Process Parameters Research Articles

Integrated Multi-Dimensional Assessment of Red Mud Recycling Technologies: Insights from Industrial Waste Treatment and life-cycle Management

A large stock of red mud (RM), with certain resource values and potential environmental hazards, places higher requirements on the selection and optimization of recycling technology. A multi-dimensional performance assessment method combined with life cycle assessment was established to address this issue. In this study, eight typical RM recycling technologies were screened and sorted into three categories, 18 assessment indicators covering technological, resource, environmental, economic, and social dimensions were defined, and the integrated performance assessments of different categories and technologies were analyzed and compared. For the building material preparation category, the environmental dimension mainly focused on reducing the consumption of chemical reagents and sintering temperatures. In the material adsorption and modification category, improvements primarily targeted the technological and societal dimensions by increasing technological readiness and social feasibility. For the metal extraction category, the technology dimension focused on improving applicability and competitiveness and optimizing process parameters. Electricity, the primary energy source, significantly affected environmental performance and constrained industrial scalability. Thus, integrated performance assessment method for RM recycling technologies offers a methodology for researchers, provides a scientific basis and guidance for technology selection for industrialized applications, and promotes the optimal management of industrial solid waste for governments.

Pharmaceutical/Biomedical Applications of Electrospun Nanofibers - Comprehensive Review, Attentive to Process Parameters and Patent Landscape.

Nanotechnology is a new science and business endeavour with worldwide economic benefits. Growing knowledge of nanomaterial fabrication techniques has increased the focus on nanomaterial preparation for various purposes. Nanofibers are one-dimensional nanomaterials having distinct physicochemical properties and characteristics. Nanofibers are nanomaterial types with a cross-sectional dimension of tens to hundreds of nanometres. They may create high porosity mesh networks with significant interconnections among pores, making them suitable for advanced applications. Electrospinning stands out for its ease of use, flexibility, low cost, and variety among the approaches described in the literature. The most common method for making nanofibers is electrospinning. This article extensively describes and summarizes the impact of various process variables on the fabrication of nanofibers. Special attention has been given to scientific and patent prospection to confirm the research interests in nanofibers.

Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm

It is possible to produce improved material performance through the utilization of computational intelligence approaches such as genetic algorithms optimization technique which are discussed in this paper. The optimization of processes and the development of models that are driven by data are the key uses of these technologies. This article offers a comprehensive introduction of the topic of materials and discusses the ways in which computational intelligence techniques might be utilized to develop new materials. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of the Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ61 graphene nanoplate (GNP) composite.

Study on Oxygen-Enriched Combustion Characteristics of Blast Furnace Gas Boiler

ABSTRACT To study the dynamic characteristics of OEC (oxygen-enriched combustion) in a BFG (blast furnace gas) boiler, the BFG boiler of an iron and steel enterprise is taken as the research object, and a thermal test bed for OEC of BFG is set up, and a thermodynamic simulation model of the BFG boiler is constructed based on the heat loss of chemical incomplete combustion obtained from thermal test, using MSP (Multi-Subject Simulation Platform). The effects of OC (oxygen concentration) and the position of oxygen entering the furnace chamber on the key parameters of the OEC process of the BFG boiler under different working conditions were analyzed. The results showed that the excess air coefficient of 1.2 and exhaust temperature of 120°C was kept constant during the thermal test. With a fuel volume of 2.4 Nm3/h, for example, after the OC was increased from 21% to 40%, the combustion flame temperature in the furnace was increased by about 34°C, the heat loss due to unburned gas was reduced by 0.21%, and the boiler efficiency was increased by 1.38%. In the dynamic model, at the instant when the OC is changed, the disturbance of the whole system is relatively large, and the FAT (furnace average temperature) outlet, the pressure of the steam ladle, and the MST (main steam temperature) will have a step change, and then gradually level off again. With the increase of OC, the combustion reaction is more and more intense, the steam side of the heat transfer process is strengthened, the boiler efficiency is improved, and the FAT chamber outlet, the steam ladle pressure and the MST after OEC in different ways of oxygen entry into the furnace chamber will be longer and longer to reach a stable value. The results of the study can provide a reference for the OEC operation of BFG boilers.

UV-Based Degradation of Fluoroquinolone Antibiotic in Wastewater: Effects of Process Parameters, Identification of Degradation Products and Evaluation of Residual Toxicity

UV-Based Degradation of Fluoroquinolone Antibiotic in Wastewater: Effects of Process Parameters, Identification of Degradation Products and Evaluation of Residual Toxicity

An experimental investigation of abrasive jet machining parameters for optimized surface finish of mild steel components

Abrasive jet machining is one of the unconventional machining processes that are initiated for alternations of surface characteristics of ductile materials, like mild steel, due to the entrainment of high-velocity abrasive particles. The present experimental investigation deals with the major process parameters of the AJM process, namely air pressure, standoff distance, and time of machining, on the average surface roughness (Ra) of mild steel specimens. Experiments were conducted on the self-developed AJM setup using aluminum oxide abrasive particles of an average size of 50 µm. The pressure of air is changed in three levels, 6, 7, and 8 bars. The standoff distance is taken as 2 mm constant for all the cases. The surface roughness was measured at machining times of 20, 40, and 45 seconds. The presence of the increase of air pressure from 6 to 8 bars applied for all the machining time showed an increasing influence on roughness, although always significant in Ra. For 8 bars of pressure and 45 s of machining, the minimum resulted in 1.39 µm, compared with 3.47-4.02 µm. The rate of decrease in surface roughness is sharp with increasing machining time from 20 to 40 seconds but marginal beyond that. An optimal machining time of 40–45 seconds was identified to obtain a minimum Ra. Capability of AJM as an effective technique to enhance the surface finish of mild steel components, and practical insight of this study is very useful toward optimization of process parameters for attaining the targeted surface quality. The results obtained may assist in expanding the industrial application of AJM toward the finishing of ductile materials in various manufacturing fields.

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.

Maximizing Machining Efficiency and Quality in AA7075/Gr/ B4C HMMCs through Advanced DS-EDM Parameter Optimization Strategies

Abstract Hybrid Metal Matrix Composites (HMMC) offer improved mechanical properties crucial for various industrial applications. However, machining these composites via traditional methods is arduous due to their inherent hardness and abrasive nature. The optimization of the Die-Sinking Electrical Discharge Machining (DS-EDM) process presents a viable solution to overcome these machining challenges and unlock the full potential of HMMCs. Despite the potential benefits of HMMCs, the lack of optimized machining strategies hampers their widespread adoption in industrial applications. Traditional machining approaches often result in excessive tool wear, poor surface finish, and suboptimal material removal rates (MRR) when applied to HMMCs. Additionally, the complex interplay between DS-EDM process parameters and material properties necessitates a systematic investigation to identify optimal machining conditions. This study proposes a multi-step methodology to systematically optimize the DS-EDM process for machining HMMCs comprising AA7075 alloy, 5% of graphite (Gr), and 5% of boron carbide (B4C) particles, which is termed as (AA7075-GR-B4C). Beginning with the formulation of the composite material through stir casting, the research progresses to an L16 Orthogonal Array (L16-OA) based Design of Experiments (DoE) to investigate the effects of key process parameters on machining performance. Experimental testing is conducted to measure mechanical properties, followed by statistical analysis using Analysis of Variance (ANOVA) with Regression Analysis to identify significant factors influencing machining outcomes. Furthermore, an Artificial Neural Network with Black Widow Optimization (ANN-BWO) approach is employed to optimize machining performance metrics such as MRR, Surface Roughness Rate (SRR), and Tool Wear Rate (TWR). The relative error (RE) was estimated between experimental MRR (E-MRR), experimental TWR (E-TWR), experimental SRR (E-SRR), and predicted MRR (P-MRR), predicted TWR (P-TWR), predicted SRR (P-SRR) generated by ANN-BWO shows effectiveness of AA7075-GR-B4C.

A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

Abstract In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square (RMS) value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through Kernel Principal Component Analysis (KPCA), thereby enabling a more nuanced representation of the gearbox's degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process's parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox's RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic predictive maintenance for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of predictive maintenance in the context of gearboxes.

Modeling the influence of technological parameters of the magnetron sputtering process using the Caroline D12C system on the proportion of nanocrystallites in the structure of thin silicon films

Modeling the influence of technological parameters of the magnetron sputtering process using the Caroline D12C system on the proportion of nanocrystallites in the structure of thin silicon films

The effect of pulverized glass waste particle sizes on the mechanical properties of AA6061-T6 friction stir welded joints.

Abstract The study sought to investigate the effect of pulverized glass waste (PGW) particle size as reinforcement for AA6061-T6 joints produced by Friction Stir Welding. The study utilized three particle sizes of 15 microns, 45 microns and 75 microns of the PGW as reinforcement alongside the established process parameters ranges of 900-1400rpm, 25-63mm/min and 1-2.5° in a Taguchi L9 orthogonal array for the welding process experimental design. The welding experiments were repeated for each reinforcement particle size mentioned above. Parallel-hole reinforcement strategy was used on all joints for the application of the PGW, Tensile strength, Hardness tests and Microstructural Analysis were carried out on the welded joints and subsequently, analysis of the data. For tensile strength, several sample sets showed a trend of higher tensile strength the smaller the PGW particles size (Figure 6). For hardness, however, there was no overall, discernable trend/pattern of increase in hardness as the particle size decreased. The highest average tensile strength value obtained was 85.25MPa. The lowest tensile strength was 14.26MPa. A distinguishing feature between the highest and lowest value for tensile strength is that the former was obtained with a traverse speed of 25 mm/min while the latter was obtained using a 40 mm/min traverse speed. The higher the rotational speed of the tool pin, the greater the heat of friction generated while the lower the traverse/welding speed, the greater the heat of friction generated as the tool pin spends enough time per unit length to generate sufficient heat and cause plastic deformation sufficiently high enough to cause grain refinement. Using imageJ, percentage distribution of PGW for each crucial zone (Nugget/Stir zone, Thermo mechanically affected zone and Heat affected zone) of the FSWed joint was generated for samples selected for microstructural analysis. The average value for each sample was obtained as well. The highest tensile strength value (85.25MPa) was obtained with a sample using the smallest PGW size (15 µm), in line with the trend (figure 6) in which tensile strength was greater the smaller the PGW size used. The highest hardness value (90.90 BHN) was obtained with the sample having the highest average value for percentage distribution of the PGW (58.06%). A distinct trend was noted in which the greater the percentage distribution of the PGW, the higher the hardness value obtained (figure 30).

Comparative evaluation of formability in cup drawing and V-bending using additively manufactured tools

ABSTRACT Additive Manufacturing (AM) is rapidly transforming industrial processes by introducing advanced methods for designing, manufacturing, and prototyping key components across various industries. Its true potential is realized in integrated component repair via reverse engineering, significantly reducing rework, maintenance costs, and time. With growing demand, AM technology continues to evolve, incorporating intelligent printing systems and diverse applications in sectors like automotive, sheet metal forming, healthcare, aviation, and consumer goods. AM also aids in optimizing geometric and process parameters for tools such as dies, punches, and blank holders, enhancing economic batch production. This study focuses on the Finite Element Analysis (FEA) of two commercial operations: bending and deep drawing, using DC04 and AA6060 materials. The analysis identifies geometric precision, thinning, and formability. In steel, increasing the bottom radius from 3 mm to 20 mm results in an 11% difference in thinning, with a maximum of 0.67 mm. For aluminium, thinning varies minimally between 0.68 and 0.75 mm. Increased sheet thickness limits formability in both materials. FDM-printed tools were used for FEA simulation, followed by stress analysis of dies to assess design safety, small batch production, and dimensional conformity.

Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study’s major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.

Research on Multi-Parameter Optimization of Conical Roller Line Processing Technology Based on Satisfaction Function

In the process of conical roller line processing, there will be problems such as low precision of processing parameters, long processing time, low utilization rate of machine tools, high rejection rate, and high processing cost, which will lead to low production efficiency. In order to solve this problem, it is necessary to iteratively optimize the size parameters, including inner diameter, ovality, and taper. By obtaining the optimal parameter combination, the size parameters in the production process are kept consistent, that is, the accuracy and performance of the workpiece during the processing are guaranteed so as to avoid the problem of quality difference, improve the production efficiency and reduce the processing cost. In view of the fact that there are often some constraints on the accuracy and efficiency in the machining process of tapered roller lines, how to optimize the parameters affecting the accuracy and the parameters affecting the efficiency to achieve balance between accuracy and efficiency in order to better meet the needs of customers for multi-objective optimization of the machining process has become the focus of research. Based on the existing research, this paper explores the multi-parameter optimization modeling and application in the machining process of tapered roller lines by constructing a satisfaction function, and then uses a genetic algorithm to iteratively search for the optimal solution by simulating natural selection and the genetic mechanism. Based on Python software v3.12, the production process of tapered roller bearings is simulated. The AHP analytic hierarchy process and CRITIC weight method are used to redistribute the parameter weights respectively. After eight iterations, it is concluded that the weight value assigned by the AHP analytic hierarchy process makes the satisfaction function value reach the best value of 0.99795 and tend to converge stably. The optimized parameter configuration significantly improves the machining accuracy and production efficiency of the tapered roller line. The optimal parameter combination is obtained: inner diameter: 9.9982 mm, ovality: 0.7 mm, taper: 0.5 degrees, production efficiency: 101.0.97 piece/h. In order to verify the optimization effect, the single value (X) and moving extreme difference (Rs) control charts in the measurement value control chart are used to analyze and verify the tolerance values of important parameters in the processing technology of the tapered roller line. The results show that the data points are all within the control limit, indicating that the processing process is in a statistical control state.

Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing

Laser wire-feed metal additive manufacturing (LWAM) is an innovative technology that shows many advantages compared with traditional manufacturing approaches. Despite these advantages, its industrial adoption is limited by complex parameter management and inconsistent process quality. To address these issues and improve geometric accuracy, this study explores how process parameters influence bead geometry. We conducted a parameter study varying laser power, wire feed rate, traverse speed, and welding angle. Using a full factorial design with a central composite design methodology, we assessed bead height and width. This allowed us to develop a model to estimate ideal process parameters. The findings offer a detailed analysis of parameter interactions and their effects on bead geometry, aiming to enhance geometric accuracy and process stability in LWAM. Moreover, we have evaluated the proposed process parameters from our developed model, which showed a significant enhancement to the overall quality. This was validated via printing a single layer and multi-layer structures. The quality of the final predicted sample using the proposed method was improved by 40% compared to the best sample produced for the Design of Experiment trials.

Influence of process parameters on the organization and properties of 316L-SCBCC bracket formed by selective zone laser melting

Abstract 316L porous skeletal scaffolds prepared by selective laser melting (SLM) technology are currently widely used in bone injuries. Its successful implantation is predicated on having properties that match those of natural bone, and process parameters significantly influence the performance of SLM-316L porous scaffold. In this study, the nine-group shaping process parameters were determined by orthogonal method, the 316L porous scaffolds were tested in compression, electrochemistry, XRD and microstructure. The influence of process parameters on the performance of body-centered cubic peripheral square structure bracket was investigated, the influence laws of process parameters on microstructure, mechanical properties and corrosion resistance were obtained. The results show that process parameters have a significant effect on the microstructure, properties and defect distribution. The reduction of defects and grain refinement in the stent is conducive to the improvement of compressive properties and hardness of the stent, the magnitude of the hardness is inversely related to the grain size. The corrosion current density of porous scaffolds are also affected by their microscopic defects and grain size. At an energy density of 78.70 J/mm3 presents the least defects and obtains smaller grains, resulting in the best mechanical properties and corrosion resistance.

Comprehensive Measurement and Simulation of Prototype Injection Moulds

The injection moulding industry is dynamically developing. The growing demand for more customizable products can be served by low or middle volume production using prototype moulds and inserts. The conventional material of prototype moulds is aluminum because of its excellent machinability, acceptable strength and stiffness and outstanding thermal conductivity. Prototype moulds are gaining ground in the injection moulding industry, yet their operational behavior (including exact mechanical and thermal process parameters) is largely unknown. We created a comprehensive state monitoring system that measures the operational strain, cavity pressure and temperature of different prototype injection moulds. This way, all important process parameters can be measured and the relations between the moulding parameters and the operational pressure loads, deformations and temperatures can be quantified and analysed.

Implementation of Cellulose-Based Filtration Aids in Industrial Sunflower Oil Dewaxing (Winterization): Process Monitoring, Prediction, and Optimization

In the production of refined sunflower oil, waxes are removed during the winterization stage, and wax crystals are separated through filtration assisted by filtration aids. Commonly used filtration aids in oil refining include perlite and diatomaceous earth. After winterization, a significant amount of filter cake remains as a by-product and is treated as waste. Today, natural cellulose fibers are being promoted as filtration aids. Their advantages are numerous, both in the production process and from an environmental perspective. However, their only disadvantage is their higher cost. Therefore, in this study, 57 filtration cycles during the industrial sunflower oil winterization step using cellulose-based filtration aids were monitored. Different process parameters, including the pressure differential on the filter, the flow rate of filtered oil, constant pressure period, the quantity of filtered oil, filtration time, the quantity of pre-coating and dosing filtration aids, the volume of filtered oil, the concentration of dosing filtration aid, as well as the mass of separated waxes, were observed. Additionally, artificial neural networks were applied to predict process parameters, optimize the process, and, above all, determine the dosage of filtration aids, which will make the process more economical. The optimal filtration process is performed at a pressure differential of 3.3 bar, lasting a total of 39 h, with 32 h at constant pressure, resulting in 322,503 kg of filtered oil and 90.41 kg of waxes. The optimal quantity of cellulose-based filtration aids employed for pre-coat was 80 kg, and for dosing, 375 kg, with an optimal concentration of 0.12% w/w.

Studies of Phase Transformation Kinetics in the System of Nanocrystalline Iron/Ammonia/Hydrogen at the Temperature of 350 °C by Means of Magnetic Permeability In Situ Measurement

The kinetics of phase transformations in the nitriding process α-Fe → γ’-Fe4N → ε-Fe3-2N of the pre-reduced iron ammonia synthesis catalyst was investigated under in situ conditions (atmospheric pressure, 350 °C) by measuring changes of mass, gas phase composition, and magnetic permeability in a differential tubular reactor. The iron nanocrystallite size distribution according to their specific active surface areas was measured, and it was found that the catalyst is bimodal as the sum of two Gaussian distributions, also differing in the value of the relative magnetic permeability. Relative magnetic permeability of small α-Fe crystals in relation to large crystals is higher by 0.02. In the area of α → γ’ transformation, the magnetic permeability dependencies change, proving the existence of two mechanisms of the α-Fe structure change in the α-Fe → γ’-Fe4N transformation. In the first area, a solution of α-Fe (N) is formed with a continuous and insignificant change of the crystal lattice parameters of the iron lattice. In the second area, there is a step, oscillatory change in the parameters of the iron crystal lattice in FexN (x = 0.15, 0.20, 0.25 mol/mol). In the range of γ’-Fe4N → ε-Fe3-2N transformation, a solution is formed, with nitrogen concentration varying from 0.25–0.45 mol/mol. During the final stage of the nitriding process, at a constant value of the relative magnetic permeability, only the concentration of nitrogen in the solution εr increases. The rate of the phenomenon studied is limited by a diffusion rate through the top layer of atoms on the surface of iron nanocrystallite. The estimated value of the nitrogen diffusion coefficient varied exponentially with the degree of nitriding. In the area of the solution, the diffusion coefficient is approximately constant and amounts to 5 nm2/s. In the area of oscillatory changes, the average diffusion coefficient changes in the range of 3–11 nm2/s, and is inversely proportional to the nitrogen content degree. The advantage of the research method proposed in this paper is the possibility of simultaneously recording, under reaction conditions, changes in the values of several process parameters necessary to describe the process. The research results obtained in this way can be used to develop such fields of knowledge as heterogeneous catalysis, materials engineering, sensorics, etc.

Rheology, Morphology and Mechanical Properties for Mixtures of Multicomponent Waste from Polymer Composites as Secondary Raw Materials

The article presents a study of the rheological properties and morphology of a mixture of multicomponent waste polymer compositions of polyamide 6 (PA6GF30) and polycarbonate (PC) from automotive parts. A comparative analysis of the melt flow rate of the obtained mixture was carried out depending on the content of its components. The compatibility of the components in the mixture and their distribution in the obtained polymer composition were studied. The effect of the composition of the secondary multicomponent mixture on the physical and mechanical properties is demonstrated. The possibility of reusing multicomponent polymer waste with the predicted main parameters of the technological process of injection molding is shown.