Modeling Stage Research Articles

On 3D printed polyvinylidene fluoride-based smart energy storage devices

Polyvinylidene fluoride (PVDF) is one of the established thermoplastics with inherent piezoelectric characteristics. In the past two decades, a lot of work has been reported on the use of virgin Polyvinylidene fluoride thermoplastics for sensing applications. But hitherto little has been reported on 3D printing of secondary (2°) recycled Polyvinylidene fluoride as a smart energy storage device (ESD). This work is focused on exploring the possibilities for 3D printing of smart energy storage device comprising of Polyvinylidene fluoride having (melt flow index (MFI) 30 g/(10 min) as per ASTM D 1238) reinforced with MnO2, graphite, ZnCl2, and N [Formula: see text] Cl in different weight proportions to form a feedstock filament for fused deposition modeling (FDM) in the first stage. The MFI of Polyvinylidene fluoride composites reinforced with MnO2, graphite, ZnCl2, and N [Formula: see text] Cl (34.095, 8.480, 42.982, 11.807 g/(10 min) respectively) was ascertained for possible 3D printing on FDM. The results suggest that even with an acceptable MFI of prepared 2° recycled Polyvinylidene fluoride, the same was not printable. Further for possible 3D printing on FDM, low-density polyethylene (LDPE) was blended in a Polyvinylidene fluoride matrix, and successful 3D printing-based energy storage device was prepared in the second stage. The study also highlights the mechanical, and morphological properties of Polyvinylidene fluoride composites have been improved after processing with a twin-screw extruder (TSE) by using different input parameters (screw temperature (T), screw speed (S), and load (L)). Overall, the study suggests that the proportion of LDPE, MnO2, graphite, ZnCl2, and N [Formula: see text] Cl has a significant effect on the rheological, mechanical, and morphological properties. The results have been supported with scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transformed infrared spectroscopy (FTIR) analysis.

Using Attack Path Formalisation Analysis in Software Design Stage Threat Modelling Method

This research proposes a comprehensive method for threat modeling in the software design stage, incorporating attack path formalization analysis. The approach involves extracting software defect information through UML active graph decomposition of the application or system and performing threat modeling. The method comprises several steps, including creating and modeling use cases, generating an application/system silhouette, decomposing the application/system using the active graph, constructing a threat tree with key asset information as the threat object, classifying and evaluating the threat object, and calculating the attack path of the threat tree. By comparing it with existing approaches, this invention aims to enhance software product safety, improve software quality, broaden the application range of threat modeling, and achieve automation in threat modeling, resulting in reduced technical threshold, cost, and development time for trusted software development.

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters, a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed. At first, the objectives of satellite mission and optimization problems are clarified, and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit, payload, power and quality disciplines. In order to solve the problem of increasing non-linearity and coupling between these disciplines while using a standard collaborative optimization algorithm, an improved genetic algorithm is proposed and applied to system-level and discipline-level models. Finally, the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler (CSM) and MATLAB. The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible. It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

Surgical workflow recognition with temporal convolution and transformer for action segmentation.

Automatic surgical workflow recognition enabled by computer vision algorithms plays a key role in enhancing the learning experience of surgeons. It also supports building context-aware systems that allow better surgical planning and decision making which may in turn improve outcomes. Utilizing temporal information is crucial for recognizing context; hence, various recent approaches use recurrent neural networks or transformers to recognize actions. We design and implement a two-stage method for surgical workflow recognition. We utilize R(2+1)D for video clip modeling in the first stage. We propose Action Segmentation Temporal Convolutional Transformer (ASTCFormer) network for full video modeling in the second stage. ASTCFormer utilizes action segmentation transformers (ASFormers) and temporal convolutional networks (TCNs) to build a temporally aware surgical workflow recognition system. We compare the proposed ASTCFormer with recurrent neural networks, multi-stage TCN, and ASFormer approaches. The comparison is done on a dataset comprised of 207 robotic and laparoscopic cholecystectomy surgical videos annotated for 7 surgical phases. The proposed method outperforms the compared methods achieving a [Formula: see text] relative improvement in the average segmental F1-score over the state-of-the-art ASFormer method. Moreover, our proposed method achieves state-of-the-art results on the publicly available Cholec80 dataset. The improvement in the results when using the proposed method suggests that temporal context could be better captured when adding information from TCN to the ASFormer paradigm. This addition leads to better surgical workflow recognition.

Categorical predictive and disease progression modeling in the early stage of Alzheimer’s disease

BackgroundA preclinical stage of Alzheimer’s disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset. New methodWe propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI. ResultsA very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms. Comparison with existing methodsUnlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models. ConclusionsThe estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/Aβ. Furthermore, pTAU/Aβ, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.

A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective.

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the “densities” were actually an abstract reflection of the “contact” frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect “contact” frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional “densities”. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.

Simplified modeling and analysis method for skyscrapers with outrigger system

In this paper, a simplified modeling and analysis method for skyscrapers with an outrigger system is introduced. The simplified method is mainly based on the superelements and dominant degree of freedom methods, and it is codified by using MATLAB software. The simplified method results are verified with analysis results of the ETABS detailed model, where static, modal, and dynamic analyses are conducted. The proposed method's applicability on different configurations of tall buildings with outriggers systems is also verified through a parametric study. A comparative study with some previous studies is conducted to prove the efficiency of the proposed method. It was found that the adopted simplified modeling method has good accuracy in predicting the lateral displacements and inter-story drifts in the linear and nonlinear analysis. Furthermore, the simplified model has good accuracy in predicting the first modal shape and its corresponding period, while it has less accuracy in predicting the higher modal shapes and periods. Also, it was found that the simplified model underestimates the Max and Min story acceleration in comparison with the accurate model. The parametric study confirmed the proposed method's applicability on different configurations and topologies of tall buildings with an outrigger system. As a result, the simplified modeling method gives a deep understanding of the outrigger system behavior and its parameters. Therefore, it is an effective tool for modeling and analysis in the preliminary design stage. However, further improvements on the simplified modeling method should be conducted in future studies to increase the accuracy and quality of the obtained results and avoid its disadvantages.

Sensitivity analysis of household factors and energy consumption in residential houses: A multi-dimensional hybrid approach using energy monitoring and modeling

The energy forecast modeling has long been referred to as the function of estimating the energy consumption of a building and providing sustainable design options for energy-efficient policies. In addition to the existing conventional schemes for energy performance and energy simulation, there is the potential to connect steady-state energy modeling to energy monitoring data, along with household factors such as family size, housing design, and occupancy ratio to better understand hard-to-measure energy-related behaviors. This paper introduces a multi-dimensional hybrid approach that combines multiple interactions between observation-based and simulation-based data using energy modeling’s graphical interface software. Compared to the observed energy consumption, the model samples show an average Pearson coefficient of determination in the range of 72% to 76% probability of the energy model data. Accordingly, research has carried out specific air conditioning set points and schedules within each household. Finally, the variation of simulated household models illustrates the greatest effect of air conditioning setpoint on savings of 20% to 60%, compared to the baseline usage. Sensitivity analysis shows that larger household size, higher occupancy ratio, lower thermal resistance for wall insulation, and slighter airflow rate can reduce the end-use and the gross site or source energy. The study underlines the diverse interactions among household characteristics, occupant schedules, and energy monitoring data, which are prospective to stimulate the application of building energy modeling in the early design stage and the optimization of energy efficiency in the operational phases.

EEG-Based classification of branded and unbranded stimuli associating with smartphone products: comparison of several machine learning algorithms

Neurocomputing studies on consumer attitudes have recently made a significant contribution to branding psychology. This study investigates consumer decisions for a branded stimulus using advanced machine learning algorithms. Electroencephalogram (EEG) signals were recorded during experiments in which ten branded and ten unbranded smartphones were shown to the subjects as stimuli. Then, EEG signals were classified using a methodological approximation consisting of four stages: pre-processing, feature extraction, channel selection, and classification. This is the first time a four-stage methodology has been applied in a neuromarketing study. The analysis study employed autoregressive (AR), principal component analysis (PCA), and piecewise constant modeling (PCM) in the feature extraction stage; artificial bee colony (ABC) and independent component analysis (ICA) in the channel selection stage; and artificial neural network (ANN) and support vector machine (SVM) in the classification stage. By comparing the successes of the methods in each stage, the methodological combination that gives the best classification performance was determined. According to the results, 72% accuracy was obtained in the average classification and an 85% accuracy on an individual basis. In the classification that achieved the best success, PCA in feature extraction, ABC in channel selection, and ANN in classification were used. In addition, the AF3-F7 (frontal brain regions) channels were observed to be the best-decomposed channel pair for different classes. The results also showed that stimulation within the brain for a branded product occurred at about 200 ms after the stimulus onset.

Big data analytics: Implementation challenges in Indian manufacturing supply chains

Big Data Analytics (BDA) has attracted significant attention from both academicians and practitioners alike as it provides several ways to improve strategic, tactical and operational capabilities to eventually create a positive impact on the economic performance of organizations. In the present study, twelve significant barriers against BDA implementation are identified and assessed in the context of Indian manufacturing Supply Chains (SC). These barriers are modeled using an integrated two-stage approach, consisting of Interpretive Structural Modeling (ISM) in the first stage and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in the second stage. The approach developed provides the interrelationships between the identified constructs and their intensities. Moreover, Fuzzy MICMAC technique is applied to analyze the high impact (i.e., high driving power) barriers. Results show that four constructs, namely lack of top management support, lack of financial support, lack of skills, and lack of techniques or procedures, are the most significant barriers. This study aids policy-makers in conceptualizing the mutual interaction of the barriers for developing policies and strategies to improve the penetration of BDA in manufacturing SC.

A Work Breakdown Structure Based Material Requirement Planning Information System Using Building Information Modeling for Flat Construction Project

Indonesian government program to provide millions homes for community has carried out hundreds flat construction project. At the same time, deviation between the budget and actual cost due to unwell planned construction material make it a disrupting focus among contractor issue. Construction material is an important element in construction projects since it cost up to 50%–60% of the total project cost. This study intend to overcome these issue by implementing well planned construction material requirement information system based on Work Breakdown Structure using Building Information Modeling. The benefit of Building Information Modeling there is a lot of information which could be stored at Building Information Modeling in early planning stage so that construction conflict could be minimize. This research is using qualitative approach to achieve information system of material needed based on Work Breakdown Structure using Building Information Modeling. First step is validating Work Breakdown Structure to define construction materials needed in flat construction project using expert assessment. Second step is developing construction material information system. Validated construction materials are used to develop information system using Building Information Modeling. A case study in flat construction project is conducted to ensure information system is working well. The result of the development of construction material information system is assessed by experts. The Delphi method is used to validate the expert assessment. Contribution from this research include utilization of Building Information Modeling to develop construction material information system is proposed and to enhance the effectiveness of the construction material requirement planning in flat construction project.

Can artificial neuron networks be used for control of HVAC in environmental quality management systems?

The concept of environmental quality management has been described in papers [1 - 4] that looked at the next generation of low energy buildings from the point of view of the occupant. Optimizing energy use is difficult for a few reasons: presence of dramatic changes in the manner we design and operate buildings, change in the role of an architect who must be a leader of interacting team, often quality management is biased towards the design more than on performance of the finished product and finally the need for integrated monitoring and modeling in the occupancy stage. Effectively, we are integrating heating/cooling and ventilation with the structure at the same time as we verify the appropriateness of the new methods to evaluate performance of these systems. In this process we require double controls, one by the occupant and the other by the computerized (smart) control system. The traditional approaches to modify human behavior generally failed because occupants were not given enough control over their environment. Thus, a major part of the trend to a low-carbon, climate resilient future will be focused on methodology to include path from a complex field testing of building performance to simplified testing that combined with simple monitoring and data from utilities would allow assessment of the energy and carbon emission in a district of a city. Our experience shows that preliminary design must be optimized during the period of service for all more complex buildings such as large residential, office or commercial buildings. In this context the artificial neural network approach appears to have significant advantages. Yet, traditionally ANN requires large data set to establish functional relations during the learning stage and therefore the first question is how precise can the control of temperature be when the heat exchanger is subjected to different climatic conditions.

Evaluation of Operational Options of Wastewater Treatment Using EQPS Models

EQPS (Effluent Quality Prediction System, Dynamita, France) was applied to analyze the appropriateness of the design of a bioreactor in A sewage treatment plant. A sewage treatment plant was designed by setting the design concentration of the secondary clarifier effluent to total nitrogen and total phosphorus, 10 mg/L and 1.8 mg/L, respectively, in order to comply with the target water quality at the level of the hydrophilic water. The retention time of the 4-stage BNR reactor was 9.6 hours, which was 0.5 for the pre-anoxic tank, 1.0 for the anaerobic tank, 2.9 for the anoxic tank, and 5.2 hours for the aerobic tank. As a result of the modeling of the winter season, the retention time of the anaerobic tank was increased by 0.2 hours in order to satisfy the target water quality of the hydrophilic water level. The default coefficients of the one step nitrification denitrification model proposed by the software manufacturer were used to exclude distortion of the modeling results. Since the process modeling generally presents optimal conditions, the retention time of the 4-stage BNR should be increased to 9.8 hours considering the bioreactor margin. The accurate use of process modeling in the design stage of the sewage treatment plant is a way to ensure the stability of the treatment performance and efficiency after construction of the sewage treatment plant.

Research on control-oriented coupling modeling for air-breathing hypersonic propulsion systems

A control-oriented coupling hypersonic propulsion system model is proposed to enable the rapid development of a propulsion model for air-breathing hypersonic flight vehicles (ABHV) in early stages of the design process and facilitate design control and analysis. An airframe/propulsion coupling hypersonic inlet model was established based on oblique shock wave theory. An isolator model in which the effects of the back pressure of the combustion chamber were adjusted by the static pressure ratio of the isolator was established. A hypersonic combustion model was also established, taking into account the fuel flow, cross-sectional area, wall friction, combustion efficiency, and exothermic reactions based on quasi-1D flow theories. Nozzle/afterbody modeling was established based on identification of the free boundary (i.e., the location of the shear layer) by Newton collision theory, and flow parameters were determined according to the influence coefficient method. The mass flow rates of air in the design state and two typical non-design states were determined geometrically based on the application of oblique shock wave theory. A propulsion coupling model that reflects the coupling of propulsion system and aerodynamics, as well as the physical mechanisms of the propulsion mechanism, was then established based on air flow rates obtained and the momentum theorem. Simulation results of airframe/propulsion integrated module air-breathing hypersonic flight vehicles (ABHVs) by the proposed model were compared to results achieved by numerical 3D Computational Fluid Dynamics (CFD) models. Results indicated that the efficacy and accuracy of the proposed models met the established requirements of control-oriented modeling, thus facilitating dynamic modeling and control in the early stage of the design process.

Consistent modeling of a catastrophic flowslide at the Shenzhen landfill using a hydro-elasto-plastic model with solid–fluid transition

Flow-type landslides are an important hazard that can cause great destruction due to the rapid flow velocity and large disaster area. This paper presents a catastrophic flowslide that recently occurred at a landfill in Shenzhen, China. This disaster involved an area about 1100 m in length and 630 m in maximum width, and caused the death of 77 people and the destruction of 33 buildings. The precise reason for the landfill’s failure is still unknown, and therefore we try to contribute an increased understanding of the event for future prevention. In this study, the failure mechanism of the studied slope was analyzed and described under partially saturated condition. The solid–fluid transition during the flowslide occurrence was described using a unified constitutive model. The model was used to perform the hydro-elasto-plastic modeling in the pre-failure stage, the viscous modeling in the post-failure stage, and the second-order work criterion was introduced in between to model the solid–fluid transition. The consistent evolution of the flowslide, including initiation, propagation, and deposit stages, was simulated and analyzed using the finite element method with Lagrangian integration points after careful calibration of the viscous parameters. The numerical results were compared with the real case and used to explain the failure mechanism.

Basin modeling in the initial stage of exploration: a case study from the North Subbasin of the South Yellow Sea Basin

Basin modeling has become an important tool for analyzing sedimentary basins. The North Subbasin of the South Yellow Sea Basin is filled with thick Meso-Cenozoic terrigenous deposits during the rift evolution stage. The accumulation of data and achievements of geological investigations in recent years have provided the preconditions for basin modeling. The necessary parameters and geological elements for simulations are collated and summarized. Modeling of tectono-thermal evolution is performed and the related trend in heat flow is reconstructed and calibrated. The heat flow value commences from an average level of 61 mW/m2 during Middle-Late Jurassic, rises to about 80 mW/m2 from circa 145 Ma to circa 74 Ma, and then undergoes a gradual decline to 65 mW/m2 until the end of Oligocene.Three evolutionary phases, namely, the initial rifting phase, syn-rifting phase, and post-rifting phase, have been identified. The modeling results show that the North Subbasin generally enters into a stage of strong rifting during Cretaceous and undergoes rapid subsidence until the Late Cretaceous, then follows by a stage of moderate rifting during the Paleogene. The input and general workflow involved in 3-D modeling are introduced. Reconstruction of the petroleum system in the North Subbasin reveals that the threshold depth of hydrocarbon generation is located near the top of the Paleogene Funing formation, and the underlying Jurassic and Cretaceous source rocks have reached or exceeded peak oil generation and have almost completed the generation and expulsion of hydrocarbons. The main generation and expulsion in the Jurassic source rocks take place during the syn-rifting and post-rifting phases, whereas the peak generation and expulsion in the Cretaceous and Paleogene source rocks take place during the post-rifting phase. Although the study area is still a relatively less explored sedimentary basin, the results of modeling can provide valuable information for exploration. A preliminary discussion of the main uncertainty factors is also presented.

Modeling Distributed Real-Time Systems in TIOA and UPPAAL

The mission- and life-critical properties of distributed real-time systems require concurrent modeling, analysis, and formal verification in the design stage. The timed input/output automata (TIOA) framework and the UPPAAL software package are two widely used modeling and verification tools for this purpose. To this end, we develop the algorithm TUConvert for converting distributed TIOA models to UPPAAL behavioral models and formally prove its correctness. We demonstrate the applicability of our algorithm by the formal verification of a distributed real-time industrial communication protocol that is modeled by TIOA.

Simplified Chip Power Modeling Methodology Without Netlist Information in Early Stage of SoC Design Process

This paper presents a novel methodology for on-chip power-noise modeling in the early stage of system-on-chip (SoC) design. Conventionally, the on-chip power-noise simulation is performed in “placement and routing” design stage. Therefore, designers experience difficulty in applying the simulation results to improve power-noise performance because of the delivery time. The proposed methodology enables modeling of the dynamic current profile, without any geometry information and estimation of SoC power noise in the register-transfer-level design phase. Each SoC sub-block is defined as a unit simplified chip power model (SCPM), and the defined unit SCPMs are integrated into one SCPM, including multiblock characteristics. SCPM presents various types of current profiles to accurately predict the maximum current peak, and it includes the background current to prevent overestimation of the ac current. To improve the simulation accuracy, this paper proposes a voltage ripple measurement method that considers the SoC operating scenario. The simulation results of the SCPM are verified by the measurement results, and the SCPM methodology shows the correlation results of 7 and 18 mV on two test vehicles with a 1.1 V core voltage. In the chip-package design industry for electronic applications, the proposed methodology presents a design guide for the power delivery network, such as essential capacitance per location (e.g., chip, package, and printed circuit board) and the limit of the off-chip routing inductance. In addition, the forecast by the SCPM simulation shows that preactive design is available at the early stages of the design process.

DEVELOPMENT OF THE PRINCIPLES OF ENERGY EFFICIENT PROCESSES OF METALLIZATION USING THERMODYNAMIC MODELING

The paper presents the results of iron solid-phase reduction study obtained by means of TERRA software package. Investigations were carried out in two stages: the experiment on the model elementary system and the study of iron reduction from iron ore by the coals of various grades. As a result of thermodynamic modeling in the first stage of oxidation-reduction conditions study in Fe – C – O system there were some boundaries of reducing, transition and oxidizing zones and the corresponding values of the ratio of oxygen to the amount of carbon in that system. Studying of coal reduction properties by calculating of equilibrium compositions in a temperature range of 373–1873 K with TERRA software package usage showed that the composition of a gas phase in the given temperature range is almost the same for all types of coals and differs according to the amount of released volatiles. The sharp increase of gas volume begins at 673 K and ends at 1073 K. The results of thermodynamic modeling in the second phase of the experiment enabled to determine the optimal CC coals consumption (low-caking), D (long-flame), 2B (lignite) required for a complete iron reduction from iron ore of the specified composition.

INFLUENCE OF THERMAL STRESSES ON THE FORMATION OF THE PLASTIC FLOW ZONE IN THE BEGINNING OF HOT FORGING OF CYLINDRICAL WORKPIECE

The article presents the solution of the problem of plastic flow modeling in the initial stage of hot drawing of a cylindrical workpiece in a forging press with combined strikers. The intensity stress fi eld for the tensor of total elastic stresses (load and thermal ones) has been received. The stress load has solved the problem of plane deformation for a workpiece of a circular cross section to which three lumped linear efforts were attached. The zone of a plastic fl ow was determined by the value of the yield strength of the material as a limitation of the elastic stress intensity. It has been shown that the tensile thermal stresses involve a near-surface layer of the workpiece into a zone of a plastic fl ow; that facilitates the process of forging. The numerical example of the formation of stress loads and thermal stresses during drawing after roughing-up of 7-ton ingot of 40KhN steel with the radius of average cross-section R = 0.35 m; its bottom (880 °C) boundary of the admissible forging temperatures was reached in 20 minutes after removal from the furnace with a temperature of 1180 °С has been considered. The graphs of the component distribution of the tensor of stress loads and thermal stresses have been introduced, as well as the respective intensities of stresses in the cross-section of the workpiece.