Hybrid optimization and prediction of mechanical properties of copper-enhanced ABS using Taguchi, ANN, and CSA–GWO algorithm

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.

Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

The article emphasizes and evaluates research on 3D printing in various environmental temperatures using fused deposition modeling (FDM). FDM is a dominant technology with high performance used in 3D printing (especially plastic) in Vietnam. 3D printed products depend on printing materials, printing design, layer height, infill speed, material shrinkage, flow, adhesion, support, cooling, heat dissipation, and temperature. In this article, we will conduct an in-depth study of the printing environment temperature for acrylonitrile butadiene styrene (ABS) material to test the influence of the printing environment temperature on the material’s mechanical properties. The printing environmental temperature is distributed from 30, 45, 60, and 75 °C, respectively. The study results show that for the printed sample to have the best mechanical properties, the printing environment temperature should be between 30 °C and 45 °C. with the recommended parameters: printing temperature 240 °C, printing angle 45 °C /-45 °C, filling density 100% and thickness 0.2 mm.

The use of 3D printing for manufacturing has assured to produce parts with complex shapes and designs. Fused deposition modeling (FDM) is one of the type of rapidly growing additive manufacturing technology. Poly (lactic acid) (PLA) or Acrylonitrile butadiene styrene (ABS) are the mostly used materials in Fused Deposition Modeling (FDM) machine. This paper is devoted to study the 3D printing parameters like influence of layer thickness and print speed on the mechanical properties of 3D-printed ABS. Nine Samples with three different layer height (0.1, 0.2 and 0.3 mm) and print speed (1000, 2000 and 3000 mm/min) were built using a FDM printer and their tensile strengths were tested using universal testing machine. The optimal mechanical properties of ABS material were found in sample of 0.2 mm layer thickness and at speed of 3000 mm/min.

Purpose This work aims to investigate the interaction effects of printing process parameters of acrylonitrile butadiene styrene (ABS) parts fabricated by fused deposition modeling (FDM) technology on both the dimensional accuracy and the compressive yield stress. Another purpose is to determine the optimum process parameters to achieve the maximum compressive yield stress and dimensional accuracy at the same time. Design/methodology/approach The standard cylindrical specimens which produced from ABS by using an FDM 3D printer were measured dimensions and tested compressive yield stresses. The effects of six process parameters on the dimensional accuracy and compressive yield stress were investigated by separating the printing orientations into horizontal and vertical orientations before controlling five factors: nozzle temperature, bed temperature, number of shells, layer height and printing speed. After that, the optimum process parameters were determined to accomplish the maximum compressive yield stress and dimensional accuracy simultaneously. Findings The maximum compressive properties were achieved when layer height, printing speed and number of shells were maintained at the lowest possible values. The bed temperature should be maintained 109°C and 120°C above the glass transition temperature for horizontal and vertical orientations, respectively. Practical implications The optimum process parameters should result in better FDM parts with the higher dimensional accuracy and compressive yield stress, as well as minimal post-processing and finishing techniques. Originality/value The important process parameters were prioritized as follows: printing orientation, layer height, printing speed, nozzle temperature and bed temperature. However, the number of shells was insignificant to the compressive property and dimensional accuracy. Nozzle temperature, bed temperature and number of shells were three significant process parameters effects on the dimensional accuracy, while layer height, printing speed and nozzle temperature were three important process parameters influencing compressive yield stress. The specimen fabricated in horizontal orientation supported higher compressive yield stress with wide processing ranges of nozzle and bed temperatures comparing to the vertical orientation with limited ranges.

: This research focuses on testing the flexural strength of Acrylonitrile Butadiene Styrene (ABS) materials used in 3D printing by the Fused Deposition Modeling (FDM) method. The objective of this study was to evaluate the mechanical strength of ABS using a full factorial experimental design, applying three main factors such as layer height, infill density and infill pattern. Flexural testing was conducted following ASTM D790 standards. A total of 27 specimens were made by varying the layer height, infill density and infill pattern. The results showed that layer height was the most influential factor on flexural strength, with the highest value of 41.815 Mpa at 0.2 mm layer height, 100% infill density, and line infill pattern. ANOVA analysis supported this conclusion with p values <0.05 for layer height, while infill pattern and infill density showed no significant effect. This study provides guidelines for the use of optimal parameters in ABS-based 3D printing processes.

This paper investigates the optimization of tensile strength, tensile strength per unit weight, and tensile strength per unit time of polyethylene terephthalate glycol (PETG) material in fused deposition modeling (FDM) technology using the Taguchi method and analysis of variance (ANOVA). Unlike previous studies that typically focused on optimizing a single mechanical property, our research offers a multi-dimensional evaluation by simultaneously optimizing three critical quality characteristics: tensile strength, tensile strength per unit weight, and tensile strength per unit time. This comprehensive approach provides a broader perspective on both the mechanical performance and production efficiency, contributing new insights into the optimization of PETG in FDM. The Taguchi method (L16 45) was designed and executed, with the layer height, infill density, print temperature, print speed, and infill line direction as the control factors. Sixteen tensile tests were conducted, and ANOVA was employed to identify the main influencing factors for each quality characteristic. For the tensile strength, the infill density was found to have the greatest impact (48.45%), while the print temperature had the least impact (0.78%). The optimal parameter combination reduced the quality loss to 31.28% and standard deviation to 55.93%. For tensile strength per unit weight, the infill line direction had the greatest impact (87.22%), whereas the print temperature had the least impact (0.77%). The optimal parameter combination reduced the quality loss to 54.09% and standard deviation to 73.54%. Regarding the tensile strength per unit time, the layer height had the greatest impact (82.12%), while the print temperature had the least impact (0.08%). The optimal parameter combination reduced the quality loss to 10.81% and standard deviation to 32.87%.

Acrylonitrile butadiene styrene (ABS) is a renowned commodity polymer for additive manufacturing, particularly fused deposition modelling (FDM). The recent large-scale applications of 3D-printed ABS require stable mechanical properties than ever needed. However, thermochemical scission of butadiene bonds is one of the contemporary challenges affecting the overall ABS stability. In this regard, literature reports melt-blending of ABS with different polymers with high thermal resistance. However, the comparison for the effects of different polymers on tensile strength of 3D-printed ABS blends was not yet reported. Furthermore, the cumulative studies comprising both blended polymers and in-process thermal variables for FDM were not yet presented as well. This research, for the first time, presents the statistical comparison of tensile properties for the added polymers and in-process thermal variables (printing temperature and build surface temperature). The research presents Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) to explain the thermochemical reasons behind achieved mechanical properties. Overall, ABS blend with PP shows high tensile strength (≈31 MPa) at different combinations of in-process parameters. Furthermore, some commonalities among both blends are noted, i.e., the tensile strength improves with increase of surface (bed) and printing temperature.

Additive manufacturing of acrylonitrile butadiene styrene (ABS) was investigated based on statistical analysis via an optimization method. The present article discusses the influence of the layer thickness (LT), infill percentage (IP), and contours number (C) on the maximum failure load and elastic modulus of the final product of ABS. ABS is a low-cost manufacturing thermoplastic that can be easily fabricated, thermoformed, and machined. Chemical, stress, and creep resistance is all excellent in this thermoplastic material. ABS combines a good balance of impact, heat, chemical, and abrasion resistance with dimensional stability, tensile strength, surface hardness, rigidity, and electrical properties. To comprehend the impact of additive manufacturing parameters on the build quality, both artificial neural network (ANN) and response surface method (RSM) were used to model the data. The main characteristics of the build considered for modeling were ultimate tensile strength (UTS) and elastic modulus. Main effect plots and 3d plots were extracted from ANN and RSM models to analyze the process. The two models were compared in terms of their accuracy and capability to analyze the process. It was concluded that though ANN is more accurate in the prediction of the results, both tools can be used to model the mechanical properties of ABS formed by 3D printing. Both models yielded similar results and could effectively give the effect of each variable on the mechanical properties.

The complex and non-linear nature of material properties evolution during 3D printing continues to make experimental optimization of Fused Deposition Modeling (FDM) costly, thus entailing the development of mathematical predictive models. This paper proposes a two-stage methodology based on coupling limited data experiments with black-box AI modeling and then performing heuristic optimization, to enhance the viscoelastic properties of FDM processed acrylonitrile butadiene styrene (ABS). The effect of selected process parameters (including nozzle temperature, layer height, raster orientation and deposition speed) as well as their combinative effects are also studied. Specifically, in the first step, a Taguchi orthogonal array was employed to design the Dynamic Mechanical Analysis (DMA) experiments with a minimal number of runs, while considering different working conditions (temperatures) of the final prints. The significance of process parameters was measured using Lenth’s statistical method. Combinative effects of FDM parameters were noted to be highly nonlinear and complex. Next, artificial neural networks were trained to predict both the storage and loss moduli of the 3D printed samples, and consequently, the process parameters were optimized via Particle Swarm Optimization (PSO). The optimized process of the prints showed overall a closer behavior to that of the parent (unprocessed) ABS, when compared to the unoptimized set-up.

Surface quality is one of the limiting aspects of additive manufacturing (AM). This paper presents the findings from a study to optimize Fused Deposition Modeling (FDM) process parameters to improve the surface roughness of the printed test specimen. Taguchi 3⁴ and L9 orthogonal array were used to design the experiment. Samples models of the same size were fabricated with an open source FDM printer using acrylonitrile butadiene styrene (ABS) material and were examined to see the structural differences. Taguchi method S/N ratio and means analysis was used to find the optimum process parameter for surface roughness. The results indicate that flow rate is the most influential process parameter towards better surface roughness, followed by layer height, printing temperature and print speed. The surface roughness of printed test specimen was found to be rougher with the increase in levels of flow rate. The flow rate is responsible for the unevenly aligned section of the deposited filament. It was discovered that the optimal proses parameter levels for surface roughness by the CR-10S Pro FDM machine are 0.1 mm of layer height, 90% of flow rate, 230°C of printing temperature, and 35mm/s of print speed. Thus, Taguchi method has proven to be a useful approach for optimizing parameters to improve the surface roughness of printed parts.

Artificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) processes. In addition, the characteristics of the networks have a direct impact on the performance of the models. In this work, a study about the use of ANN to model surface roughness in FFF processes is presented. The main objective of the paper is discovering how key ANN parameters (specifically, the number of neurons, the training algorithm, and the percentage of training and validation datasets) affect the accuracy of surface roughness predictions. To address this question, 125 3D printing experiments were conducted changing orientation angle, layer height and printing temperature, and measuring average roughness Ra as response. A multilayer perceptron neural network model with backpropagation algorithm was used. The study evaluates the effect of three ANN parameters: (1) number of neurons in the hidden layer (4, 5, 6 or 7), (2) training algorithm (Levenberg–Marquardt, Resilient Backpropagation or Scaled Conjugate Gradient), and (3) data splitting ratios (70%–15%–15% vs. 55%–15%–30%). Mean Absolute Error (MAE) was used as the performance metric. The Resilient Backpropagation algorithm, 7 neurons, and using 55% of training data yielded the best predictive performance, minimizing the MAE. Additionally, the impact of the dataset size on prediction accuracy was analysed. It was observed that the performance of the ANN gets worse as the number of datasets is reduced, emphasizing the importance of having sufficient data. This study will help to select appropriate values for the printing parameters in FFF processes, as well as to define the characteristics of the ANN to be used to model surface roughness.

The Additive manufacturing (AM) technology familiarize many innovative and monetary gains when compared to conventional manufacturing methods. This research work affords an insight for supplanting metallic materials like stainless steel, titanium, carbon steel by means of durable plastics namely ABS (Acrylonitrile Butadiene Styrene) and PLA (Polylactic Acid). The mechanical behaviour of additive manufactured ABS and PLA, produced by fused deposition modelling are investigated at different densities. The main idea of this research work is to identify the mechanical characteristics of ABS and PLA at various conditions and to develop 3D printed thin walled cylinders by fused deposition modelling. The thin walled cylinders contribute its applications in pressure tanks and other low-pressure processing equipment. The fabrication of thin walled cylinders involves parameters such as maximum operating pressure, temperature, density, and print orientation. The tensile and flexural tests are carried over AM thin walled cylinders to estimate its mechanical properties, durability and corrosion resistance behaviour. It is found that the AM thin walled cylinders fabricated using ABS and PLA has better flexibility, less weight, high impact resistance and corrosion resistance, and durability when compared to metallic materials in very less production time and cost, the wastage of materials is also being reduced. Results shows that the ABS has better tensile and flexural strength than PLA. This methodology provides a better approach for fabricating various components through bio degradable ABS and PLA to enhance modern manufacturing industries.

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 filament fabrication (FFF) 3D printing has been recently adopted in various industries and production processes. Three-dimensional printing (3DP) has gained significant popularity and is being adopted in schools, universities, and fabrication labs, as well as in science, technology, engineering, and mathematics (STEM) education curricula. The aim of this study is to evaluate the energy consumption and environmental impacts of multiple parts with different complexity levels based on various process parameters through FFF printing. This paper focuses on three material filaments: polylactic acid (PLA), tough PLA (T-PLA), and acrylonitrile butadiene styrene (ABS). The influence of geometric complexity, layer height, density, infill pattern, speed, and temperature on energy and the environment will be analyzed through a life-cycle assessment approach. Moreover, this study provides a set of guidelines for 3DP users in education for the energy-efficient and sustainable use of 3D printers. Our results reveal that for the proposed geometries, an energy increase of 8% is recorded for PLA when transitioning from the simple geometry to the very complex one. However, for ABS and T-PLA, no change in energy values due to geometric change is observed. Layer height is found to be the most influential parameter on energy consumption, with an increase of 59%, 54%, and 61% for PLA, ABS, and T-PLA, respectively. Printing temperature, on the other hand, is found to be the least influential parameter on energy and the environment. Furthermore, PLA is found to be the most environmentally friendly material, followed by ABS and T-PLA in terms of climate change, human toxicity, and cumulative energy demand impact categories. However, for the ozone depletion category, ABS contributes the most to environmental damage compared to T-PLA. The results suggest that PLA can be used for visual and prototype models, whereas ABS and T-PLA serve as good candidates for complex end-use applications and functional parts. The presented guidelines will assist 3DP users in the adequate and optimal use of 3DP technology in order to achieve resource efficiency, energy savings, and environmental sustainability.