Significant Process Parameters Research Articles

Systematic Investigation into Laser Powder Bed Fusion of Ti-5553 through Single-track and Multi-layer Studies for Tailored Manufacturing Solutions

Laser Powder Bed Fusion (LPBF) is recognized as an appealing fabrication process for producing metallic parts with customized properties. In the current research, a comprehensive approach is employed to systematically correlate single-track and multi-layer fabrication, aiming to generate a reliable process map, assess the effect of process parameters on the properties of LPBF-made Ti-5553, and guide the manufacturing of tailored structures. Based on single-track morphology, melt pool geometry, and multi-layer density, 30 combinations of laser power and scanning speed were categorized into three groups to identify the desirable process parameters. The investigation of single-tracks and multi-layers reveals that deeper melt pools, created with higher energy input, result in a more elongated grain structure, higher α phase content, increased strength and hardness, and reduced ductility. It is observed that achieving higher ductility involves a slight decrease in strength. Specifically, a substantial increase of ∼65% in ductility occurs with only a ∼3.5% reduction in strength. Also, it is found that the volumetric energy density (VED) alone is not sufficient as a design parameter, and the significant process parameters (e.g., laser power and scanning speed) should be considered, as two samples with the same VED yield different properties.

Prediction of catchment efficiency in direct energy deposition using dimensional analysis and machine learning

Direct Energy Deposition (DED) is an additive manufacturing process used to fabricate thin/thick-walled structures and high-value component restorations. As blown powder-based DED gains prominence, catchment efficiency becomes imperative for enhancing material utilization and process effectiveness. This study focuses on predicting catchment efficiency within a DED process by investigating four significant process parameters: laser power, powder feed rate, carrier gas flow rate, and deposition speed. The primary objective is to devise a dimensionless number capable of predicting catchment efficiency based on a combination of these parameters, categorizing efficiency into low, medium, and high regions. This approach reduces the need for resource-intensive experimentation. The experimental validation of the proposed dimensionless number was conducted, showing an error rate of around 8%. Additionally, six machine learning classifier algorithms – Support Vector Machines, K-Nearest Neighbours, Decision Tree, Random Forest, Linear Regression, and Gaussian Naive Bayes – were employed to categorize catchment efficiency zones. Support Vector Machine demonstrated the best performance with a predictive accuracy of 0.98. This study provides a methodology for catchment efficiency prediction, enhancing decision-making and resource optimization, and can be used to optimize process parameters for thin-wall or relatively thin-wall fabrication.

Experimental Study on Dry Milling of Stir-Casted and Heat-Treated Mg-Gd-Y-Er Alloy Using TOPSIS

This study examines the dry milling process of a rare-earth-based magnesium alloy, emphasizing the optimization of the milling parameters and their impact on the surface quality, cutting forces, and the rate of material removal. The objective is to improve our comprehension of the milling behavior of the Mg-Gd-Y-Er alloy. The Taguchi technique is adopted to formulate the experimental design. This study methodically investigates the influence of heat treatment (T4 and T6) on milling performance, and the effects of speed, feed rate, and depth of cut. The output variables considered for this investigation are the surface roughness (Ra, Rz, Sa, and Sz), material removal rate (MRR), and cutting force. To optimize the milling parameters and achieve superior outcomes, the multi-objective optimization technique TOPSIS is used. At a feed rate of 150 mm/min, a spindle speed of 1500 rpm, and a depth of cut of 1 mm, the T4-treated sample exhibits a minimum surface roughness value of 0.0305 µm. The highest resultant force values of 96.4416 N and 176.1070 N for 200 °C and 225 °C T6-treated alloys are obtained by combining process parameters such as a spindle speed of 1500 rpm, a feed rate of 50 mm/min, and a depth of cut of 1.5 mm. Furthermore, the maximum closeness coefficient value is achieved by combining a spindle speed of 1000 to 1500 rpm, a feed rate of 150 mm/min, and a depth of cut of 0.5 mm to 1 mm. The closeness coefficient value is significantly influenced by the most significant process parameters, as indicated by the ANOVA results.

Intelligent industry-oriented oro-solid formulation of famotidine: Advanced statistical optimization and Ex-Vivo characterization

BackgroundSol-Gel is the conventional approach for in-situ formulation having several limitations, including instability, stringent storage conditions, dose inaccuracy, inconsistent drug release, and patient inconvenience. ObjectivesThe aim of the present investigation was to design and characterize an intelligent oro-solid in-situ targeted dosage form of Famotidine (FTD) incorporating the cross-linked chitosan-vanillin (CrL-CSV) which instantly converted into a gel upon contact with acidic media. Material and MethodsCrL-CSV was designed using microwave-assisted method inculcating FTD and vanillin (VNL). Interaction was observed using FTIR and DSC. QbD was applied to develop intelligent formulation. Taguchi design was accomplished to screen significant critical material attributes (CMAs) and process parameters (CPPs). Box-Behnken design (BBD) was used to correlate the CMAs/CPPs: the amount of CS, VNL, microwave time, and critical quality attributes (CQAs): % FTD release, mucoadhesive strength, and viscosity. CrL-CSV were evaluated using various in-vitro, ex-vivo characterization and stability. Results and DiscussionFTIR and DSC revealed no interaction between the FTD and excipients. The amount of CS, VNL, and microwave time were screened as significant variables due to the high S/N ratio and delta value from the Taguchi design. A significant effect of CMA/CPP on the CQAs was observed in BBD. The optimum CrL-CSV was designed accomplishing 8.74 % chitosan (CS), 0.94 % VNL exposing 80sec in microwave. Immediate gelling (<1min.), desired viscosity (51432.6cps), optimal mucoadhesion strength (0.40 N), better control on drug release (91.5 % in 8 h) and high stability were achieved in the optimum batch of CrL-CSV. ConclusionAn intelligent formulation inculcating the CrL-CSV was developed, which has instant gelling time, high mucoadhesion time, and strength, and controlled release with desired physical properties. Progressive tools like Taguchi and BBD was explored. The formulation can be helpful at industry as it increases patient compliance, processing and stability.

A mini‐review of intensified synthesis of 1st and 2nd generation biofuels in the presence of perovskite catalysts

AbstractEnhancement of a sustainable environment through the choice of a selective catalyst with high activity, regeneration nature, and high stability is an important aspect to be focused on to achieve a high yield and maximum conversion of feedstock to biodiesel (1st generation biofuel), and also in the biomass valorization/pyrolysis (2nd generation biofuel synthesis). Depending on the nature of the catalyst and synthesis method adopted for biofuel production and biomass valorization, the variations in the process conditions, final yield, and conversion are varied accordingly. A prospective development and application of perovskite catalysts in the synthesis of 1st and 2nd generation biofuels using various process intensification strategies for the development of a clean and green environment is reviewed in this study. The synthesis of types of perovskite catalysts polycrystalline, nano‐sized, and powdered oxide are also discussed in this review. It is also concluded that, apart from other process parameters, molar ratio is one of the most influencing sensitive factors in the case of 1st generation biofuel synthesis, whereas during the production of 2nd generation biofuels, catalyst concentration and liquid–solid ratio are more significant process parameters that change based on the nature of the catalyst selected for the reaction.

Development of a hybrid model to estimate surface roughness of 3D printed parts

The research aims to develop a hybrid model to estimate the surface roughness of 3D printed parts, considering the significant process parameters and measurement uncertainties inherent in additive manufacturing (AM). The methodology involves a Taguchi L-27 orthogonal array design of experiments (DOE) to identify significant factors. This is followed by a deep dive analysis of the roughness profile and the modelling of the measurement process uncertainties. The ANOVA tests reveal that build orientation and layer height are critical contributors to surface roughness. However, it was evident from the experimental analysis that the layer height and build orientation shares a complex relation on surface roughness. The two novelties of this paper are: one, the development of a hybrid model of surface roughness by mathematically quantifying the staircase (step and stack) and corner geometry effects of the part geometry. Two, strengthening the model’s surface roughness prediction accuracy by incorporating the stylus flight and probe reachability measurement uncertainty functions. The final mathematical function was validated with multiple experimental data as well as literature data. The results show that the proposed model is robust to estimate the surface finish accurately at build orientations in the full range of 0–90 degree. The statistical analysis shows that the proposed model is capable of accurately predicting about 90 % variations in the data at different layer heights and build orientations. The proposed model is useful to accurately predict the surface finish of 3D printed parts, a priori, during the part design or before printing, thereby improving the surface quality of the AM parts.

Parametric Screening of Fermentation Conditions for Protease Production from Pineapple Waste

Pineapple waste is abundant in cellulose, hemicellulose, lignin, complex carbohydrates, and protein. One simple way to use pineapple waste as a substrate is to utilize it with the probiotic beverage to produce enzymes through submerged fermentation. Studies on fermentation using pineapple waste have mainly focused on cellulose, xylanase, and pectinase, with little information on protease fermentation. Future studies on pineapple waste-based protease fermentation should be conducted. The present work aims to investigate the significant process parameters affecting protease fermentation from pineapple waste. The parametric screening was performed using the two-level fractional factorial design by Design Expert 7.1.6. In this work, four process parameters were manipulated for protease fermentation which is incubation time from 24 hours to 72 hours, temperature from 20.0 °C to 40.0 °C, substrate concentration from 10.0 % to 30.0 % and pH value from 4.0 to 8.0. The result showed that the most significant process parameters affecting protease fermentation were temperature, pH value and incubation time. This study investigated that the highest protease activity, 0.118 U/mL can be obtained with 48 hours of incubation time, 30.0 °C of temperature, 20.0 % of substrate concentration and pH 6.0.

Experimental Modeling, Statistical Analysis, and Optimization of the Laser-Cutting Process of Hardox 400 Steel.

Fiber laser cutting machines are widely used in industry for cutting various sheet metals. Hardox steel is widely used in the construction of machinery and equipment that are subjected to wear and impact due to its anti-wear properties and good impact resistance. In this experimental study, the effect of input parameters including laser output power (LOP), laser-cutting speed (LCS), and focal point position (FPP) of fiber laser on the surface roughness and kerf width of Hardox 400 steel sheets are studied. In addition, the optimization of input parameters to achieve the desired surface roughness and kerf width are investigated and analyzed using the response surface methodology (RSM). The experiments are performed using a 4 kW fiber laser-cutting machine and the output results including surface roughness and kerf width are measured using roughness meters and optical microscope. The results of the analysis of variance (ANOVA) for surface roughness and kerf width show that the FPP and LCS are the most significant process parameters affecting the surface roughness and kerf width. With a positive focal point, the surface roughness decreases while the kerf width increases. With increasing the laser-cutting speed, both the surface roughness and kerf width decrease.

Multi-objective optimization of hot embossing process for high-quality glass micro gratings

This study proposed a multi-objective optimization strategy to produce high-quality glass micro gratings. The applied methods include factor screening, surface response methodology (RSM), non-dominated sorting genetic algorithm (NSGA-II) and decision-making. Specifically, the significant factors were selected among several process parameters in hot embossing through screening experiments. Afterward, the regression models for the transferred ratio, uniformity of glass microstructures and warpage concerning those significant process parameters were established. Meanwhile, the significance of selected factors and their interactions was analyzed. Based on the regression models, the Pareto front was obtained by using NSGA-II program, where the top 10 solutions were applied in glass hot embossing. Finally, a decision-making strategy was employed to further choose a feasible solution that allows the fabrication of glass micro gratings with a high transfer ratio, high uniformity, low warpage, low surface roughness, and no defect. The experimental results indicated that embossing temperature has the most significant effect on the replication accuracy of glass micro gratings, followed by embossing force and embossing time. However, warpage was not significantly sensitive to any process parameters in this hot embossing process. The defect-free glass micro gratings could be eventually fabricated under the optimal process parameters, achieving a transfer ratio and uniformity of higher than 95 % and 83 %, respectively. Moreover, their Ra surface roughness and warpage can be lower than 13 nm and 1.5 μm. The satisfactory quality of embossed micro gratings revealed that the proposed multi-objective optimization strategy is feasible in glass hot embossing.

Powder-bed 3D printing by selective activation of magnesium phosphate cement: Determining significant processing parameters and their effect on mechanical strength

The present work addresses powder bed binder jetting additive manufacturing by selective magnesium phosphate cement activation. Despite the potential of this technology to aid the digitalization of the construction industry, the effect of processing parameters on the mechanical performance of printed materials has not yet been studied to generate a guideline for the further development of the technology. Statistical methodologies were used to screen the effect of four printing process parameters (printing speed, layer thickness, raster angle, and build direction on flexural and compressive strength). As the exploited technology works with constant fluid pressure, the physical interpretation of the effect of each factor can be considered taking into account the interactions between the binder materials in the powder bed. Analysis of variance (ANOVA) indicated that printing speed and layer thickness significantly affect mechanical performances. Furthermore, the layout of samples for the printing process is preferable to be parallel the printhead movement. An anisotropic behavior was observed, and the samples subjected to compressive forces parallel to the layer plane possessed lower strength values. This effect can be interpreted as a result of a weak area of low density in between layers, leading to a pronounced delamination under compression.Even though the strength of the printed material is not suitable for a structural concrete, it can be marginally improved by design of experiment and optimized for non-structural applications, such as for porous artificial stone. Design of experiment coupled with ANOVA methods can be used in the future to support the development of novel material mixtures, thus expanding the fields of application of this novel additive manufacturing technology.

Multi-Criteria Optimization of Al2O3-ZrO2(Y2O3)-based Self-lubricating Composite Cutting Tools for Increasing Shear Angle, and Wear Resistance in Machining Processes

Abstract In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt.% and CaF2 at 5Wt.% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.

Application of local fatigue strength approach to assess and optimise the impact of deep rolling on the fatigue performance of railway axles

Deep rolling is a frequently used post-treatment process to increase the service life of railway axles. Through the process application, the surface roughness is reduced, the surface is work-hardened and compressive residual stresses are introduced in the near surface region in a controlled manner. These three effects have a positive impact on service life, and in the investigated case of railway axles, the introduced residual stress state constitutes the most significant potential. The compressive residual stresses introduced near the surface, which are responsible for the positive lifetime effect, are inevitably balanced by compensating tensile residual stresses below the surface. Therefore, it is generally challenging to estimate the actual potential of the process application properly. Classical design methods consider only the condition at the surface and are thus not suitable for a surface layer-based analysis. In the present publication, the concept of local fatigue strength is applied for the fatigue strength assessment of deep rolled railway axles. The locally permissible fatigue strength at each surface-layer depth of the railway axle is determined as a function of local properties and compared with the locally occurring load. This allows a proper fatigue design in depth and, at the same time, the estimation of the critical point at which the potential of crack initiation and thus failure occurs. Based on the methodology presented, it is possible to determine the fatigue benefit achieved by deep rolling railway axles considering the influence of various deep rolling parameters, and to further systematically optimise the significant process parameters. An increase in fatigue strength of 4.7 % can be proven by deep rolling the railway axle with reference parameters compared with the not deep rolled state. By applying the proposed optimisation, fatigue strength can be further increased up to 10.4 %.

Integrated approach to Wire Arc Additive Manufacturing (WAAM) optimization: Harnessing the synergy of process parameters and deposition strategies

The flexibility of Additive Manufacturing (AM) technologies in the metal 3D printing process has gained significant attention in research and industry, which allows for fabricating complicated and intricate Near-Net-Shape (NNS) geometry designs. The achievement of desired characteristics in Wire-Arc Additive Manufactured (WAAM) components is primarily contingent upon the careful selection and precise control of significant processing variables, including bead deposition strategy, wire materials, type of heat source, wire feed speed, and the application of shielding gas. As a result, optimizing these most significant process parameters has improved, producing higher-quality WAAM-manufactured components. Consequently, this has contributed to the overall rise in the method's popularity and many applications. This article aims to provide an overview of the wire deposition strategy and the optimization of process parameters in WAAM. The optimization of numerous wire deposition techniques and process parameters in the WAAM method, which is required to manufacture high-quality additively manufactured metal parts, is summarised. The WAAM optimization algorithm, in addition to anticipate technological developments, has been proposed. Subsequently, a discussion ensues regarding the potential for WAAM optimization within the swiftly growing domain of WAAM. In the end, conclusions have been derived from the reviewed research work.

A STUDY ON MULTI-OBJECTIVE OPTIMIZATION OF PULSED TIG WELDING PARAMETERS AND THEIR EFFECT ON JOINT PROPERTIES OF HIGH MANGANESE STEELS USING TAGUCHI-BASED GRA APPROACH

This investigation focuses on the study of the effect of process parameters like peak current ([Formula: see text], base current ([Formula: see text], pulse frequency ([Formula: see text], shielding gas flow rate ([Formula: see text] on mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and flexural strength (FS) of the welded joints during pulsed TIG welding of SAILMA 450 and EN14 B steels. Taguchi’s L[Formula: see text] orthogonal array has been used for conducting the tests. Multi-objective optimization has been performed using Grey relational analysis (GRA) in order to maximize the mechanical strength and to find out the optimal set of parameters. The optimum parametric combination is obtained at a peak current of 220[Formula: see text]Amps, base current of 120[Formula: see text]Amps, pulse frequency of 5[Formula: see text]Hz and shielding gas flow rate of 17[Formula: see text]l/min. Analysis of variance (ANOVA) is used to predict the significant process parameters. It has been observed from the ANOVA analysis that peak current and pulse frequency have more influence on the output responses than the shielding gas flow rate and base current. The results of the confirmatory test show an improvement of 0.5801 in the GRG, which is satisfactory. A microstructure study has been performed using scanning electron microscopy (SEM) for the optimal set of process parameters.

Parametric study of WEDM of titanium grade 12 using RSM and desirability approach

The present experimental study aims to scrutinize the effect of different process parameters on the material removal rate (MRR) and surface roughness (SR) of titanium grade 12 alloy during wire electro-discharge machining (WEDM) using response surface methodology. Four parameters (pulse-on time, pulse-off time, wire feed and gap voltage) and three levels of each selected variable are considered to conduct the experimental work. Depending on experimental results, a mathematical model is generated for both MRR and SR. An analysis of variance (Anova) study is performed to find significant process parameters. The Anova results show that the developed models for both MRR and SR are significant, and pulse-on time is found to be the most significant parameter. Additionally, the desirability approach is used to scrutinize the single-objective and multi-objective criteria of response variables. The desirability functions for all of the cases are found to be 1. The maximum MRR is observed to be 12.4845 mm3/s, while the minimum value of SR is found to be 1.4911 μm. For multi-objective optimization, the maximum value of MRR and the minimum value of SR are obtained as 12.0942 mm3/min and 1.7167 μm, respectively. Finally, the surface morphology of the machined surfaces is examined using scanning electron microscopy micrographs.

Optimization and modelling of WCEDM process parameters for GZR-AA7475 HMMC using GRA and Taguchi approach

Purpose This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite. Design/methodology/approach To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (Ip), pulse-on-time (TON) and flushing pressure (PF) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses. Findings Results from the current study concluded that the optimal condition for grey relational grade is obtained at TON = 105 µs, Ip = 100 A and PF = 90 kg/cm2. Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure. Originality/value Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

Optimising 3D printing parameters through experimental techniques and ANOVA‐Based statistical analysis

AbstractAdditive manufacturing (AM) has revolutionized the manufacturing industry by enabling the fabrication of complex geometries and designs with ease. 3D printing—fused deposition modeling (FDM) has emerged as a prevalent technique, owing to its versatility and cost‐effectiveness. However, the FDM process is complex and depends on multiple parameters, which makes it challenging to obtain high‐quality and consistent 3D printed components. The purpose of this study is to simplify the printing process for users and potentially improve the overall quality and consistency of printed objects. This research delved into optimising 3D printing parameters, specifically raster orientation and in‐fill speed, for PLA material through three experimental studies. The mean effect of these parameters and the effects of their interaction through analysis of variance (ANOVA) on tensile properties were also discussed. Initial experiments identified the most suitable parameters and its optimal values for PLA, which were then applied to five different materials: PETG, PLA tough, Recycle PLA, Plain PLA, and ABS. Tensile tests assessed the printed parts, and scanning electron microscopy (SEM) was employed to analyze fracture interfaces and material failure causes. This study identified a raster of 45°/−45° and 30 mm/s infill speed as optimal for diverse 3D printing materials. Notably, ABS, PETG, and tough PLA exhibited better tensile strengths, surpassing manufacturer benchmarks. However, Plain PLA and Recycled PLA, despite lower tensile strengths, proved valuable for specific applications. Interestingly, all tested materials showed greater flexibility than manufacturer recommendations, suggesting their suitability in scenarios needing both strength and flexibility. This study's results offer promising avenues for refining 3D printing practices, to the advantage of all users. The findings from this study offer significant insights for future research to investigate the effect of other process parameters on the quality of 3D printed parts, leading to further advancements of AM.Highlights Optimised 3D printing parameters. Applicability of optimised settings extended across various materials. ABS, PETG, and tough PLA exceeded manufacturer benchmarks in tensile strength. Experimental and ANOVA findings are in good agreement, revealing significant process parameters.

The Application of Box-Behnken Design for Investigating the Supercritical CO2 Foaming Process: A Case Study of Thermoplastic Polyurethane 85A.

Thermoplastic polyurethane (TPU) is a versatile polymer with unique characteristics such as flexibility, rigidity, elasticity, and adjustable properties by controlling its soft and hard segments. To properly design and understand the TPU foaming process through supercritical CO2, a design of experiments approach, the Box-Behnken design (BBD) was adopted using commercial TPU 85A as the model compound. The effect of saturation pressure, saturation temperature, and immersion time on the mean pore size and expansion ratio were investigated. The design space for the production of TPU foam was shown, and the significance of process parameters was confirmed using the analysis of variance (ANOVA). In addition, extrapolation foaming experiments were designed and validated the feasibility of the response surface model developed via BBD. It was found that the pore size of TPU 85A foam could be controlled within 13 to 60 μm, and a stable expansion ratio could be designed up to six.

Surface Modification of Magnesium Alloy AZ91D Using Nanopowder Mixed Electrical Discharge Machining for Biodegradable Implant.

Powder-mixed electrical discharge machining (PMEDM) enhances the effectiveness of the electric discharge machining process. It has been used on the Mg alloy AZ91D to address biodegradation concerns in implants. By combining nano-conductive powder particles with the dielectric fluid, PMEDM creates a functional surface. Process parameters like pulse on time, pulse off time, peak current, and powder concentration are examined to optimize material removal rate (MRR), surface roughness (SR), and white layer thickness (WLT). The optimization of input parameters was completed using the Taguchi L9 technique and further analyzed using ANOVA technique that illustrates Ton and pulse-off time as more significant process parameters for powder mixed electric discharge machining as compared with electric potential and peak current. The optimal surface roughness value is found to be 2.215 μm at 3A pulse current and 15 μs Toff time which suggest the material to be suitable for implants.

Mechanistic modelling of drug dissolution for additively manufactured polymeric tablets: A novel layer-by-layer approach

Additive manufacturing has been consolidating its position in various fields over the last few decades. The development of drug delivery systems like oral tablets have been successfully accomplished using this ingenious technology. The effectiveness of an oral tablet can be analysed by its drug release behaviour and this study aims to predict this release by developing a model for a 3D printed tablet dissolution. A polymer-dissolution based mathematical model have been developed by considering a novel layer-by-layer approach that is found during additive manufacturing procedure. The shape and volume of the tablet have been found to be crucial in predicting the drug dissolution for a given time. Further, the model was verified against the drug dissolution trend obtained from a Fused Deposition Modeling (FDM)-printed polyvinyl alcohol (PVA) tablet having benidipine hydrochloride drug substance. Four different shapes have been considered to test the robustness of the model. Further, the sensitivity test have been conducted on the model and it revealed detachment rate and layer thickness as significant process parameters.