Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing.

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity-time curves, acquired by a rubber process analyser for styrene-butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

Influence of the Physical State of Microencapsulated PCM on the Pressure Drop of Slurry in a Circular Channel.

Phase Change Material (PCM) is mainly used in thermal energy storage. The addition of small PCM particles to the working fluid circulating in the heat exchange systems allowed to increase the amount of transported energy thanks to the use of latent heat-the heat of phase change. Encapsulating PCM in microcapsules avoids the disadvantages of PCM emulsions and makes the resulting slurry an attractive heat energy carrier. The paper presents the effect of the aggregate state of PCM enclosed in microcapsules on the flow resistance of the slurry through a rectilinear tubular channel. The tests were carried out with the use of a tube with an internal diameter of 4 mm and a measuring section length of 400 mm. A slurry of 21.5 wt.% PCM microcapsules (MPCM) was used as the working fluid in distilled water. A slurry with temperatures of 18.4 °C (PCM encapsulated in a solid state), 26.1 °C (PCM is in a phase change), and 30.5 °C (PCM in a liquid state) flowed through the measuring section. The mass flow rate of the MPCM slurry reached 70 kg/h (Remax = 2150). It was shown that the higher the Re number, the higher the value of the flow resistance, and the more clearly this value depended on the temperature of the slurry. Detailed analyses indicate that the observed changes were not the result of a change in the viscosity of the slurry, but its density depending on the state of the PCM. Significant changes in the density of the slurry in the range of the phase transition temperature are the result of significant changes in the volume of the microcapsule containing the phase change material in different aggregate states.

Ecotoxicity Study of New Composite Materials Based on Epoxy Matrix DER-331 Filled with Biocides Used for Industrial Applications.

The impact of fillers in the epoxy oligomer on the test culture of the marine ecosystem was investigated. The content of additive-biocides-was selected based on the complex research using STAT-GRAPHICS® Centurion XVI. The ecotoxicity of composite surfaces was determined in model systems using methods which are standard in eco-microbiology. The microorganism was identified by studying morphological, cultural, biochemical, and antigenic properties. Studies of the structure and the microrelief of the surfaces of composite materials were conducted using scanning electron microscopy (SEM) and X-ray energy dispersive spectroscopy. Based on comprehensive research, it was established that the composition of oxytetracycline with content of q = 1.5 wt% and nanosilver with a content of q = 0.075 wt% per 100 wt% of the DER-331 oligomer and 10 parts by the mass of the TETA hardener ensures the formation of a porous nano-heterogeneous structure of the coating, which leads to the acceleration of the release of silver ions from the surface of the polymer. The rational content of the complex additives of biocides ensures adhesion to the cell wall of bacteria with subsequent penetration into it and subsequent change to the cell membrane, its death, and, therefore, the suppression of the fouling process of metal structures.

Research on the Oscillation in Centerless Grinding Technology When Machining Bearing Steel.

In today's engineering industry, technical diagnostics presents many advantages for improving the management of machining centers and automated production lines. As the fourth industrial revolution is currently being implemented, which includes machine diagnostics, the idea of adding information from the field of vibrodiagnostics was born. The vibration of the workpiece or machine tool negatively affects the geometric parameters of the machined surfaces of the workpiece. Through vibrodiagnostics, the influence of cutting parameters on the oscillation of a bearing steel workpiece during centerless grinding is investigated. The presented publication deals with the vibration of the mechanical parts of a centerless grinding machine. The oscillations are recorded by acceleration sensors, which are also placed on the support ruler in which the workpieces are guided, and the recorded data are input parameters for statistical processing of acceleration values in the form of statistical characteristics (minimum, lower quartile, median, upper quartile, maximum). In this paper, this procedure was applied for the selection of the optimum cutting parameters (for the speed of the support wheel), where the machining parameters at which the minimum oscillation values occur were selected based on the above-mentioned statistical characteristics. This optimization procedure revealed increased vibration values which reached the highest amplitude on the ruler, namely accelerations of 11 m/s2, the origin of which was subsequently detected by STFT because the occurrence of resonance events or the excitation of natural frequencies of the machine were suspected. The STFT analysis identified a resonant region at machine start-up determined by the spindle speed which excites the resonance on the machine. The speed range between 1950 and 2150 rpm, which corresponds to the built-up resonance, was provided to the technologists to ensure that the machine was not operated around this resonance region at 400 and 760 Hz until the undesired phenomenon was eliminated. The results of the individual measurements provided information on the ideal setting of the cutting parameters and the current state of the machine.

Experimental Study and Verification of New Monolithic Rotary Cutting Tool for an Active Driven Rotation Machining.

Forced rotation turning appears to be an effective machining method due to higher tool life, time efficiency and acceptable quality. Several studies have been carried out to investigate the basic characteristics of forced rotation machining. So far, tools are used whose design included several components. However, such tools may generate vibrations, which are undesirable in the process. In engineering practice, most vibration problems are solved by reducing the cutting parameters (cutting speed and feed rate), which reduces machining productivity. For this reason, a new type of monolithic rotary tool has been designed that eliminates the design complexity and high assembly accuracy requirements of current rotary tools. Based on the performed experimental research, it is possible to define the influence of cutting parameters on the cutting force. Next, the equation of the cutting force and the resulting roughness of the machined surface was determined. In the introduction, the results of the analysis of machining parameters with a rotary tool were added. The presented solution fundamentally validates the new monolithic tool for forced rotation technology and defines its application for different machining materials.

Structural and Mechanical Changes of AlMgSi0.5 Alloy during Extrusion by ECAP Method.

SPD (several plastic deformations) methods make it possible to obtain an ultrafine-grained structure (UFG) in larger volumes of material and thus improve its mechanical properties. The presented work focuses on the structural and mechanical changes of aluminium alloy AlMgSi0.5 (EN AW 6060) during processing by repeated extrusion through the ECAP rectangular channel. After a four-pass extrusion, the samples' microstructures were observed using an optical microscope, where refinement of the material grains was confirmed. Tensile tests determined the extrusion forces and allowed interpretation of the changes in the mechanical properties of the stressed alloy. The grain size was refined from 28.90 µm to 4.63 µm. A significant improvement in the strength of the material (by 45%) and a significant deterioration in ductility (to 60%) immediately after the first extrusion was confirmed. The third pass through the die appeared to be optimal for the chosen deformation path, while after the fourth pass, micro-cracks appeared, significantly reducing the strength of the material. Based on the measurement results, new analytical equations were formulated to predict the magnitude or intensity of the volumetric and shape deformations of the structural grain size and, in particular, the adequate increase in the strength and yield point of the material.

A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends.

In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry.

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process.

The article aims to use the generated sound as operational information needed for adaptive control of the metalworking process and early monitoring and diagnosis of the condition of the machined materials using a newly introduced surface roughness quality index due to the sound-controlled machining process. The object of the measurement was correlation between the sound intensity generated during cutting and the material parameters of the machined surface, i.e., the roughness of the machined surface and the degree of wear of the cutting tool. The roughness was measured during longitudinal turning of a steel billet with a P25 insert made of 12X18H10T steel and a T15K6 cutting insert made of a titanium, cobalt, and tungsten group alloy. The correlation between the sound and roughness of the machined surface was 0.93, whereas between the sound and wear of the cutting tool was 0.93. The correlation between sound and tool wear in the experiment with P25 and T15K6 cutting inserts and the correlation between sound and roughness is positive.

Selective Laser Sintering of PA 2200 for Hip Implant Applications: Finite Element Analysis, Process Optimization, Morphological and Mechanical Characterization.

Polyamide 12 (PA 22000) is a well-known material and one of the most biocompatible materials tested and used to manufacture customized medical implants by selective laser sintering technology. To optimize the implants, several research activities were considered, starting with the design and manufacture of test samples made of PA 2200 by selective laser sintering (SLS) technology, with different processing parameters and part orientations. The obtained samples were subjected to compression tests and later to SEM analyses of the fractured zones, in which we determined the microstructural properties of the analyzed samples. Finally, an evaluation of the surface roughness of the material and the possibility of improving the surface roughness of the realized parts using finite element analysis to determine the optimum contact pressure between the component made of PA 2200 by SLS and the component made of TiAl6V4 by SLM was performed.

Prediction Model of the Resulting Dimensions of Welded Stamped Parts.

The combination of stamping and subsequent welding of components is an important area of the automotive industry. Stamping inaccuracies affect the final size of the stamping and the welded part. In this article, we deal with a specific component that is produced by such a procedure and is also a common part of the geometry of a car. We focused on the possibility of using a negative phenomenon-deformation during welding-on the partial elimination of inaccuracies arising during stamping. Based on the planned experiment, we created a prediction model for the selected part and its production, with the help of which it is possible to determine suitable welding parameters for a specific dimension of the stamping and the required monitored dimension of the welded part. The article also includes the results of additional experimental measurements verifying the accuracy of the model and prediction maps for practice.

A New Method of Predicting the Structural and Mechanical Change of Materials during Extrusion by the Method of Multiple Plastic Deformations.

The formulation of the Hall-Petch relationship in the early 1950s has raised immense interest in studying the influence of the grain size of solid materials on their properties. Grain refinement can be achieved through extreme deformation. In the presented study, Equal-Channel Angular Pressing (ECAP) was successfully applied to produce an ultrafine-grained microstructure in a pure commercial Cu of 99.9 wt%. Samples were processed by ECAP at 21 °C for six passes via route A. A new equation of equilibrium that allows the exact determination of the number of extrusions and other technological parameters required to achieve the desired final grain size has been developed. The presented research also deals, in a relatively detailed and comparative way, with the use of ultrasound. In this context, a very close correlation between the process functions of extrusion and the speed of longitudinal ultrasonic waves was confirmed.

Correction: Razvan et al. Selective Laser Sintering of PA 2200 for Hip Implant Applications: Finite Element Analysis, Process Optimization, Morphological and Mechanical Characterization. Materials2021, 14, 4240.

The authors wish to make the following correction to their paper [...].

Thermodynamics and Mechanics of Thermal Spraying of Steel EN 10060 Substrate with NiCrBSi Alloy after Milling.

The objective of this paper is to present a new way of identifying and predicting the relationship between thermodynamic and physical-mechanical parameters in the formation of a layer after spraying on a substrate with NiCrBSi alloy and its subsequent processing by milling. The milling of the spherical surface of the EN 10060 material after spraying was performed on the DMU 40 eVolinear linear milling centre. The experimental part of the article is focused on investigating the influence of cutting parameters when machining a selected combination of materials (substrate-coating: EN 10060 steel-NiCrBSi alloy). The experiment is based on the results of direct measurements of three basic cutting parameters, namely: cutting speed vc (m∙min-1), feed per tooth fz (mm), and the depth of cut ap (mm). The new distribution functions of selected cutting parameters were derived. The analytical results of the thermodynamic calculations performed on nickel-based alloy can be used for accurate predictions of the technological parameters of milling a spherical substrate made of EN 10060 steel after HVOF spraying, and also for both sample preparation and the subsequent production of high-quality coatings.

Modelling the Stiffness-Temperature Dependence of Resin-Rubber Blends Cured by High-Energy Electron Beam Radiation Using Global Search Genetic Algorithm.

Modelling the influence of high-energy ionising radiation on the properties of materials with polymeric matrix using advanced artificial intelligence tools plays an important role in the research and development of new materials for various industrial applications. It also applies to effective modification of existing materials based on polymer matrices to achieve the desired properties. In the presented work, the effects of high-energy electron beam radiation with various doses on the dynamic mechanical properties of melamine resin, phenol-formaldehyde resin, and nitrile rubber blend have been studied over a wide temperature range. A new stiffness-temperature model based on Weibull statistics of the secondary bonds breaking during the relaxation transitions has been developed to quantitatively describe changes in the storage modulus with temperature and applied radiation dose until the onset of the temperature of the additional, thermally-induced polymerisation reactions. A global search real-coded genetic algorithm has been successfully applied to optimise the parameters of the developed model by minimising the sum-squared error. An excellent agreement between the modelled and experimental data has been found.

A Study of Using Natural Sorbent to Reduce Iron Cations from Aqueous Solutions.

Iron is an essential trace element, but at high doses, this element may pose a health risk. Wastewater from iron ore mining, steel production, and metal processing, among other heavy metals, also contains high concentrations of iron (Fe3+). The use of sorption on natural materials is a potential alternative to conventional methods for removing iron ions, also because of low cost. The methods presented in this article are based on the study of kinetic properties and the acquisition of adsorption isotherms, which are one of the most important characteristics of adsorption mechanisms. The course of sorption is analyzed according to the Freundlich sorption isotherm model. Isotherm parameters are evaluated using experimental results of ferric cation sorption. The results presented relate to the investigation of natural zeolite-clinoptilolite as a ferric cation sorbent, providing a measurement of the sorption kinetics as well as the observed sorption parameters of iron cations from aqueous media. The optimal time for equilibrium in the adsorption system is determined from the kinetic dependencies. The dependence of the achieved equilibrium concentration on the initial concentration of the solution was also expressed, both graphically and analytically. The new prediction model was compared with the traditional Freundlich model. Finally, adsorption isotherms tested under laboratory conditions for a practical application can be recommended for the preliminary examination of the possible technological use of natural zeolite in the wastewater treatment process.

Application of Cardio-Forecasting for Evaluation of Human-Operator Performance.

The paper presents the results of the development of the cardio-forecasting technology, which introduces a new method to monitor the state of human-operator, which is characteristic for the given production conditions and for individual operators, to predict the moment of exhaustion of his/her working capacity. The work aims to demonstrate the unique, distinctive features of the cardio-forecasting technology for predicting an individual limit of his/her working capacity for each person. A unique methodology for predicting individually for each person the moment when he/she reaches the limit of his/her working capacity is based on a spectral analysis of a human phonocardiogram in order to isolate the frequency component located at the heart contraction frequency. The trend of the amplitude of this component is approximated by its model; consequently, the coefficients of the trend model are determined. They include the operator's operating time until his/her working capacity is exhausted. A methodology for predicting the moment when he/she reaches the limit of his/her working capacity for each person individually and assessment based on this degree of criticality of their condition will be realized as a software application for smartphones using the Android operating system.

Investigation of Surface Roughness and Predictive Modelling of Machining Stellite 6.

The aim of the paper was to examine the influence of cutting conditions on the roughness of surfaces machined by longitudinal turning, namely of surfaces coated with Stellite 6 prepared by high-velocity oxygen fuel (HVOF) technology and applied onto a standard structural steel substrate. From the results of measurements of the cutting parameters, a prediction model of the roughness parameters was created using mathematical and statistical methods. Based on a more detailed analysis and data comparison, a new method for prediction of parameters of longitudinal turning technology was obtained. The main aim of the paper was to identify the mutual discrete relationships between the substrate roughness and the machining parameters. These were the feed rate vc (m·min-1), in the case of turning and milling, and the feed rate f (mm·rev-1) and the depth of cut ap (mm). The paper compared and verified two approaches of this method, namely the mathematical statistical approach, the analytical approach and measured dates. From the evaluated and interpreted results, new equations were formulated, enabling prediction of the material parameters of the workpiece, the technological parameters and the parameters of surface quality.

Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems.

The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

Evidence of Tetrahedrally Coordinated Nickel Cations in Nanostructured NiFe2O4.

Nanostructured nickel ferrite (NiFe2O4) is prepared via high-energy ball milling of the bulk counterpart at ambient temperature. The structure of the as-prepared nanoferrite is characterized by Raman spectroscopy and 57Fe Mössbauer spectroscopy. Due to the ability of these spectroscopic techniques to probe the local environment of ions, valuable complementary insight into the nature of the local structural disorder of nanosized NiFe2O4 is provided. For the first time, evidence is given of the tetrahedrally coordinated nickel cations in the nanomaterial.

The Effect of High-Energy Ionizing Radiation on the Mechanical Properties of a Melamine Resin, Phenol-Formaldehyde Resin, and Nitrile Rubber Blend.

Irradiation by ionizing radiation is a specific type of controllable modification of the physical and chemical properties of a wide range of polymers, which is, in comparison to traditional chemical methods, rapid, non-polluting, simple, and relatively cheap. In the presented paper, the influence of high-energy ionizing radiation on the basic mechanical properties of the melamine resin, phenol-formaldehyde resin, and nitrile rubber blend has been studied for the first time. The mechanical properties of irradiated samples were compared to those of non-irradiated materials. It was found that radiation doses up to 150 kGy improved the mechanical properties of the tested materials in terms of a significant increase in stress at break, tensile strength, and tensile modulus at 40% strain, while decreasing the value of strain at break. At radiation doses above 150 kGy, the irradiated polymer blend is already degrading, and its tensile characteristics significantly deteriorate. An radiation dose of 150 kGy thus appears to be optimal from the viewpoint of achieving significant improvement, and the radiation treatment of the given polymeric blend by a beam of accelerated electrons is a very promising alternative to the traditional chemical mode of treatment which impacts the environment.