Assessments of layer height, nozzle temperature and infill density and Taguchi analysis for optimisation of impact strength of biomaterial polylactic acid in 3D printing process

The influencing parameters like infill density, infill pattern, nozzle temperature and layer height on tensile strength of carbon fiber-reinforced Polylactic Acid (PLA) samples were investigated. PLA along with carbon fiber composite filament is used as a printing material owing to its excellent structural properties. Fused Deposition Modeling (FDM) technique is employed in the present investigation due to its simplicity. Fused Filament Fabrication (FFF) is one of the simplest and most cost-effective printing techniques in the Additive manufacturing process. The different printing parameters are used to develop the tensile samples as per Taguchi’s design of experiments. A L16 orthogonal array was selected from the set of levels and factors in the present investigation. The tensile specimens were printed as per the ASTM D638 testing standard. It has been observed from the results that the most influencing parameter on tensile strength is infill density, and infill pattern layer thickness is having very less influence on tensile strength followed by nozzle temperature. The maximum tensile strength of 61.83 MPa is obtained at a line pattern with 90% of infill density with a layer thickness of 0.05 mm printed with a nozzle temperature of 210 °C.KeywordsAdditive manufacturingFDMPLATensile strengthTaguchi methodANOVA

Abstract. 3D printing is a revolutionary manufacturing technology that provides previously unheard-of levels of efficiency and design freedom. Fused Deposition Modeling (FDM) is a popular method for creating complicated parts using thermoplastic materials like Polylactic Acid (PLA). Layer height, layup speed, and infill pattern are some of the process variables that have a major impact on the mechanical characteristics of PLA components that are FDM produced. The goal of this study was to maximize the mechanical performance of PLA components made with honeycomb and cubic infill patterns. The best combinations of layer height, layup speed, and infill density were found via Taguchi optimization. The findings showed that in terms of modulus and tensile strength, honeycomb infill designs continuously performed better than cubic patterns. Notably, a 40% infill density with a 0.3 mm layer height and a 60 mm/s layup speed yielded the best mechanical properties for honeycomb-patterned parts. The Taguchi study emphasized how important layer height and infill density are to mechanical performance. These results offer useful recommendations for producers looking to enhance FDM procedures in order to create long-lasting, premium PLA components. Tensile strength and modulus were found to be most significantly impacted by infill density and layer height when the Taguchi method was used to optimize the process parameters.

Polylactic acid (PLA), is a thermoplastic polyester that has many uses in both consumer goods and industrial settings. The mechanical characteristics of PLA specimens created using Fused deposition modeling (FDM), a cost-effective 3D printing process, including tensile strength, shore hardness, and dimensional precision, have been studied for use in specialised engineering applications. Layer height, infill density, and printing speed are the choices made for the specimen’s 3D printing. Design of experiments use Taguchi’s L9 orthogonal array. Using analysis of variance (ANOVA), designers can determine the relative importance and percentage contribution of each process parameter to each answer. Using Taguchi method while conducting test for individual responses result shows that for tensile strength printing speed is 70 mm/s, layer height 0.2mm and 40% infill density as optimum parameters while for the hardness it is 60 mm/s,0.3mm and 40%, and for the dimensional deviation found 60 mm,0.2 mm, 40% respectively. Proposed TGRA method found optimum parameter for all the responses in single test as printing speed is 70 mm/s, layer height 0.3 mm and 40% infill density also validated by conducting confirmation test. Finally, a superior specimen with all-around mechanical characteristics is fabricated using Taguchi based grey relational analysis (TGRA) as a multi-objective optimization technique.

This work aims at studying the effect of various inputs like infill density, layer thickness, speed of printing, and nozzle temperature on tensile strength of the Poly Lactic Acid (PLA) specimen printed using Fused Deposition Modeling (FDM) process technique. Experiments have been conducted using a variety of different combinations of influencing parameters chosen from Taguchi's L16 orthogonal array. A regression-based mathematical model has been developed to establish relationship between output and input variables. The significance of input parameters in terms of degree of influence on tensile strength has been determined using Analysis of variance (ANOVA). It has been observed that the input parameters in layer height, print speed, and nozzle temperature are more significant than other parameters. Additionally, factorial plots have been utilized to determine the effect of each input parameter on the output, namely the tensile strength. Moreover, Taguchi analysis was done to find the best combination of input parameters for maximum Tensile strength. It has been observed that maximum value of Tensile Strength can be obtained at print speed of 50 mm/s, layer thickness of 0.2 mm, infill density of 100%, and nozzle temperature of 220 \(^\circ{\rm C}\). The results obtained from Taguchi Analysis have been experimentally validated.KeywordsAnalysis of variance (ANOVA)Fused deposition modeling (FDM)Poly lactic acid (PLA)Regression analysis (RA)

Hidden layers perform a vital role in the performance of artificial neural networks (ANNs), especially for serpentine problems where the curtailment of accuracy and time complexity is observed. A higher number of hidden layers and neurons enlarge the order of weights and influence the ANN accuracy. In the present work, an ANN model is developed to predict the surface roughness of fused deposition modelling (FDM) parts considering the effect of the number of neurons, hidden layers, layer height, infill density, nozzle temperature, and print speed. A feedforward back-propagation machine learning algorithm is used to model an ANN. This study finds better prediction accuracy with the ANN architecture having two hidden layers with 150 neurons on each layer (R-squared: 0.875) followed by one hidden layer with 250 neurons (R-squared: 0.83). This study finds a decrease in the surface roughness of FDM parts with the increase in infill density. However, the surface roughness was observed to increase with the layer height, print speed, and nozzle temperature. This study finds a decrease in the surface roughness of FDM parts with the increase in infill density and an increase in the surface roughness with the layer height, print speed, and nozzle temperature.

PurposeThe aim of this study is to investigate the printing parameters of fused deposition modeling (FDM), a material extrusion-based method, and to examine the mechanical and thermal properties of their polylactic acid (PLA) components reinforced with copper, bronze, and carbon fiber micro particles.Design/methodology/approachTensile test samples were created by extruding composite filament materials using FDM-based 3D printer. Taguchi method was used to design experiments where layer thickness, infill density, and nozzle temperature were the printing variables. Analysis of variance (ANOVA) was applied to determine the effect of these variables on tensile strength.FindingsThe results of this study showed that the reinforcement of metal particles in PLA material reduces strength and increases elongation. The highest tensile strength was obtained when the layer thickness, infill density, and nozzle temperature were set to 100 µm, 60%, and 230 °C, respectively. As a result of thermal analysis, cooper-PLA showed the highest thermal resistance among metal-based PLA samples.Originality/valueIt is very important to examine the mechanical and thermal quality of parts fabricated in FDM with metal-PLA composites. In the literature, the mechanical properties of metal-reinforced composite PLA parts have been examined using different factors and levels. However, the fabrication of parts using the FDM method with four different metal-added PLA materials has not been examined before. Another unique aspect of the study is that both mechanical and thermal properties of composite materials will be examined.

Fused Deposition Modeling (FDM) has significantly advanced in the additive manufacturing of complex geometrical and customized parts, particularly for thermoplastics like Polylactic Acid (PLA). The present study aimed to optimize FDM process parameters to improve the tensile strength of 3D-printed PLA, a crucial mechanical property for various applications. The Taguchi method was employed to systematically and effectively analyze the effects of six key process parameters: nozzle temperature, printing speed, layer thickness, infill density, infill pattern, and orientation. The analysis revealed that among these parameters, only nozzle temperature and infill density had a significant impact on tensile strength, as demonstrated by the variance analysis. By optimizing these critical parameters, the tensile strength of the printed PLA parts was improved from the previously reported 35 MPa to 40 MPa, representing a notable enhancement. Additionally, a linear regression-based empirical model was developed, achieving an R-squared value of 89.2%, enabling accurate prediction of tensile strength for given process parameter values. These findings provide a vital foundation for enhancing the mechanical performance of FDM-printed PLA components. They are particularly relevant for applications across industries requiring high-strength materials, further solidifying the potential of FDM in advanced manufacturing scenarios.

<div class="section abstract"><div class="htmlview paragraph">3-Dimensional (3D) printing is an additive manufacturing technology that deposits materials in layers to build a three-dimensional component. Fused Deposition Modelling (FDM) is the most widely used 3D printing technique to produce the thermoplastic components. In FDM, the printing process parameters have a major role in controlling the performance of fabricated components. In this study, carbon fibre reinforced polymer composites were fabricated using FDM technique based on Taguchi's Design of experimental approach. The matrix and reinforcement materials were poly-lactic acid (PLA) and short carbon fibre, respectively. The goal of this study is to optimize the FDM process parameters in order to obtain the carbon fibre reinforced PLA composites with enhanced hardness and compressive strength values. Shore-D hardness and compression tests were carried out as per American Society for Testing and Materials (ASTM) D2240 and ASTM D695 standards respectively, to measure the output responses. The FDM process parameters considered in this study are layer height, infill density and infill pattern. The grey relational analysis (GRA) based multi-response optimization technique is used to optimize the process parameters. Analysis of variance is used to determine the most influential process parameter. The results showed that 3D printed components with improved performance characteristics could be achieved at 0.1mm layer height, Grid shaped infill pattern, and 75g/cm<sup>3</sup> infill density with a Shore-D hardness value of 76 and compressive strength of 42 N/mm<sup>2</sup>. It was identified that for multi-response optimization of equal weightage condition, the layer height contributed 44.44% followed by the contribution of Infill pattern and Infill density by 25.93% and 18.04% respectively. The developed regression model predicted the grade value at 90% confidence interval.</div></div>

This research employs the Taguchi method and analysis of variance (ANOVA) to investigate, analyze, and optimize the impact strength of tough polylactic acid (PLA) material produced through fused deposition modeling (FDM). This study explores the effect of key printing parameters-specifically, infill density, raster angle, layer height, and print speed-on Charpy impact strength. Utilizing a Taguchi L16 orthogonal array experimental design, the parameters are varied within defined ranges. The results, analyzed through signal-to-noise (S/N) ratios and ANOVA, reveal that infill density has the most substantial impact on Charpy impact strength, followed by print speed, layer height, and raster angle. ANOVA identifies infill density and print speed as the most influential factors, contributing 38.93% and 36.51%, respectively. A regression model was formulated and this model predicted the impact strength with high accuracy (R2 = 98.16%). The optimized parameter set obtained through the Taguchi method, namely, a 100% infill density, 45/-45° raster angle, 0.25 mm layer height, and 75 mm/s print speed, enhances the impact strength by 1.39% compared to the experimental design, resulting in an impact strength of 38.54 kJ/m2. Validation experiments confirmed the effectiveness of the optimized parameters.

Fused deposition modeling (FDM) is the most economical additive manufacturing (AM) technology available for fabricating complex part geometries. However, the involvement of numerous control process parameters and dimensional instabilities are challenges of FDM. Therefore, this study investigated the effect of 3D printing parameters on dimensional deviations, including the length, width, height, and angle of polylactic acid (PLA) printed parts. The selected printing parameters include layer height, number of perimeters, infill density, infill angle, print speed, nozzle temperature, bed temperature, and print orientation. Three-level definitive screening design (DSD) was used to plan experimental runs. The results revealed that infill density is the most consequential parameter for length and width deviation, while layer height is significant for angle and height deviation. The regression models developed for the four responses are non-linear quadratic. The optimal results are obtained considering the integrated approach of desirability and weighted aggregated sum product assessment (WASPAS). The optimal results include a layer height of 0.1 mm, a total of six perimeters, an infill density of 20%, a fill angle of 90°, a print speed of 70 mm/s, a nozzle temperature of 220 °C, a bed temperature of 70 °C, and a print orientation of 90°. The current study provides a guideline to fabricate assistive devices, such as hand and foot orthoses, that require high dimensional accuracies.

Manufacturing industry has been evolving during the last few centuries. Industry 1.0 started with mechanization and the use of steam power. Mass production using production lines and assembly lines dominated Industry 2.0 era. Industry 3.0 era brought automation, flexibility and product diversity and Flexible Manufacturing Systems (FMS) and cellular systems were extensively used. Recently, there is a shift towards the fourth industrial revolution (Industry 4.0). Industry 4.0 includes the combination of technologies working together to fulfill a manufacturing task. Industry 4.0 utilizes internet of things (IIoT), big data, cloud computing, cybersecurity, autonomous robotics, augmented reality, and additive manufacturing (AM). The purpose of Industry 4.0 is to integrate the entire network to function as one system. In this study, we are focusing on scheduling 3D printing machines, namely Markforged Mark Two printers. Process parameters that can be considered in these printers are layer height, infill density, print speed, build orientation, infill patterns, and print temperature. These machines are Fused Filament Fabrication (FFF) 3D printers. The parameters considered in this study are infill density and layer height. Infill density dictates the amount of material that is filled on the inside of an object while it prints. Infill density has a role in a part’s strength and weight. Generally speaking, the greater the infill density, the stronger and heavier an object will be. Lower infill densities on a part suggest that the object’s intentions are purely visual with higher infill densities meant for functional parts. Markforged Mark Two allows infill density for rectangular infill to be from 0–92%. On the other hand, layer height determines the amount of material that is extruded through the nozzle during each pass. Markforged Mark Two allows for three different layer heights to be examined, 100, 125 and 200 mm. Layer height plays a large role in print time as the amount of material extruded effects the completion rate of the object. Layer height’s impact can also be seen by a part’s fineness or detail. This is represented visually on the object by being able to see each pass of the plastic material. For example, an object with a larger layer height will look rougher and not as smooth as an object with a lower layer height. However, it is well known that a lower layer height increases print time whereas a larger layer height implies a faster print time. Several parts with different geometries and also sizes are included in the study. The scheduling performance measure considered is makespan. The objective of the study is to find the optimal parameter settings for multiple jobs such that makespan is minimized subject to minimum restrictions on print parameters for various jobs. A mathematical model is presented to minimize makespan first. Once the optimal makespan is found, the model is re-run such that better quality parameter settings are determined while keeping the optimal makespan unchanged. Later, the results of the experimentation with various parts are discussed and future work is recommended.Keywords3D PrintingSchedulingMakespan

Low-cost desktop 3D printing is now dominated by free and open source self-replicating rapid prototype. However, optimum printing process parameters have not been provided by the manufacturer, since there are several process parameters that need to be optimized to obtain acceptable dimension error and strength properties. This paper aims to present the optimum process parameters for the 3D printing process of Polylactic Acid (PLA) part using Taguchi Method. A specimen standard of ASTM D638 Type IV made of biodegradable polymer, PLA, has been used as a tensile strength test to represent printed part quality. Four printing process parameters: temperatures, extrusion width, infill density and infill pattern have been optimized using Taguchi Methods. Test was carried out to find the highest tensile strength based on the optimum parameter setting and validated them with experiment. The result shows that the tensile strength response was predominantly influenced by infill density followed by nozzle temperature, infill pattern and extrusion width. The optimum level setting was obtained at 75% of infill density (C3), 215oC of nozzle temperature (A3), honeycomb infill (D1) and 0.3 mm of extrusion width (B1). At optimized parameters the tensile strength PLA parts significantly was found of 30.52 MPa at a confidence interval of 95%.

Effects of process parameters and annealing on the tensile strength of 3D printed carbon fiber reinforced polylactic acid

The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the on-demand necessity to perform surgery during space missions. Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing. Among all 3D printing techniques, fused deposition modelling (FDM) is a low-cost and more rapid printing technique. This article proposes the fabrication of surgical instruments, namely, forceps and hemostat using the fused deposition modeling (FDM) process. Excellent mechanical properties are the only indicator to judge the quality of the functional parts. The mechanical properties of FDM-processed parts depend on various process parameters. These parameters are layer height, infill pattern, top/bottom pattern, number of top/bottom layers, infill density, flow, number of shells, printing temperature, build plate temperature, printing speed, and fan speed. Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid (PLA) parts printed by FDM. The experiments have performed through Taguchi's L27 orthogonal array (OA). Variance analysis (ANOVA) ascertains the significance of the process parameters and their percent contributions to the evaluation indexes. Finally, as a multi-objective optimization technique, grey relational analysis (GRA) obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties. Scanning electron microscopy (SEM) examines the types of defects and strong bonding between rasters. The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength (42.6 MPa) and modulus of elasticity (3274 MPa).

Explainable artificial intelligence method is used to understand the underlying phenomenon of the machine learning (ML) algorithm prediction. In this work, a powerful XAI technique, SHapley Additive exPlanations (SHAP) is implemented by inputting the trained XGBregressor ML model. The following 3D printing process parameters, layer thickness, wall thickness, infill density, infill pattern, nozzle temperature, bed temperature, print speed, material, fan speed are considered to predict the tensile strength, roughness and elongation. SHAP explanations clarify process parameters’ proportional and cumulative effects on anticipated qualities. The XGBoost model achieved a mean squared error (MSE) of 0.591 and root mean squared error (RMSE) of 0.769. SHAP visualization plots are presented to understand the patterns of interaction between the most influential process parameters. The plots revealed that layer height positively correlates with roughness, while nozzle temperature is the most influential factor for tensile strength. Infill density is key for elongation, with higher infill leading to higher predicted elongation. This knowledge can be used to prioritize parameter optimization and control for achieving desired material properties, ultimately leading to more reliable and consistent 3D printing processes.