Process Parameters Research Articles

The influence of laser process parameters on the Iron loss of grain-oriented silicon steel

Laser scribing effectively reduces the energy loss of grain-oriented silicon steel in alternating and pulsating magnetic fields. This study focuses on the 27Q120 material, analyzes the macroscopic surface morphology and iron loss variations of grain-oriented silicon steel under the influence of two types of laser spots (circular spots without the application of a diffractive optical element (DOE) and rectangular spots formed by a DOE), laser power (600W to 2400W), and scribing spacing (3.5mm to 7.5mm) as process parameters. The relationship between magnetic domain width, stress, and iron loss was analyzed. The result shows that the iron loss improvement rates (β) achieved with circular and rectangular spot scribing were 16.054% and 17.116%, respectively. Corresponding magnetic domain widths were 0.17754 mm and 0.1636 mm, with hysteresis losses of 0.5114 W/kg and 0.5033 W/kg, respectively. Under the same laser power and scribing spacing, a lower energy density significantly reduced the damage to the surface macroscopic morphology by the laser, leading to improved iron loss. Furthermore, the iron loss of grain-oriented steel is positively correlated with the width of magnetic domains and negatively correlated with compressive stress.

Track trajectory, molten pool and defect characterization in NiTi single-track selective laser melting (SLM) experiments

In order to explore the law of selective laser melting (SLM) forming of Nitinol alloy, and obtain high quality additive manufacturing parameters of Nitinol alloy, a series of single-track SLM experiments on Nitinol alloy were conducted under different combined parameters of laser power and scanning speed. The effects of laser power, scanning speed and linear energy density on track forming, molten pool evolution and pore formation were systematically discussed by characterizing the track trajectory, molten pool morphology, dimensions and defects of the molten pool, and the ideal process parameters were obtained. The experimental results revealed that the average track width and width uniformity decreased with the increase of the scanning speed, the spheroidization phenomenon and height fluctuation became more pronounced, and the phenomena of necking, subsection and interruption increased significantly at the fixed laser power. Under the fixed scanning speed, the width, height and width uniformity of the molten track gradually increased with the increase of the laser power, and the phenomena of spheroidization, subsection and interruption gradually reduced. Moreover, the upper zone of the molten pool is shaped like an inverted bowl, and the lower zone is mainly divided into five categories, which are lack of fusion, shallow bowl shape, deep bowl shape, keyhole shape and deeper keyhole shape under different process parameters. The defects of the molten pool mainly include lack of fusion and trapped gas. An ideal molten pool without defects can be obtained under the parameter conditions of 100-150W laser power and 100-200mm/s spanning speed.

Altered microstructure, phase transformation behaviors and shape memory response of CuAlMn shape memory alloys fabricated by selective laser melting

In this paper, nearly defect-free CuAlMn SMAs were successfully fabricated by selected laser melting (SLM). The result of microstructure show that only one type of thermal-induced martensite was detected in the prepared specimens, and the process parameters play a vital role in phase transformation behaviors. The alloy shows excellent ultimate tensile strength of 820 MPa and elongation of 11.5 %. Furthermore, the high shape memory response, i.e. shape recovery rate of 91.4 %–99.6 %, shape memory strain of 2.59 %–5.46 %, was presented under compressive strain not exceeding 8 %.

Formulation and Optimization of Solid Lipid Nanoparticle-based Gel for Dermal Delivery of Linezolid Using Taguchi Design.

Linezolid (LNZ) is a synthetic oxazolidinone antibiotic approved for the treatment of uncomplicated and complicated skin and soft tissue infections caused by gram-positive bacteria. Typically, LNZ is administered orally or intravenously in most cases. However, prolonged therapy is associated with various side effects and life threatening complications. Cutaneous application of LNZ will assist in reducing the dose, hence minimizing the unwanted side/adverse effects associated with oral administration. Dermal delivery provides an alternative route of administration, facilitating a local and sustained concentration of the antimicrobial at the site of infection. The current research work aimed to formulate solid lipid nanoparticles (SLNs) based gel for dermal delivery of LNZ in the management of uncomplicated skin and soft tissue infections to maximise its benefits and minimise the side effects. SLNs were prepared by high-shear homogenisation and ultrasound method using Dynasan 114 as solid lipid and Pluronic F-68 as surfactant. The effect of surfactant concentration, drug-to-lipid ratio, and sonication time was investigated on particle size, zeta potential, and entrapment efficiency using the Taguchi design. The main effect plot of means and signal-to-noise ratio were generated to determine the optimized formulation. The optimized batch was formulated into a gel, and ex-vivo permeation study, in-vitro and in-vivo antibacterial activity were conducted. The optimised process parameters to achieve results were 2% surfactant concentration, a drug-to-lipid ratio of 1:2, and 360 s of sonication time. The optimized batch was 206.3± 0.17nm in size with a surface charge of -24.4± 4.67mV and entrapment efficiency of 80.90 ± 0.45%. SLN-based gel demonstrated anomalous transport with an 85.43% in vitro drug release. The gel showed a 5 cm zone of inhibition while evaluated for in vitro antibacterial activity against Staphylococcus aureus. Ex-vivo skin permeation studies demonstrated 20.308% drug permeation and 54.96% cutaneous deposition. In-vivo results showed a significant reduction in colony-forming units in the group treated with LNZ SLN-based gel. Ex-vivo studies ascertain the presence of the drug at the desired site and improve therapy. In-vivo results demonstrated the ability of SLN-based gel to significantly reduce the number of bacteria in the stripped infection model. The utilization of SLN as an LNZ carrier holds significant promise in dermal delivery.

Jet cavitation-enhanced hydration method for the preparation of magnesium hydroxide

During the preparation of magnesium hydroxide via the hydration method, in-situ growth and agglomeration often inhibit the reaction. This study used active magnesium oxide as the raw material and employed jet cavitation technology to enhance the hydration process. Based on the growth process of magnesium hydroxide, the mechanism of jet-enhanced hydration was analyzed. The effects of reaction temperature (T), reaction time (t), solid-liquid ratio (s), and cavitation number (σ) on the hydration rate were investigated. An L25(54) orthogonal experiment explored the significance of each factor's impact on the hydration rate. The hydration products were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and a specific surface area analyzer. Results indicate that the factors affecting the hydration rate, in order of significance, are cavitation number > reaction temperature > solid-liquid ratio > reaction time. The optimal process parameters were determined to be a reaction temperature of 70 °C, reaction time of 80 min, solid-liquid ratio of 1:12, and cavitation number of 0.42. Under these conditions, the hydration rate reached 94.87 %, producing well-dispersed lamellar magnesium hydroxide with a narrow particle size distribution (median particle size D50 = 4.511 μm) and a BET specific surface area of 11.345 m²/g.

Modeling and Prediction of Surface Roughness in Ball End Milling of Oxygen-Free High Conductivity Copper Using Adaptive Neuro Fuzzy Inference System

Oxygen Free High Conducting Copper (OFHC) is one of the reasons materials for numerous applications due to its high thermal conductivity, great machinability, and great quality In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed as a predicting model of the machining performance of OFHC measured for surface roughness in terms of process parameters, namely, the cutting feed rate or feed per tooth, axial depth of cut, radial depth of cut, and the cutting speed.

Advancements in Biofiller-Reinforced PLA Composites for 3D Printing: A Review

Additive manufacturing is key in realizing highly complex and high-performance composite materials. Among all the various kinds of functionality that could be added to a composite component, continuous fiber-reinforced composites have been drawing much attention because of their attractive combination of properties. The comprehensive spectrum of knowledge that ranges from the basics concerned with structure, morphology, synthesis, physical, and chemical properties finally reaches to this analytical study of advanced composites. Most generally, such a composite is structured on a thermoplastic or thermoset polymer matrix that embeds the load-carrying reinforcing fibers; probably best known are carbon, glass, and aramid fibers. These composites, which involve fibers continuously reinforcing, have high strength relative to their weight. They are also anisotropic, meaning they have qualities that vary from one direction to another – something which might be customisable for specific load cases. They warp and shrink less during their life in an outside environment than conventionally made bioplastics due to a fact that short fibers can provide reinforcement to them. This 3-D printing object is manufactured by special additive manufacture technology during the manufacturing process after compounding and extruding the fiber-reinforced composite filament. The final properties of 3D printed parts could be tailorable through changing variables such a fiber type, fiber length, volume fraction, polymer matrix material, orientation of fibers, and printing process parameters. These continuous fiber-reinforced 3D printed composites could achieve tensile strengths up to one GPa, have stiffness values reaching even a rating of countless Gpa, and have ad thicknesses in the range from about 1.4–1.8 g/cm³. This finding could be applied in basically all major industries, from aerospace and the automotive industry to sports gear. Such conclusions from the analysis may be useful in the selection of appropriate materials and techniques while even guiding the development of new applications with respect to such advanced composite materials.

Investigation of mechanical properties in FFF- produced PLA samples using the Erichsen test: application of definitive screening and RSM

Purpose This study aims to investigate how printing parameters affect the mechanical properties of specimens produced through fused filament fabrication, using the Erichsen test to assess deformation characteristics and material durability under stress. Design/methodology/approach Polylactic acid (PLA) specimens were printed and tested in accordance with the ISO 20482 standard. Definitive screening was conducted to identify the most influential process parameters. This study examined the effects of four key process parameters – number of layers, layer height, crossing angle and nozzle diameter – on force, distension, peak energy and energy to break. Each parameter was assessed at three levels and a large number of required experiments was managed by using response surface methodology (RSM). Findings This study revealed that the number of layers, layer height and crossing angle are the most significant factors that influence the mechanical properties of 3D-printed materials. The number of layers had the greatest impact on the peak force, contributing 44.25%, with thicker layers typically enhancing material strength. The layer height has a significant effect on energy absorption and deformation, with greater layer heights generally improving these properties. Nozzle diameter contributed only 1.10%, making it the least influential factor; however, its impact became more pronounced in interactions with other parameters. Originality/value This paper presents a comprehensive experimental investigation into the effects of process parameters on the crack strength and behavior of 3D-printed PLA specimens using the RSM method. The documented results can be used to develop optimization models aimed at achieving desired mechanical properties with reduced variation and uncertainty in the final product.

Effect of preheating substrates and process parameters on cladding track geometry fabricated by laser cladding

Laser cladding (LC) is an advanced technique used for repairing large-scale components, such as rotors and valves, in thermal power plants and steel rolling mills. This technology is designed to facilitate efficient repairs, minimize repair costs, conserve materials, reduce processing time, and ensure adequate dilution between the substrate material and the deposited coating, while also enhancing corrosion resistance. In this research, the effects of preheating substrates and input process parameters on the geometric accuracy of single-track deposits, as well as the microstructure composition of multi-track deposits, were investigated. The Taguchi method was used to design an L9 orthogonal experimental array. The impact of process parameters, including substrate temperature, laser power, and scanning speed, on the morphology of a single track (track width, height, penetration depth, and dilution) was evaluated through ANOVA statistical analysis. SS316L stainless steel, recognized for its wear resistance, was used as the deposition material. The findings reveal that substrate temperature is the most critical factor influencing the dilution of the coating. Additionally, the microstructure of the deposited layers were systematically analyzed.

Modeling of drilling tool vibration in the process of drilling blast wells

Purpose. To determine the characteristic features and model bit vibration during its interaction with rock in the process of drilling technical (blast) wells in open pit mines. Methodology. The following methods were used in the work: analysis of scientific and practical solutions; statistical methods for processing the results of experimental studies; methods of analytical synthesis; computer modeling methods for synthesis and analysis of mathematical models. Findings. The process of interaction between a drill bit and a rock was analyzed. The conditions and causes of vibration of drilling equipment are determined. The spectral analysis of the vibration signal, the formation and analysis of the map of the order of rotation of the rotating parts of the drilling rig during the drilling of technical (blast) wells were performed to identify the bit in the frequency domain of the measured concomitant integrated vibration signal as a source of high-amplitude vibration. The modulated signal measured in the time domain at the specified frequency carries information about the physical and mechanical characteristics of the drilled rock and the state of the bit. The analysis of the experimental studies and modeling of the process of interaction of the bit with the rock allows us to conclude that the obtained statistical indicators of the accompanying vibration signal really adequately characterize the process of well drilling. Originality. A method for determining the characteristics of the interaction of a drill bit with rock in the process of drilling technical (blast) wells based on measuring the parameters of the accompanying vibration signal is proposed. The method differs from the known ones in the fact that in the process of changing the operating mode of the drive of the rotating parts of the drilling rig, an order map is formed over the entire range of its revolutions, the frequency of high-amplitude vibration of the bit is determined, which corresponds to a certain peak order of revolutions, and at this frequency the statistical parameters of changes are measured. Practical value. This approach to the process of drilling wells in open pit mines makes it possible to quickly determine the physical and mechanical characteristics of the drilled rock and adjust the process parameters accordingly to increase its productivity and energy saving.

Effect of surface square textures on the physical field of static and dynamic pressure thrust bearings and multi-objective optimization

Purpose The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge. Design/methodology/approach Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance. Findings The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2ºC compared to a smooth thrust bearing. Originality/value The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural network, and the improved multi-objective salp swarm algorithm was used to optimize the process parameters.

Optimizing tribological performance of 3D-printed poly(lactic acid) components through process parameter analysis

Optimizing tribological performance of 3D-printed poly(lactic acid) components through process parameter analysis

Surface Mechanical Property Prediction and Process Optimization of 18CrNiMo7-6 Carburized Steel Stator Guide Based on Radial Basis Function Neural Network and NSGA-II Algorithm

The carburizing process is a key technology that affects the mechanical properties of the surface of the hydraulic motor stator guide rail, and the related process parameters have an important influence on surface hardness, the thickness of the carburized layer, and the deformation of the guide rail. However, at present, the relationship between the carburizing process parameters and the surface mechanical properties of the target is not clear. This paper proposes a “hardness prediction and process parameter optimization” method. Firstly, a finite element model is established, with carburizing time, temperature, and carbon potential as the three input factors; the optimal Latin hypercubic experimental design and sensitivity analysis are applied. Secondly, surface hardness, carburized layer thickness, and deformation are taken as the output values, and an RBF neural network is used to construct the prediction model. The results show that the RBF neural network can be accurately used for the prediction of surface hardness, the thickness of the carburized layer, and deformation, and for the optimization of process parameters. The optimized parameters of surface hardness and the thickness of the carburized layer were increased by 4.2% and 5.1%, respectively, and the deformation amount was reduced to 0.31 mm, achieving the goal of optimal design.

Development of Bamboo Based Nylon 66 Composite through Friction Stir Processing-An Attempt

The concept of developing a bamboo-based Nylon 66 polymer matrix composite is a relatively novel and very new technical attempt through friction stir processing (FSP). This innovative approach can create a composite material that strengthen the mechanical properties of bamboo and the versatility of Nylon 66. However, successful implementation requires careful consideration of FSP process parameters such us tool rpm, traverse speed, tilt angle and tool design. Among the processing parameters, the tool rpm shows the significant role for severe plastic deformation and temperature generation which leads to the achieving excellent bond. Therefore, in this present study attempt has made to develop the polymer matrix composite (nylon 66 and bamboo powder) using FSW process at a variant rotational speed of 300,400 and 500 rpm with the constant traverse speed of 25 mm min-1. The deformation behavior and the peak temperature evolved under the tool shoulder during the stirring motion is studied using COMSOL Multiphysics simulation. It is found that, the specimen processed with higher rpm (500rpm) shows the higher volumetric strain and the peak temperature and it is evident that with increasing rpm the higher amount of severe plastic deformation occurred. Initially the process parameters are optimized without bamboo powder on the nylon 66 plate of 6 mm thickness and found that the 500 rpm processed specimen shows the defect free. Prior to the actual FSP, the small keyways taken on the center of the nylon 66 plate in order to add the bamboo powders in it. Actual processing was done with bamboo powder after adding natural bamboo powder of size 50µm. The result reveals that, the bamboo powder has partially expelled out during the period of FSP. This complete study concedes that the processing parameters needs to be optimized and also bamboo added method need to be studied for the successful development of bamboo-based polymer matrix composite.

A BIONIC-INSPIRED DRAGONFLY ALGORITHM FOR PARAMETRIC OPTIMIZATION AND DAMAGE INVESTIGATION DURING MACHINING OF MODIFIED POLYMER NANOCOMPOSITE

Polymer nanocomposite is commonly used to develop structural components of space, aircraft, biomedical, sensor, automobile, and battery sector applications. It remarkably substitutes the heavyweight metallic and nonmetallic engineering materials. The machining principles of polymer nanocomposites are intensely different and complex from traditional metals and alloys. The nonhomogeneity, abrasive, and anisotropic nature differs its machining aspect from conventional metallic materials. This investigation aims to execute the CNC drilling of modified nanocomposite using Graphene–carbon (G-C) @ epoxy matrix. The process constraints, namely, cutting speed (S), feed (F), and wt.% of graphene oxide (GO) vary up to three levels and are designed according to the response surface methodology (RSM) array. The nonlinear model is created to predict surface roughness (Ra) and delamination (Fd) on regression analysis. It has been found that the average error for Ra is 0.94% and for Fd it is 3.27%, which is acceptable in model predictions. The metaheuristics-based evolutionary Dragonfly algorithm (DA) evaluated the optimal parametric condition. The optimal setting prediction for the DA is observed as cutting speed (S)-37.68[Formula: see text]m/min, feed (F)-80[Formula: see text]mm/min, and wt.% of graphene oxide (GO)-1%. This algorithm demonstrates a higher application potential than the previous efforts in controlling Ra and Fd values. Both the drilling response values are found to be minimized when the cutting speed increases and the feed decreases. The best fitness value for the DA is 1.626 for surface roughness and 5.086 for delamination. This study agreed with the prediction model’s outcomes and the process parameters’ optimal condition. The defects generated during the sample drilling, such as fiber pull out, uncut/burr, and fiber breakage, were examined using FE-SEM analysis. The optimal findings of the DA module significantly controlled the damages during machining.

Tensile strength of friction stir additive manufactured laminated AA 6061/TiC/GS composites

Abstract With the fast progress of industrial manufacturing, friction stir additive manufacturing has fascinated wide-ranging consideration in the industry due to high material consumption rate. Friction stir additive manufacturing (FSAM), a newly developed solid-phase additive manufacturing technique was employed to fabricate AA6061/TiC/GS composite. The process parameters like tool rotational speed, transverse speed, tool tilt angle and type of tools used in friction stir additive manufacturing were analyzed. Taguchi’s L16 orthogonal array and ANOVA method was used to find the optimum process parameters for the tensile strength. Development characteristics of stirred zone, recrystallization and mixing of reinforced particles will significantly improve the mechanical properties of the fabricated composites. Microstructural investigation and fractography was done by using optical microscopy and scanning electron microscopy (SEM). Corrosion, wear behavior and elemental analysis through EDS was also performed for the fabricated material. The maximum tensile strength of 385.74 MPa was attained under optimal parameters of the tool rotational speed 1,200 rpm, transverse speed 55 mm min−1, and tool tilt angle of 1° for scrolled tapered octagonal tool pin. The findings of the linear regression model showed a minor variance between model and experimental values. Prominent results of the experiment were compared by few other researcher’s findings working in similar area.

Mathematical Modeling of Fluidized Bed Magnetizing Roasting of Iron Ore Fines

The fluidized bed magnetizing roasting of low‐grade iron ore fines is employed as a beneficiation technique in iron‐making and steel‐making industries. In the present work, the unreacted shrinking core reaction kinetic model is coupled with the two‐fluid and kinetic theory of granular flow gas–solid flow model to simulate magnetizing roasting of hematite to magnetite in iron ore fines using a fluidized bed reactor. The model is validated with published experimental findings. Thereafter, the influence of different process parameters such as gas temperature, composition, velocity, and particle size on the reduction fraction and rate along with mass fraction and emission is studied. The reduction rate increases with gas temperature and mass fraction while it decreases with particle size. The emission increases with gas temperature, particle size, and mass fraction. However, the influence of gas velocity on these parameters is not significant. The reduction rate and time vary from 0.0010 to 0.0067 s−1 and 65 to 553 s, respectively, at a reduction fraction of 0.5. The mass fraction and emission range from 0.80 to 0.92 and from 0.63 to 4.14 g kg−1 ore, respectively.

Parametric investigation of friction stir welding of aluminum alloy and Inconel 718 using finite element analysis

Friction Stir Welding (FSW) has emerged as a prominent technique for joining metallic materials, offering advantages in both similar and dissimilar material combinations. Despite its effectiveness, further advancements are needed to address industry demands and enhance the understanding of the FSW process. Welding nickel-based superalloys like Inconel 718 poses specific challenges, with existing literature suggesting that achieving successful welds requires high axial forces and precise process parameters, limiting its practical application in industries. To tackle these issues, this research proposes the utilization of 3D finite element models integrating multiphysics aspects, encompassing material flow and heat transfer mechanisms such as conduction, convection, and radiation. These models were validated against existing experimental data and employed to analyze temperature and strain distributions within the heat affected zone and weld nugget. The findings offer insights into the impact of various process parameters, including rotational speed, welding speed, normal force, cooling rate, and the effect of induction preheating, on key performance metrics like temperature profiles, grain size distribution, microhardness, and stress evolution. The outcomes underscore a notable correlation between process variables and performance indicators, facilitating a comprehensive understanding of the FSW process dynamics.

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.

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available.Scientific contributionOur research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.Graphical