Experimental Optimization of Green FDM Process Parameters: An Integrated MCDM Approach

Optimization of process parameters and evaluation of surface roughness for 3D printed nylon-aramid composite

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.

Fused deposition modelling (FDM) is one of the leading additive manufacturing technologies for producing plastic parts in a layer by layer manner d from a digital file. The actual levels of processed parameters of the manufactured parts using fused deposition modelling (FDM) are considered the major obstruction to optimize the mechanical properties. As a result of the FDM parameters and the interaction among them the parts’ mechanical properties of the manufactured parts are affected, thus, many experiments have been dedicated to find the proper parameter setting. This paper presents a study on the influence of three FDM process parameters (air gap, raster angle, and build orientation) on the flexural strength of the FDM Nylon 12 manufactured parts. Response surface methodology (RSM) is used as a statistical technique to analyse results. Analysis of variance (ANOVA) method is also used to test the significance of parameters. The results indicate that the air gap and raster angle are the most influential parameters affecting the flexural strength.

Surface roughness (SR) is one of the major parameters used to govern the quality of the fused deposition modeling (FDM)-printed products, and the FDM process parameters can be easily regulated in order to obtain a good surface finish. The surface quality of the product produced by the FDM is generally affected by the staircase effect that needs to be managed. Also, the production time (PT) to fabricate the product and volume percentage error (VPE) should be minimized to make the FDM process more efficient. The aim of this paper is to accomplish these three objectives with the use of the parametric optimization technique integrating the artificial neural network (ANN) and the whale optimization algorithm (WOA). The FDM parameters which have been taken into consideration are layer thickness, nozzle temperature, printing speed, and raster width. Experimentation has been conducted on printed samples to examine the impact of the input parameters on SR, VPE, and PT according to Taguchi's L27 orthogonal array. The ANN model has been built up using the experimental data, which was further used as an objective function in the WOA with an aim to minimize output responses. The robustness of the proposed method has been validated on the optimal combinations of FDM process parameters.

Additive manufacturing is an emerging method to produce customized parts with functional materials without big investments. As one of the common additive manufacturing methods, fused deposition modeling (FDM) uses thermoplastic-based feedstock. It has been recently adapted to fabricate composite materials too. Acrylonitrile butadiene styrene (ABS) is the most widely used material as FDM feedstock. However, it is an electrically insulating polymer. Carbon Nanotubes (CNTs) on the other hand are highly conductive. They are attractive fillers because of their high aspect ratio, and excellent mechanical and physical properties. Therefore, a nanocomposite of these two materials can give an electrically conductive material that is potentially compatible with FDM printing. This work focuses on the investigation of the relationships between the FDM process parameters and the electrical conductivity of the printed ABS/CNT nanocomposites. Nanocomposite filaments with CNT contents up to 10wt% were produced using a twin-screw extruder followed by 3D printing using FDM method. The starting material was pellets from a masterbatch containing 15 wt% CNT. Compression-molded samples of ABS/CNT were also prepared as the bulk baselines. The effects of CNT content and nozzle size on the through-layer and in-layer electrical conductivity of the printed nanocomposites were analyzed. Overall, a higher percolation threshold was observed in the printed samples, compared to that of the compression-molded counterparts. This resulted in the conductivity of the printed samples that is at least one order of magnitude lower. Moreover, at CNT contents up to 5 wt%, the in-layer conductivity of the printed samples was almost two orders of magnitudes higher than that in the through-layer direction. In ABS/3 wt% CNT samples, the through-layer conductivity continuously decreased as the nozzle diameter was decreased from 0.8 mm to 0.35 mm. These variations in the electrical conductivity were explained in terms of the CNT alignment, caused by the extrusion process during the print, quality of interlayer bonding during deposition, and the voids created due to the discrete nature of the printing process.

Machine learning for the intelligent analysis of 3D printing conditions using environmental sensor data to support quality assurance

PurposeThe purpose of the experimental investigation was to optimize the process parameters of the fused deposition modeling (FDM) technique. The optimization of the process was performed to identify the relationship between the chosen factors and the tensile strength of acrylonitrile butadiene styrene (ABS) and carbon fiber polylactic acid (PLA) thermoplastic material, FDM printed specimens. The relationship was demonstrated by using the linear experimental model analysis, and a prediction expression was established. The developed prediction expression can be used for the prediction of tensile strength of selected thermoplastic materials at a 95% confidence level.Design/methodology/approachThe Taguchi L9 experimental methodology was used to plan the total number of experiments to be performed. The process parameters were chosen as three at three working levels. The working range of chosen factors was the printing speed (60, 80 and 100mm/min), 40%, 60% and 80% as the infill density and 0.1mm, 0.2mm and 0.3mm as the layer thickness. The fused deposition modeling process parameters were optimized to get the maximum tensile strength in FDM printed ABS and carbon fiber PLA thermoplastic material specimens.FindingsThe optimum condition was achieved by the process optimization, and the desired results were obtained. The maximum desirability was achieved as 0.98 (98%) for the factors, printing speed 100mm/min, infill density 60mm and layer thickness 0.3mm. The strength of the ABS specimen was predicted to be 23.83MPa. The observed strength value was 23.66MPa. The maximum desirability was obtained as 1 (100%) for the factors, printing speed 100mm/min, infill density 60mm and layer thickness 0.2mm. The strength of the carbon fiber PLA specimen was predicted to be 26.23MPa, and the obtained value was 26.49MPa.Research limitations/implicationsThe research shows the useful process parameters and their suitable working conditions to print the tensile specimens of the ABS and carbon fiber PLA thermoplastics by using the fused deposition modeling technique. The process was optimized to identify the most influential factor, and the desired optimum condition was achieved at which the maximum tensile strength was reported. The produced prediction expression can be used to predict the tensile strength of ABS and carbon fiber PLA filaments.Practical implicationsThe results obtained from the experimental investigation are useful to get an insight into the FDM process and working limits to print the parts by using the ABS and carbon fiber PLA material for various industrial and structural applications.Social implicationsThe results will be useful in choosing the suitable thermoplastic filament for the various prototyping and structural applications. The products that require freedom in design and are difficult to produce by most of the conventional techniques can be produced at low cost and in less time by the fused deposition modeling technique.Originality/valueThe process optimization shows the practical exposures to state an optimum working condition to print the ABS and carbon fiber PLA tensile specimens by using the FDM technique. The carbon fiber PLA shows better strength than ABS thermoplastic material.

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

RSM based investigation of compressive properties of FDM fabricated part

Additive manufacturing (AM) is a sustainable manufacturing approach because it can lower emissions and have a lower negative impact on the environment. 3D-printed interference screws from poly-lactic acid, eco-friendly filament, by fused deposition modelling (FDM), were fabricated with different process parameters. The most popular technique for reconstructing the anterior cruciate ligament is interference screw fixation. This study considered the role of the AM sustainability paradigm in terms of material input for FDM and product manufacturing. This study considered the role of the AM sustainability paradigm in terms of material input for FDM and process efficiency by investigating the effect of the AM process parameters, such as printing temperature (PT), printing speed (PS), and infill percentage (IP) on the density and stripping torque (ST). Later, the degradation profile of the screw was analyzed using the weight loss of screws. The experimental design of the fully factorial design was with two levels for each process parameter. The significant process parameters and their interactions were through statistical analysis of the resulting design. Results have shown that PT, PS, and IP significantly influenced the density and ST of 3D-printed interference screws. In five weeks, 3D-printed interference screws started to lose weight.

Fused deposition modelling (FDM) is one of the most promising and widely used additive manufacturing (AM) technologies. FDM is commonly used to fabricate simple to complex 3D parts from polymeric materials. Different 3D-printed polymeric components manufactured using FDM technology exhibit various mechanical and load-bearing properties. They are utilized in various industries, particularly the medical industry, for load-bearing applications in bio-implants. Therefore, it is crucial to understand the mechanical characteristics, especially the fatigue behaviour, of these 3D-printed parts under cyclic loadings. This paper focuses on the dynamic behaviour of 3D-printed polymeric parts produced through the FDM process and under different loading modes. First, the FDM mechanism, the various polymeric materials used in FDM, and the effective FDM parameters are briefly introduced. Then, the paper comprehensively covers one of the most important mechanical characteristics of 3D-printed parts for load-bearing applications: their fatigue life under different printing conditions and cyclic loadings. The most influential factors on the fatigue behaviour of polymers include the type of polymeric material, surrounding environmental conditions, cyclic loading conditions, the type of testing specimen, and the FDM printing parameters (such as raster angle, infill density, nozzle diameter, and nozzle temperature). In addition to the anisotropic behaviour reported by various studies on 3D-printed parts using FDM, it has been concluded that the layered structure (with a large number of interfaces between the raster) and the presence of microholes are two factors that weaken the fatigue strength of 3D-printed structures compared to other fabrication processes. These conditions can be exacerbated by choosing inappropriate printing parameters, and often, the failure mechanism in 3D-printed parts fabricated with FDM is delamination.

Fused deposition modeling (FDM) is one of layer-manufacturing technologies that have been widely used for rapid prototyping applications in product design and development. Due to the intensive energy, rapid cooling and phase changes, parts made by FDM and other layer-manufacturing processes are deviated from the designed geometry, and some require laborious post-processing. Most severe form inaccuracies such as curl, warping, and delamination are attributed to the residual stress accumulations during prototype fabrications. This study investigates the FDM process, which comprises complicated heat and mass transfer phenomena coupled with mechanical loading and phase changes. A finite element analysis model using element activations has been developed to simulate the mechanical and thermal phenomena in FDM, and further used for residual stress and part distortion simulations. The model has also been used to study the toolpath effects on the FDM process. Toolpath patterns affect the residual stresses not only in the magnitude, but also in the distribution which shows stress concentrations aligned with the primary direction of the toolpath. Experimentally fabricated and measured prototypes show that the part distortion-center shifts distinctly due to different toolpath patterns, which is consistent with the residual stress characteristics in the simulations. From the simulations, it is also shown that the short-raster toolpath results in higher residual stresses, and thus possibly larger distortions, than the long-raster and alternate-raster patterns, both having similar stress distributions and distortion features.

Additive manufacturing (AM) technology has the ability to produce parts or products using data from 3D CAD models based on adding material. Fused deposition modeling (FDM) is among the most popular AM technologies wherein the plastic materials like acrylonitrile-butadiene-styrene filaments get added in the form of semi-molten plastic layers from bottom to top to produce the final product. Besides, the merits of using the FDM process, it faces challenges related to strength, dimensional accuracy, surface finish, and so on. The mechanical, tribological, and surface finish of functional parts is an essential consideration in FDM. In this work, the role of process parameters such as the part positions and raster inclinations involved in the manufacturing of parts by FDM has been evaluated experimentally to obtain the desired properties for reducing production time, the quantity of supporting material, and overall cost including maintenance costs. The study revealed that part position is a more significant parameter than the raster inclinations on the surface roughness and mechanical properties of the FDM parts. It also concludes with the proper values of part positions and raster inclinations for achieving optimal mechanical properties, roughness, and manufacturing costs to withstand operating loading conditions.

Digital twin (DT) and additive manufacturing (AM) technologies are key enablers for smart manufacturing systems. DTs of AM systems are proposed in recent literature to provide additional analysis and monitoring capabilities to the physical AM processes. This work proposes a DT framework for real-time performance monitoring and anomaly detection in fused deposition modeling (FDM) AM process. The proposed DT framework can accommodate AM process measurement data to model the AM process as a cyber-physical system with continuous and discrete event dynamics, and allow for the development of various applications. A new performance metric is proposed for performance monitoring and a formal specification based anomaly detection method is proposed for AM processes. Implementation of the proposed DT on an off-the-shelf FDM printer and experimental results of anomaly detection and process monitoring are presented at the end.

Additive manufacturing is gaining popularity nowadays due to its applications in the automotive, medical, aerospace industries, and sports to manufacture complex parts. Fused deposition modelling is an additive manufacturing process utilised widely due to its lower cost, fast prototyping and faster production time. The present study focuses on applying FDM to the composite material filament and the impact of parameters of FDM on the part quality. A composite filament material was prepared to combine PLA (Poly-lactic Acid) and banana fibre. Specimens were prepared using this composite filament by varying various parameters such as layer thickness, infiltration and build orientation. Flexural and tensile tests were performed as per standards. It was found that the material properties considered are significantly affected by percentage infill and build orientation. The ‘on edge’ build orientation provides better material properties as compared to the other two orientations. The tensile strength is observed to be 73% and 77% more in flat orientation and ‘on edge’ orientation, respectively, as compared to upright orientation. Also, the flat orientation and ‘on edge’ orientation respectively showed 60% and 70% more flexural strength than the upright orientation.