthotropic infill.

Emphasizing better ways to produce coated structures with regular infill, improve quality, and still enjoy better affordability in production, the research team outlines their new method for generating stress adapted orthotropic infill for 3D printing. While pointing out that methods like FDM 3D printing, known for manufacturing solid structures, offer consistently stable structures, the researchers state that optimization of complex geometries is an ideal challenge that technology like 3D printing and additive manufacturing should be able to overcome when using infill too. They begin by discussing homogenization-based topology optimization for coated structures, and then the second half of their paper explains their method for creating high-resolution objects on fine mesh.

Coating is the first topic at hand, although the researchers point that the technique to discern between both infill and coating is almost identical.

“The procedure to distinguish between coating and infill makes use of two well-established filter methods in topology optimization,” state the researchers. “The first is a smoothing operation using the density filter. The second is a projection step to force the smoothed values on the interval towards either 0 or 1.”

The team noted that in using a single smoothing and projection (SSP) technique, the structures tended to have better compliance, but they found voids within the structure in areas not covered by coating. With double smoothing and projection (DSP), they found that there was almost a 90 percent reduction rate of vanishing coating—in comparison to SSP. And although further testing would be required, the researchers also theorize that ‘vanishing coatings’ could be prevented if image processing was applied after homogenization-based optimization and enforced coating. Overall, they found DSP to ensure ‘clear distinction’ between the coating, infill, and any voids.

As they began mapping coated designs in the second half of their study, they explained how their innovative method refining periodicity allows for regular infill spacing.

Example of the mapping procedure.

“Numerical experiments demonstrate that the projected designs, despite a lack of separation of scales, are very close (within 1%–2%) to the homogenization-based performance,” stated the researchers.

Such optimization of infill also produced designs with finer resolution and higher performance—all with a computational cost they project to be ten times lower, or more. This approach also results in 31% stiffness improvement (or similar weight reduction) when dropping conventional isotropic infill in favor of orthotropic stress adapted infill.

“This overall promising approach allows for extension of the method to 3D or to more complex loading situations. The main challenge here will lie in finding a parameterization that allows for smoothly varying microstructures through the domain,” concluded the research team. “We are confident that such a parameterization can and will be found.”

Infill can be a critical component in the stability and overall success of a 3D printed structure. Many users tend to overlook or struggle with optimizing this variable, but different customization techniques can lead to improved functionality in experimenting with new 3D printing materials, seeking properties like better tensile strength, and enhancing innovation overall.

Find out more about infill and orthotropic materials here. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com.

Visual explanation of adaptive periodicity and required transition zone.

[Source / Images: ‘Homogenization-based stiffness optimization and projection of 2D coated structures with orthotropic infill‘]

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Researchers Investigate Tensile Properties of 3D Printed PLA Specimens: Is 80% Infill Best?

Comparison between 3D printed products from both recycled and virgin PLA.

Plastic is still one of the most popular, least expensive 3D printing materials, and polylactic acid, better known as PLA, is at the top of the bunch, due to its high strength, biocompatibility, and biodegradable nature. A lot of studies have been completed regarding PLA 3D printing filament, and a team of researchers from the Universiti Malaysia Pahang (UMP) recently published a paper, titled “Preliminary investigations of polylactic acid (PLA) properties,” that details their investigation of the tensile property of a PLA specimen 3D printed using FDM technology, along with figuring out the optimum combination of 3D printing parameters to achieve maximum mechanical properties.

The abstract reads, “This research work aims to investigate the tensile property of Polylactic Acid (PLA) and to determine optimum printing parameter combinations with the aim of acquiring maximum response using low-cost fused filament printer. Two parameters chosen to be varied in this research are raster angle and infill density, with the value of 20°, 40°, 60° and 20%, 50%, 80% respectively. Tensile specimens with a combination of these two parameters were printed according to ASTM D638 type 1 standard. Three mechanical properties were analysed, namely ultimate tensile strength, elastic modulus and yield strength. It was found that the tensile property increases with the infill density. Meanwhile, both high and low raster angle have shown the considerably high mechanical properties. The optimum parameters combination is 40° raster angle, and 80% infill density. Its optimum mechanical property is 32.938 MPa for ultimate tensile strength, 807.489 MPa for elastic modulus and 26.082 MPa for yield strength.”

Applications for FDM 3D printing include more load-bearing parts for specific requirements, and many of these demand a “certain level of mechanical property information” to be set as a benchmark in order to assess a 3D printed PLA part’s strength.

This strength typically relies on some specific 3D printing parameters, including layer height, infill density, and raster angle, which refers to the direction of the beads of material in regards to loading of a part or component. In the experiment, several parameters were kept constant in order to “avoid mislead of result obtained.”

“The design of the experiment includes the parameter and its value selected to be investigated,” the researchers wrote. “Two chosen parameters are raster angle and infill density, with a value of 40°, 60°, 80° and 20%, 50%, 80% respectively. Layer height is kept constant at 0.1 mm throughout this whole experiment. The total number of parameter combinations are 9. Since the sample size is 5, the total number of specimens printed are 45 specimens.”

While the layer height remained 0.1 mm for each of the nine parameter combinations, the raster angle was split up into three groups: three at 40°, three at 60°, and three at 80°. The three infill densities tested were 20%, 50%, and 80%. The team used a Rainstorm Desktop 2D Multicolor Printing Printer Reprap Prusa i3, with 0.4 mm nozzle, to manufacture the tensile test specimen out of 1.75 mm PLA, which was designed using SOLIDWORKS and sliced with Repetier Host software.

Average stress strain curve for all 9 parameter combinations.

The team completed tensile testing, with a maximum load of 50 kN, on all nine of the 3D printed, 3.2 mm thick PLA specimens at a speed of 5 mm/min, according to ASTM D638 standard. The ultimate tensile strength, yield strength, and elastic modulus were extracted from multiple regions and points: proportional limit, elastic limit, yield point, ultimate stress point, and fracture point.

“Ultimate tensile strength (UTS) is the maximum stress that a material can withstand without undergoing plastic deformation while being stretched or pulled,” the researchers explained. “Elastic modulus is the ratio of the force exerted upon a substance to the resultant deformation it experiences, or also known as stiffness. Meanwhile, yield strength is the stress required to produce a small specified amount of plastic deformation.”

The team discovered that when the infill increased, so too did all the property values, which means it’s possible to choose the right infill percentage in order to achieve economic material use.

“Upon analysis of all obtained data, the best-suited parameter combination that results in optimum mechanical property have been identified, which is 3rd parameter combination of 40° raster angle and 80% infill density,” the researchers noted. “It has resulted in the ultimate tensile strength of 32.938 MPa, elastic modulus of 807.489 MPa and yield strength of 26.082 MPa. The 9th combination was not chosen as the optimum parameter condition due to its lower yield strength comparatively. Selection of optimum parameter combination was made based on the criteria of possible maximum mechanical property.”

(a) Fabricated PLA tensile specimen, (b) working mechanism of FDM

The researchers did not investigate the effect of infill density or raster angle on the specimen’s UTS, yield strength, or elastic modulus for this paper.

“The tensile properties of PLA 3D printed specimens were successfully extracted from the plotted stress-strain graphs upon tensile tests. From the obtained experimental data, the optimum parameter combination with the maximum mechanical property was determined. However, these research results are not adequate for 3D printer user to explore the options they have based on their specific need,” the researchers concluded. “Therefore, this scope of research must be extended by including more ranges of parameter values to be investigated.”

Co-authors of the paper are S. R. Subramaniam, M. Samykano, S. K. Selvamani, W. K. Ngui, K. Kadirgama, K. Sudhakar, and M. S. Idris.

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