Carbon-fibre reinforced PLA (PLA-CF) composites produced via Fused Deposition Modelling (FDM) offer high specific strength for semi-structural applications; however, optimizing their process parameters to balance load-bearing capacity with ductility under high-throughput conditions remains a critical challenge. This study establishes a robust multi-objective optimization framework combining Response Surface Methodology (RSM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to tailor the fracture mechanics of PLA-CF. By systematically investigating the synergistic effects of layer height, infill density, infill pattern, and printing speed (150-250 mm/s), the analysis reveals that infill density is the dominant driver for tensile strength (48.5% contribution), while layer height governs ductility. The hybrid RSM-TOPSIS approach successfully identified a practical "sweet spot" for industrial manufacturing: a configuration of 0.25 mm layer height, 80% infill density, Line pattern, and a high printing speed of 225 mm/s. This optimal set yielded a superior mechanical synergy of 30.42 MPa ultimate tensile strength and 7.64% elongation at break. Crucially, Scanning Electron Microscopy (SEM) confirmed that these parameters induce a transition from brittle delamination to a toughening mechanism characterized by extensive fibre pull-out and matrix fibrillation. These findings provide a validated pathway for fabricating tough, structural PLA-CF components at industrially viable production speeds.

Fused Deposition Modelling (FDM) additive manufacturing is being used more and more to create lightweight polymeric composites. However, because of the intricate dynamics of interfacial bonding, processing parameter optimization for multi-material sandwich structures continues to be a special challenge. This work systematically investigates the tensile and flexural behaviour of 3D-printed polylactic acid (PLA) and carbon fibre-reinforced PLA (PLA-CF) sandwich specimens using a Taguchi L27 orthogonal array design. Layer height (0.20, 0.25, 0.30 mm), infill pattern (Gyroid, Tri-Hexagon, Honeycomb), and printing speed (125, 175, 225 mm/s) are among the control elements assessed. Multiple linear regression, analysis of variance (ANOVA), and signal-to-noise ratio plots were used to analyse the generated dataset for Young's modulus, ultimate tensile strength (UTS), elongation at failure, and flexural characteristics. The statistical findings show that the infill pattern, which accounts for 48-80% of the property variance, is the most important element influencing mechanical performance. Quantitatively, the Triply Periodic Minimal Surface (TPMS) Gyroid pattern optimized tensile stiffness and ductility, whereas the Honeycomb core architecture produced the highest flexural strength and maximum ultimate tensile strength (similar to 28.5 MPa) because of effective planar stress distribution. Maintaining an ideal intermediate printing pace revealed to be physically necessary to guarantee correct interlayer fusion and avoid heat degradation during extrusion, even though variations in printing speed showed a slight statistical impact on the total property variance. Moreover, the stiffness and pseudo-ductility of the components were mostly affected by layer height. The suggested combination of sandwich architecture and parameter optimization offers a very efficient way to modify the mechanical performance of PLA-based composites for lightweight structural applications, which is supported by fractographic observations demonstrating improved interlayer bonding and fiber-matrix interaction under ideal conditions.

This research examines the parametric optimization of dual-material sandwich tensile specimens produced by Fused Deposition Modelling (FDM), utilizing Polylactic Acid (PLA) for the outer shell and Carbon Fiber Reinforced PLA (PLA-CF) for the infill. Through the use of a Taguchi L9 orthogonal array design, the effects of layer height, infill density, and infill pattern on mechanical properties and fracture morphology were assessed. The results indicate that a layer height of 0.30 mm, a concentric infill pattern, and an infill density of 90% produce the highest ultimate tensile strength (41.82 MPa) and Young's modulus (780.72 MPa), attributed to enhanced interfacial bonding and fiber dispersion. Scanning electron microscopy (SEM) analyses validate pristine PLA/PLA-CF interfaces and consistent carbon fiber distribution in optimized samples, with grid patterns notably augmenting ductility. The research concludes that the strategic selection of FDM parameters facilitates the creation of robust, lightweight, and high-strength PLA/PLA-CF sandwich structures appropriate for advanced engineering applications.

The mechanical reliability of high-performance polymer composite materials can be enhanced by optimizing the process parameters of Fused Deposition Modelling (FDM). A new lightweight polylactic acid (PLA) and carbon fibre-reinforced PLA (PLA-CF) sandwich structure is studied and optimized under the Taguchi L9 orthogonal array design to achieve the best tensile behaviour. The maximum ultimate tensile strength (σ UTS ) of 47.9 MPa, Young’s modulus ( E ) of 1526 MPa and elongation at break (Ɛ f ) of 5.19% was achieved with the optimal parameter combination of concentric infill pattern (Level 1), 0.1 mm layer height (Level 1) and 150 mm/s printing speed (Level 1). The concentric infill gave the best structural reliability (peak S/N ratio of 33.57 dB) using the signal-to-noise (S/N) ratio analysis. Analysis of Variance (ANOVA) showed that the infill pattern was the most significant factor, accounting for 36.1% of the variation in load-bearing capacity and 34.2% of stiffness. The results of the confirmation test confirmed the Taguchi regression model (R 2 = 94.74%, p = .029) and showed that it significantly enhances the mechanical performance compared to the baseline configuration. The fractography performed by the Quantitative Scanning Electron Microscopy (SEM) showed that both the printing speed and the internal porosity were increased, however, the energy-dissipative mechanisms, such as fibre pull-out (23.8 ± 12.4 µm), contributed to the preservation of structural integrity. The results provide a scalable approach for the production of affordable and defect-tolerant multi-material FDM composites that can be used for automotive and aerospace applications.

The present study investigates the influence of Fused Deposition Modelling (FDM) process parameters on the mechanical properties and surface quality of multi-material PLA-ABS Bi-layer Laminate Structures fabricated at elevated printing speeds. A Taguchi L9 orthogonal array was used to evaluate the effects of printing speed (150-250 mm/s), layer height (0.1-0.3 mm), and infill pattern (concentric, octagram spiral, and Hilbert curve) on tensile properties and surface roughness. The results show that a printing speed of 150 mm/s, a layer height of 0.1 mm, and concentric infill provided optimal mechanical performance, yielding a Young's modulus of 1252.29 MPa, an ultimate tensile strength of 42.17 MPa, and an elongation at break of 5.79%. Surface roughness analysis indicated the minimum roughness (Ra = 3.4 & micro;m) at a printing speed of 250 mm/s and a layer height of 0.2 mm using concentric infill. Statistical evaluation using mean effect plots, signal-to-noise ratios, and ANOVA revealed that layer height predominantly governs surface quality, while infill pattern and printing speed significantly influence tensile behaviour. Optical fractography and scanning electron microscopy showed that fracture initiation occurred at interlayer interfaces and inter-raster voids. Well-bonded specimens exhibited ductile micro-void coalescence, whereas poorly bonded samples failed by interlayer separation and brittle fracture propagation. These findings demonstrate that appropriate selection of layer height and infill pattern enables rapid fabrication of PLA-ABS Bi-layer Laminate components with improved mechanical performance and surface finish.

The fused deposition modelling (FDM) parameters for ABS components were optimised using a combined experimental and computational approach. A central composite design was used to fabricate 31 ASTM D638 Type IV tensile specimens by adjusting layer height, infill density, infill pattern, and printing speed. Elongation at failure varied from 5.1

Corrosion of boiler tubes at high temperature has been one of the serious issues resulting in maximum shutdown of boilers. Boiler tubes and superheaters are mainly made of stainless steel. In this paper, SS316 and Superni-718 have been used for comparative study of corrosion mechanism in actual husk-fired boiler environment. Flyash characterization has been reported in this paper along with SEM/EDS analysis of samples. XRD analysis has also been discussed to determine the phases formed on the specimens. An attempt has been made to develop a two-step corrosion mechanism for the corroded samples. The findings showed that for 800 h of boiler exposure, the minimum thickness loss of 0.29 mm was obtained for Superni-718 and so, minimum corrosion rate was also obtained for the superalloy whereas, SS316 showed an increase of 85.05

The main purpose of this paper is develop a semi-analytical model to use computationally efficient orthogonal beam-wise elastically restrained dynamic Timoshenko beam mode shapes in energy method for scrutinizing the accurate mode shapes of orthogonally stiffened Mindlin plates. The Dynamic Timoshenko trial functions are used in energy based semi-analytical Rayleigh-Ritz method to formulate Eigen value problem. The different aspect ratios of plate, plate thickness ratio, stiffener width ratio and stiffener height ratios are considered to demonstarte the model for various boundary conditions of plate for calculating frequency parameters and mode shapes. The formation of elliptical and circular nodal patterns with a combination of chess-board like configurations for both lower and higher modes has been seen.

PurposeFerritic steels are widely used to construct the superheater tubes for boilers present in power plants. Owing to the usage of variety of fuels, these superheated tubes get corroded very easily which account for major losses and unwanted shutdowns. The purpose of this study is to check the corrosion behaviour of Stellite 6 coated steel at high temperature under corrosive environment.Design/methodology/approachIn the present investigation, the effect of Detonation gun sprayed Stellite 6 coating deposited on T91 steel was reported under simulated oxidation and hot corrosion environment at 900 degrees C.SEM and XRD were done to characterize the corrosive product.FindingsIt was observed that the bare specimen underwent significant spallation, followed by the formation of a porous and massive oxide layer. Although the coating was successfully deposited and improved the corrosion resistance of the steel, major cracks were observed on the surface of the coated specimens.Originality/valueThermal spray coatings are widely used to deposit coatings on the components used in extreme environment. Variety of coating powders are available which can be deposited as per the requirements. However, it is necessary to evaluate the compatibility and durability of the coating with the specific alloy. Therefore, in the present investigation, Detonation gun sprayed Stellite 6 coating deposited on T91 steel was studied under simulated oxidation and hot corrosion environment at 900 degrees C.Peer reviewThe peer review history for this article is available at: Link to the website of web of science.

The layered manufacturing (LM) based locking bone plates (LBPs) are highly favored for biomedical applications due to its implant customization flexibility, control over porosity, and functional parameters. Design of experiments mainly relies on predefined experimental run orders; however, machine learning (ML) can handle complex and nonlinear relationships for predicting output responses. This research investigates the application of various ML models for predicting the impact strength, torque values, and punch shear strength of poly lactic acid (PLA) based LBPs. A dataset of 100 data points for each response variable was developed, analyzing the influence of printing parameters, like infill density (ID), layer height (LH), wall thickness (WT), and print speed (PS). The ID, LH, WT, and PS were varied within the ranges of 20%-100%, 0.1-0.5 mm, 0.4-1.2 mm, and 20-100 mm/s, respectively. Among the ML models evaluated, XGBoost and Adaboost exhibited strong alignment of the predicted values with actual values. The decision tree regression, showed the lowest predictive accuracy due to its tendency to overfit and lack of iterative refinement. The findings suggest that ML models can effectively predict the mechanical properties of PLA-based LBPs, offering valuable insights for optimizing printing parameters to enhance the performance of orthopedic implants. The findings can guide biomedical engineers in making data-driven decisions to enhance the strength of LBPs, thereby improving patient outcomes in orthopedic treatments.Highlights Predicted impact strength, torque, and shear strength of biomedical specimens. Evaluated 100 data points per variable to assess printing parameters' influence. XGBoost and Adaboost showed superior accuracy, with R2 consistently above 0.9. Decision Tree regression exhibited the lowest accuracy due to overfitting issues. Aims to guide biomedical engineers in data-driven decisions for better patient outcomes.

Locking bone plates (LoBPs) are utilized in orthopedic surgeries for supporting segments of distal ulna fracture. Primarily constructed from metallic biomaterials that are much stiffer than natural bone, LoBPs result in stress shielding and are prone to corrosion. As a result, there has been a growing preference for biocompatible and biodegradable polymeric biomaterials for creating patient-specific implants using 3D printing. Among various biomaterials, poly(lactic acid) (PLA) stands out due to its favorable biocompatibility and biodegradability. The layer-by-layer deposition in this process raises issues about layer bonding, reducing the mechanical strength of the implants. Nevertheless, adjusting process parameters can enhance the mechanical strength of the produced parts. The current study aimed to examine the influence of printing parameters on the impact strength and torque withstanding ability of biocompatible and biodegradable PLA-based LoBPs using response surface methodology. The experimental results reveal that an increase in infill density and wall thickness minimize porosity and enhance inter-layer bonding, imparting high impact and torsional resistance against forces. Conversely, an increase in layer height and printing speed induces porosity, leading to early fracture of layers under sudden impact and torsional forces. The fractured surface morphology of LoBPs after impact and torsional testing was analyzed using SEM. The MATLAB-based optimization yielded maximum impact strength and torque values of 27.175 kJ m(-2) and 3644 N mm, respectively. The study underscores the potential of biocompatible and biodegradable PLA-based 3D-printed LoBPs for sustainable integration into biomedical applications. (c) 2024 Society of Chemical Industry.

The three-dimensional (3D) printed poly lactic acid (PLA) bone plates lack mechanical strength, resulting in premature failure. Coating these plates with polydopamine (PDM) forms covalent bonds with the PLA molecular structure, enhancing their mechanical properties. The mechanical strength of the coated bone plates is influenced by infill density, submersion time, shaker speed, and coating solution concentration. However, conducting experiments for each parameter value to achieve maximum biomechanical tensile strength (BTS) and biomechanical flexural strength (BFS) is time-consuming and costly. Overall, the combination of response surface methodology (RSM) and machine learning (ML) enables determination of the best printing parameters, leading to reduced material waste, personalized bone plates tailored to individual anatomy, improved implant fit, and functionality. Moreover, this approach has the potential to reduce the need for additional surgeries and overall costs. To optimize coating parameters, this study employs RSM and ML techniques, including genetic algorithm (GA), particle swarm optimization (PSO), random search optimization (RSO), and differential evolution (DE). Experimental validation of the optimized process parameters and their corresponding fitness values is carried out using both RSM and ML approaches. The results demonstrate that GA has the closest relationship between experimental and fitness values, followed by DE, RSM, PSO, and RSO.

The designed biomedical implants require excellent shear strength primarily for mechanical stability against forces in human body. However, metallic implants undergo stress shielding with release of toxic ions in the body. Thus, Fused Deposition Modeling (FDM) has made significant progress in the biomedical field through the production of customized implants. The mechanical behavior is highly dependent on printing parameters, however, the effect of these parameters on punch shear strength of ASTM D732-02 standard specimens has not been explored. Thus, in the current study, the effect of infill density (IFD), printing speed (PTS), wall thickness (WLT), and layer thickness (LYT) has been investigated on the punch shear strength using Response Surface Methodology. The Analysis of Variance (ANOVA) has been performed for predicting statistical model with 95% confidence interval. During the statistical analysis, the terms with p-value lower than 0.05 were considered significant and the influence of process parameters has been examined using microscopic images. The surface plots have been used for discussing the effect of interactions between printing parameters. The statistical results revealed IFD as the most significant contributing factor, followed by PTS, LYT, and WLT. The study concluded by optimization of printing parameters for obtaining the highest punch shear strength.

xThe usage of fossil fuels such as coal is now being reduced and shifted to bio-fuel in the boilers. It is because fossil fuels are exhaustible and generate CO2 emission during burning. Whereas, bio-fuels are abundantly available at low cost and also produce fewer greenhouse gases. However, burning of such fuels create a lot of corrosive species which can damage the components used for construction of boilers. Ferritic, austenitic, and martensitic steels are widely used to construct various parts of the boiler. Hence, in this study, four different grades of steels known as T91, SS304, SS316, and SS410 have been placed in the actual boiler environment for 493h. The boiler utilizes wood chips, rice husk, sawdust, bamboo base and leaf cuttings as burning fuel and operates at 850 +/- 50 degrees C. The results indicate that T91 steel showed a maximum rate of corrosion followed by SS304, SS316, and SS410. Martensitic steel 410 showed the minimum loss in weight among other steels. No intergranular corrosion was noticed in 410 steel, but the oxide so formed on the surface was fragile and porous although the chromium content is less in SS410 as compared to SS304 and SS316.

Purpose Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates. Design/methodology/approach The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination ( R 2 ) and mean absolute error (MAE). Findings Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments. Research limitations/implications The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study. Originality/value This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

Purpose The purpose of this paper is to investigate the silt erosion performance of Bare, 75%Cr 2 O 3 + 25%Al 2 O 3 and 85%Cr 2 O 3 + 15Al 2 O 3 -coated SS304 under various control parameters such as rotation speed, concentration of silt and particle size of silt used for making slurry. This can provide insight for using chromia and alumina-based coatings for hydro-turbines. Design/methodology/approach Taguchi approach was used to identify the effect of three input parameters on the bare and coated alloys. L 16 orthogonal array is used for determining the signal-to-noise (S/N) ratio for each process parameter. For each level of parameters taken into consideration about the erosion wear, the arithmetic mean of the S/N ratio is calculated. On the essence of the results of S/N ratios, it is possible to determine the effect of the most dominating parameters of the erosion wear. Findings Results show that the erosion increases with an increase in silt concentration (Wt.%). It has been analyzed that the rotational speed has the most significant effect followed by the particle size and concentration on erosion wear for all uncoated and coated SS-304 samples. Maximum resistance to erosion is provided by 85%Cr 2 O 3 + 15%Al 2 O 3 . The least erosion wear for process parameters has occurred at the optimal parametric combination of rotational speed (N) = 415 rev/min, concentration (C) = 15 Wt.% and particle size range as <53 µm for uncoated and coated stainless steel. Originality/value The study clearly shows the silt erosion performance of chromia and alumina coatings of different compositions at different input parameters. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0028/

Distal ulna locking bone plates (DLBPs) are commonly employed in the treatment of distal ulna fractures. However, commercially available metallic bone plates experience stress shielding and lack corrosion resistance. Poly lactic acid (PLA) is highly favored biopolymer due to its biocompatible and bioabsorbable nature with human tissues. The use of additive layer manufacturing (ALM) is gaining attention for creating customized implants with intricate structures tailored to patient autonomy. ALM-based PLA bone plates must provide high resistance against impact and torsional forces, necessitating the adjustment of printing process parameters. This study focuses on examining the influence of key printing parameters, on the impact strength and torque-withstanding capability of DLBPs. Experimental results, along with microscopic images, reveal that an increase in infill density (IF) and wall thickness imparts strong resistance to layers against crack propagation under impact and torsional loads. On the contrary, an increase in layer height and printing speed leads to delamination and early fracture of layers during impact and torsional testing. IF significantly contributes to improving the impact strength and torque-withstanding capability of DLBPs by 70.53% and 80.65%, respectively. The study highlights the potential of the ALM technique in developing DLBPs with sufficient mechanical strength for biomedical applications.

The fabrication of poly lactic acid (PLA) bone plates (BPs) using extrusion-based additive manufacturing (EAM) lacks mechanical strength, which can be addressed with a biocompatible polydopamine (PDAM) coating. This study investigates the effect of ultrasonic vibrations on the mechanical behavior of PDAM-coated PLA BPs and examines the effects of varying infill density and infill pattern on surface morphology. PLA BPs were fabricated using EAM process and coated with PDAM, with and without ultrasonic assistance, under various process parameters. Surface morphology was analyzed under three conditions: 3DP, 3DP + Coating, and 3DP + UP assisted Coating at different infill densities and patterns. The results showed a 50.55-76.33% improvement in tensile strength and a 37.47-64.24% enhancement in flexural strength with varying infill density. Ultrasonic-assisted coating parameters also significantly improved both tensile and flexural strength of the 3DP BPs. The significance of these results lies in the ability to fabricate PLA BPs with enhanced mechanical strength, addressing the issue of PDAM particle accumulation at the container base with increase in coating solution concentration, during traditional direct immersion coating method. This study has potential applications in the BP manufacturing sector, enabling the production of patient-specific BPs with superior mechanical properties using ultrasonic-assisted coating process.

Poly Lactic Acid (PLA) based bone plates fabricated using Fused Deposition Modeling have poor mechanical strength which can be improved by biocompatible polydopamine (PDM) coating. However, PDM particles, being heavy in nature, settle at the container bottom with increase in coating solution concentration at the time of bone plate coating using dip coating technique. Thus, the present work aims to witness the effect of ultrasonic assisted coating parameters on tensile strength of coated bone plates. The coating parameters involving power of ultrasonic vibrations, coating solution concentration and immersion time were varied. The standard Response Surface Methodology (RSM) was applied and experimental trials were performed for obtaining tensile strength of bone plates under varied coating parameters. The objective of the present study was to compare the values of tensile strength predicted using RSM and machine learning (ML) models. Based on the obtained experimental values, gradient boosting regression (GBReg), linear regression (LReg) and random forest regression (RFReg) were trained and tested for predicting tensile strength of bone plates. The accuracy and prediction errors corresponding to RSM and ML based models were compared with respect to R2, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings revealed that GBReg exhibited R2, MSE, RMSE and MAE values as 0.9312, 1.7142, 1.2877 and 1.0861 respectively, while RSM showed R2, MSE, RMSE and MAE values as 0.882, 2.13, 1.4595 and 1.258 respectively. RSM model has shown minimum accuracy with high prediction errors amongst the four models. GBReg has outperformed other ML models in terms of their accuracy and error metrics. The present study therefore suggests the application of GBReg based ML model for predicting tensile strength of PDM coated bone plates in response to its accurate and robust prediction performance.