Dimensional Machining Research Articles

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface.

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.

Исследование свойств сплавов на основе кремниевой бронзы, напечатанных с применением технологии электронно-лучевого аддитивного производства

Introduction. Additive technologies make it possible to curb material expenses by reducing allowances for the final dimensional machining of workpieces. For such expensive materials as copper and copper alloys, this method is considerably attractive from a perspective of increasing resource efficiency in production. The operational properties of the C65500 alloy manufactured using additive technologies have not been fully studied and require additional research. The aim of the work is to study the structural and phase state, mechanical and operational properties of C65500 bronze specimens printed using electron beam additive manufacturing technology. In the work, specimens made of C65500 wire with different heat input values are studied, some of which were subjected to thermal treatment and mechanical processing, as well as specimens, manufactured using multi-wire technology. The work uses such research methods as the study of corrosion resistance of bronze specimens using a potentiostat, confocal laser scanning microscopy, friction tests and X-ray phase analysis. Results and discussion. Processing of specimens by plastic deformation (compression) and subsequent annealing leads to the most serious structural changes. Based on X-ray phase analysis, it is found that higher silicon content is observed in the case of the addition of silumins to bronze. The study of mechanical properties shows that the specimens, printed using multi-wire technology, have the highest strength properties. During tribological testing, fluctuations in the value of the friction coefficient are revealed, due to the scheme of the experiment and the combined adhesive-oxidative mechanism of specimens’ wear. The addition of 10 wt.% aluminum filament to bronze in the additive manufacturing process is an effective means for increasing the resistance of the material to electrochemical corrosion and increasing its wear resistance.

Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning

Assessment of beef freshness is critical to ensure a safe and sustainable beef supply chain. This work aims to investigate the feasibility of the viscoelasticity of beef to evaluate its freshness using the airflow-3D machine vision technique and machine learning models. The 3D camera was continuously used to acquire deformation images under the action of the airflow. The obtained images were preprocessed using region of interest ( ROI ) segmentation, filtering denoising, and down-sampling. And then the depth and volume of processed images were obtained by the Oriented Bounding Box ( OBB ) algorithm and volume algorithms, separately. Two four-element viscoelastic models were established to fit depth and volume respectively for obtaining viscoelastic characteristics. Finally, regression models were built and compared using viscoelastic characteristics to determine the optimum prediction models and methods for two freshness indicators. The backpropagation neural network ( BPNN ) and SVR based on selected features were the best prediction models for pH and total volatile basic nitrogen ( TVB-N ) content evaluation in beef, and the correlation coefficients ( R c and R p ) of the calibration set and prediction set were 0.7636, 0.9036, and 0.7669, 0.8388, respectively. • A test electronic instrument based on airflow-3D machine vision was independently developed. • The depth and volume of spatial deformation were quantified by algorithms of point cloud image quantization. • Two four-element viscoelastic models of the creep recovery phase were established to extract viscoelastic characteristics. • Machine learning models for simultaneously predicting two common freshness indicators were built. • The prediction effects of different viscoelastic feature fusions and different models were compared.

An Interactive Two-Stage Framework for Simultaneous Machine Selection and Buffer Allocation

In manufacturing, machine selection and buffer allocation are extremely important and widely studied problems, as both can significantly affect the system performance. Solving either problem alone in a deterministic setting is already challenging. In this study, however, we simultaneously solve a high dimensional machine selection and buffer allocation problem in a highly stochastic and complex production line environment. The objective is to simultaneously select the optimal combination of machine type and buffer size allocation to maximize production throughput under limited cost and total buffer size constraints. We develop an efficient, interactive two-stage optimization method, called Machine Selection and Buffer Allocation algorithm (MSBA), that consists of a rapid machine selection (RMS) procedure and an adaptive global particle and local hyperbox search (AGPLHS) algorithm to solve the problem via simulation optimization. MSBA achieves seamless integration of RMS and AGPLHS so as to enable the simultaneous machine selection and buffer allocation to be solved efficiently. Furthermore, we compare our overall framework with three common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search). It is shown that our interactive two-stage framework outperforms the competing algorithms both in terms of effectiveness and efficiency. Note to Practitioners—Two important problems to solve in many production processes such as semiconductor manufacturing are machine selection and buffer allocation. Various methodologies have been utilized to solve each of the two problems individually. This work proposes an interactive two-stage simulation optimization algorithm which simultaneously solves both problems in complex, high dimensional and profoundly stochastic environments. While the most basic problem setting is a serial multi-workstation environment, complexities such as re-work stations, merging, splitting, parallel machine, and multi-product processor can be incorporated into the simulation model and solved using our proposed methodology. The methodology is user-friendly and intuitive yet adaptable enough to handle a wide range of production line environments or solve other stochastic optimization problems which require the simultaneous optimization of a mixture of binary and integer-based solutions. The numerical study, consisting of two different job-shop environments and including three benchmark algorithms, provides evidence for the efficiency of the proposed algorithm. Sensitivity analysis on user-defined parameters is also provided for the benefit of practitioners.

Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

PurposeMachine learning (ML) models are increasingly being used in industrial maintenance to predict system failures. However, less is known about how the time windows for reading data and making predictions affect performance. Therefore, the purpose of this research is to assess the impact of different sliding windows on prediction performance.Design/methodology/approachThe authors conducted a factorial experiment using high dimensional machine data covering two years of operation, taken from a real industrial case for the production of high-precision milled and turned parts. The impacts of different reading and prediction windows were tested for three ML algorithms (random forest, support vector machines and logistic regression) and four metrics (accuracy, precision, recall and F-score).FindingsThe results reveal (1) the critical role of the prediction window contingent upon the application domain, (2) a non-monotonic relationship between the reading window and performance, and (3) how sliding window selection can systematically be used to improve different facets of performance.Originality/valueThe study's findings advance the knowledge of ML-based failure prediction, by highlighting how systematic variation of two important but yet understudied factors contributes to the development of more useful prediction models.

Elevated blood pressure is associated with advanced brain aging in mid-life: A 30-year follow-up of The CARDIA Study.

High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear. We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP<120mm Hg, diastolic BP<80mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses. Every 5mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively). Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.

Formation and Destruction of Oxide Films during High-Speed Anodic Dissolution of Chromium-Nickel Steels in Electrolytes for Their Electrochemical Dimensional Machining

It is shown that at high-speed pulsed galvanostatic anodic dissolution of chromium-nickel steels Kh3N35VT (CSN17335), Kh18N10 (AIS1 304) in the regions of pulse durations of 20–2000 ms and current densities of 1–100 A/cm2 in electrolytes for their electrochemical dimensional machining (ECDM) (chloride, nitrate, and mixed chloride-nitrate with an electrical conductivity of 0.15 S/cm), a significant part of the charge (up to 50%) is spent on the formation of a passivating oxide film having a semiconductor character. As a result, the film, not the alloy, is subject to electrochemical treatment. As a result, the current output of the ECDM process of these materials under pulsed conditions is ~ 50–70%, depending on the composition of the alloy. The speed of the process increases with the transition to a constant processing current because of the destruction of the film due to its thermokinetic instability (“thermal explosion”) caused by an increase in the surface temperature.

Development of Technology for Producing Holes in Parts with Unpredictable Surface Fluctuations

The article deals with the methods of forming liquid rocket engines parts, in which sheet material is used as a blank. The analysis of the existing methods of processing workpieces with a variable surface profile allows us to conclude that the mechanical method is the most promising type of processing for hole perforation. The drilling holes technology in parts made of sheet blanks is presented. The features of processing non-rigid parts with unpredictable surface fluctuations are considered. The ways of ensuring the stability of the treatment process, increasing the technological indicators, which allowed expanding the area of dimensional machining, are shown. The results of the work contribute to improving the manufacturability of new generations products of equipment, which is important for mechanical engineering.

Surface Integrity of Wire Electrochemical Machined Inconel 718

Wire electrochemical machining (wire ECM) combines the advantages of wire electrical discharge machining (wire EDM) and electrochemical machining (ECM). The advantages of flexible 2.5 dimensional machining without the need for an application-specific fixture are combined with the excellent surface integrity properties of the ECM. These properties make the process particularly suitable for highly critical components, like fir tree slots in turbine discs. Knowledge of the resulting surface integrity in terms of microstructure formation, oxide layers, etc. is essential for such components. Therefore, in this paper modified rim zones are investigated by wire ECM e.g. with energy filtered transmission electron microscopy.

Design, development and test of a novel broach for long polypropylene tubes

Polymers are frequently used for a variety of applications in industry. One relevant field is plastic tubing. As plastic tubes are manufactured from thermoplastic material through hot-extrusion their dimensional accuracy is only moderate. Wherever high accuracy on tube diameters and roundness are required, the conventional production techniques reach their limits and more complex technologies are needed. Known approaches from metal machining are deep hole drilling and internal broaching. However, machining processes with chip removal on polymeric materials are not so common. Therefore, the authors studied cutting operations of polypropylene experimentally. Based on their findings they designed and built an internal broach for the machining of polypropylene tubes. Finally, the authors tested their developed broaching tool in a relevant industrial environment. The tool not only fulfills the dimensional machining requirements but also solves the present chip evacuation problem in a unique way. It enables free cutting through the whole operation and creates continuous straight chips with no need for chip breakers. Consequently, the timely cleaning step, required in traditional broaching, can be eliminated, which reduces the lead time significantly. Furthermore, the set of materials allows dry broaching. Coolant is not necessary. This leads to less waste, a cleaner production line, and avoids damage through aggressive liquids. Lastly, their design ensures that no loss of dimension occurs when resharpening the tool.

Blood pressure is associated with advanced brain aging patterns on MRI in midlife: The CARDIA Study

AbstractBackgroundHigh blood pressure has been associated with cognitive outcomes such as dementia. Most studies have focused on blood pressure in mid or late life, thus this association in early adulthood is undefined. We aimed to investigate the relationship between sub‐clinical blood pressure in early adulthood and brain aging in midlife.MethodFrom the prospective study of Coronary Artery Risk Development in Young Adults, 662 Black and White participants were identified with follow‐up of 30 years (age 18–30 years at baseline) and brain MRI at Year 30. Cumulative exposure of systolic and diastolic blood pressure (BP) from baseline through Year 30 were estimated by areas under the curve and analyzed as time‐weighted averages. We used a previously‐validated high dimensional machine learning pattern analysis method to derive brain aging indices that quantify individual differences in spatial patterns of age‐related atrophy: the SPARE‐BA index (Spatial Pattern of Atrophy for Recognition of Brain Aging). Cognitive function was assessed at year 30 with the Rey Auditory Verbal Learning Test (RAVLT), Digit Symbol Substitution Test (DSST), Stroop Test, and Verbal Fluency. We used linear regression models to estimate the associations between cumulative BP and predicted brain age and cognitive function at year 30.ResultCumulative systolic and diastolic BP were positively associated with higher predicted brain age (β: 0.10, 95% CI: 0.04‐0.17, β: 0.10, 95% CI: 0.02‐0.19, respectively), adjusted for chronological age at year 30, sex, education, and race. Participants with cumulative systolic or diastolic BP over the recommended guidelines (systolic BP&gt;120, diastolic BP&gt;80), had on average 3‐years older predicted brain age (95% CI: 1.00‐4.67; 95% CI: 1.45‐5.13, respectively), compared to those with normal levels. Moreover, higher predicted brain age was associated with poorer performance on the RAVLT (p= 0.009), DSST (p&lt;0.001), Stroop Test (p&lt;0.001) and Verbal Fluency (0.009).ConclusionElevated blood pressure from early to middle adulthood, especially above recommended guidelines, is associated with worse cognition in midlife and advanced brain aging. Elevated sub‐clinical blood pressure could be an important target for early intervention in order to prevent cognitive impairment.

Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations

Canada has extensive forests and peatlands that play key roles in global carbon cycle. Canadian soils and peatlands are assumed to store approximately 20% of the world’s soil carbon stock. However, the spatial and vertical distributions of the soil organic carbon (SOC) concentration in Canada have not been systematically investigated. SOC concentration, expressed in g C kg−1 soil, affects the chemical and physical properties of the soil, such as water infiltration ability, moisture holding capacity, nutrient availability, and the biological activity of microorganisms. In this study, we tested a three dimensional (3D) machine learning approach and 40 spatial predictors derived from 20 years of optical and microwave satellite observations to estimate the spatial and vertical distributions of SOC concentration in Canada in six depth intervals up to 1 m. A quantile regression forest method was used to build an uncertainty map showing 80% of prediction intervals. Results showed that a random forest model associated with 25 covariates was successful in capturing 83% of spatial and vertical SOC variation over the country. Soil depth was the most important covariate to predict SOC concentration, followed by surface temperature and elevation. The SOC concentration in forested areas was highest in the top layers (0–5 cm), but soils located in peatlands showed higher C concentration in all soil depths. Among the terrestrial ecozones of Canada, Pacific Maritime and the Hudson Plain mostly covered by forest trees and peatlands, respectively, show highest SOC concentration, while the lowest concentration are observed in the Prairies and Mixed Wood Plain ecosystems that are the agricultural areas of the country. This study provides a deeper understanding of the major factors controlling SOC concentration in Canada and shows potential areas with high carbon reserves, which are crucial in view of the ongoing climate change. In addition, the presented methodological framework has great potential to be used in future soil carbon storage inventories using satellite observations. Mapping SOC concentration and associated uncertainties in Canada are fundamental to detect trends of gains or losses in SOC linked to recent and future national or global policy decisions related to soil quality and carbon sequestration.

Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models (such as CNN models), heavy communication traffic can be incurred by exchanging model updates via the Internet between clients and the parameter server (PS), implying that the network resource can be easily exhausted. Compressing model updates is an effective way to reduce the traffic amount. However, a flexible unbiased compression algorithm applicable for both uplink and downlink compression in FL is still absent from existing works. In this work, we devise the Model Update Compression by Soft Clustering (MUCSC) algorithm to compress model updates transmitted between clients and the PS. In MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of each model update. Moreover, we prove that: 1) The compressed model updates are unbiased estimation of their original values so that the convergence rate by transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that the influence of the compression error on the model accuracy is minimized. Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster. B-MUCSC is suitable for scenarios with very scarce network resource. Ultimately, we conduct extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate that our algorithms can not only substantially reduce the volume of communication traffic in FL, but also improve the training efficiency in practical networks.

Abnormal Power Consumption Detection Based on Data-Driven

Based on high dimensional random matrix theory and machine learning algorithm, a method to detect abnormal power consumption behaviour of users is proposed. Firstly, the K-means clustering algorithm is used to divide the power loads into load types that obey specific distribution law or with random fluctuation. Then the linear eigenvalue statistics (LES) index can be used to detect the abnormal power consumption behaviour for the former such as unimodal load or bimodal load. And the difference between the actual and predicted value of regression model based on XGBoost algorithm can be used as the basis for judging abnormal power consumption behaviour of the latter. The method proposed in this paper is applicable to different types of loads and can implement a good discriminant effect.

A consensus-based global optimization method for high dimensional machine learning problems

We improve recently introduced consensus-based optimization method, proposed in [R. Pinnau, C. Totzeck, O. Tse, S. Martin, Math. Models Methods Appl. Sci. 27 (2017) 183–204], which is a gradient-free optimization method for general non-convex functions. We first replace the isotropic geometric Brownian motion by the component-wise one, thus removing the dimensionality dependence of the drift rate, making the method more competitive for high dimensional optimization problems. Secondly, we utilize the random mini-batch ideas to reduce the computational cost of calculating the weighted average which the individual particles tend to relax toward. For its mean-field limit – a nonlinear Fokker-Planck equation – we prove, in both time continuous and semi-discrete settings, that the convergence of the method, which is exponential in time, is guaranteed with parameter constraints independent of the dimensionality. We also conduct numerical tests to high dimensional problems to check the success rate of the method.

Удосконалення процесу обробки тіл обертання електричною дугою

In modern mechanical engineering, electrical discharge machining (EDM) methods are widely used for machining bodies of rotation from difficult-to-machine materials. Those methods ensure sparing cutting and make it possible to machine any electrically conductive material irrespective of its physical and chemical properties, in particular hardness. There is a known method for dimensional machining of bodies of rotation with electric arc using a wire electrode tool that is pulled along in the machining area thus "compensating" for that tool's EDM wear and tear. The machining accuracy is therefore significantly heightened. However, when implementing this method, an effect of splashing the working fluid outside the working area of the machine and a pronounced luminous effect from the burning of the electric arc in the machining area are observed. That worsens the working conditions. In addition, when pulling the wire electrode tool along the convex surface of the electrode holder, the sliding friction arises, which eventually leads to mechanical destruction of the contact point. As a result, a deep kerf is formed on the electrode holder. When the depth of the kerf reaches the diameter of the wire electrode tool, the destruction of the electrode holder by the electric arc begins. Consequently, the durability of the electrode holder in the known method is unsatisfactory. A method of dimensional machining of bodies of rotation with electric arc using a wire electrode tool with the immersion of the machining area in the working fluid has been proposed, which makes it possible to improve the working conditions of the operator by eliminating the effect of fluid splashing and removing the luminous effect of arc burning in the machining area. In addition, it has been proposed to make the electrode holder in the form of a roller that rotates with a guide groove for the wire electrode tool, while the nozzle for creating the transverse hydrodynamic fluid flow has been proposed to be mounted in a separate fixed housing that is adjacent to the electrode holder. This technical solution replaces the sliding friction with the rolling one thus enhancing the durability of the electrode holder. Mathematical models of the process characteristics of the DMA-process (dimensional machining with electric arc) for bodies of rotation using a wire electrode tool with the immersion of the machining area in the working fluid have been obtained that make it possible to control the machining productivity, the specific machining productivity, the specific electric power consumption, and the roughness of the surface machined.

Convergence of a first-order consensus-based global optimization algorithm

Global optimization of a non-convex objective function often appears in large-scale machine learning and artificial intelligence applications. Recently, consensus-based optimization (CBO) methods have been introduced as one of the gradient-free optimization methods. In this paper, we provide a convergence analysis for the first-order CBO method in [J. A. Carrillo, S. Jin, L. Li and Y. Zhu, A consensus-based global optimization method for high dimensional machine learning problems, https://arxiv.org/abs/1909.09249v1 ]. Prior to this work, the convergence study was carried out for CBO methods on corresponding mean-field limit, a Fokker–Planck equation, which does not imply the convergence of the CBO method per se. Based on the consensus estimate directly on the first-order CBO model, we provide a convergence analysis of the first-order CBO method [J. A. Carrillo, S. Jin, L. Li and Y. Zhu, A consensus-based global optimization method for high dimensional machine learning problems, https://arxiv.org/abs/1909.09249v1 ] without resorting to the corresponding mean-field model. Our convergence analysis consists of two steps. In the first step, we show that the CBO model exhibits a global consensus time asymptotically for any initial data, and in the second step, we provide a sufficient condition on system parameters — which is dimension independent — and initial data which guarantee that the converged consensus state lies in a small neighborhood of the global minimum almost surely.

Feature selection via computational intelligence techniques

Feature selection (FS) has become an essential task in overcoming high dimensional and complex machine learning problems. FS is a process used for reducing the size of the dataset by separating or extracting unnecessary and unrelated properties from it. This process improves the performance of classification algorithms and reduces the evaluation time by enabling the use of small sized datasets with useful features during the classification process. FS aims to gain a minimal feature subset in a problem domain while retaining the accuracy of the original data. In this study, four computational intelligence techniques, namely, migrating birds optimization (MBO), simulated annealing (SA), differential evolution (DE) and particle swarm optimization (PSO) are implemented for the FS problem as search algorithms and compared on the 17 well-known datasets taken from UCI machine learning repository where the dimension of the tackled datasets vary from 4 to 500. This is the first time that MBO is applied for solving the FS problem. In order to judge the quality of the subsets generated by the search algorithms, two different subset evaluation methods are implemented in this study. These methods are probabilistic consistency-based FS (PCFS) and correlation-based FS (CFS). Performance comparison of the algorithms is done by using three well-known classifiers; k-nearest neighbor, naive bayes and decision tree (C4.5). As a benchmark, the accuracy values found by classifiers using the datasets with all features are used. Results of the experiments show that our MBO-based filter approach outperforms the other three approaches in terms of accuracy values. In the experiments, it is also observed that as a subset evaluator CFS outperforms PCFS and as a classifier C4.5 gets better results when compared to k-nearest neighbor and naive bayes.

Specification of Dimensional Machining of Product Surfaces

Experiments must often be conducted to make good the lack of useful initial data in specifying the conditions of dimensional machining. As shown in the present work, the effort and cost of such experiments may be considerably decreased—especially for products with geometrically complex surfaces—by making use of similarity theory and physical modeling. This approach to specifying the manufacturing conditions of product batches proves effective in practice, as illustrated for the final grinding of products with spherical surfaces and wear-resistant coatings.

МОДЕЛЮВАННЯ ТЕПЛОВИХ ПРОЦЕСІВ ФОРМУВАННЯ ОДИНИЧНОЇ ЛУНКИ РОЗРЯДАМИ СУЧАСНИХ ГЕНЕРАТОРІВ ЕЛЕКТРОЕРОЗІЙНИХ ВИРІЗНИХ ВЕРСТАТІВ

The peculiarities of forming a single crater geometry and heat affected zone depth during wire electrodischarge machining of a high-quality structural steel 45 on machines equipped with modern current generators are considered. With the help of mathematical modeling based on numerical solutions of the axisymmetric quasi-three-phase nonlinear thermal conductivity problem with moving phase boundaries, the curves describing the phase boundaries of the evaporation, material melting and austenitic transformation temperature, depending on energy parameters of the discharge and material thermophysical properties are received. The volumes of the material removed from the crater in the form of steam and liquid are estimated. The data on the energies spent on the phase transformation of the anode material: the melting and evaporation, their share in the total discharge energy applied to the steel anode at different processing modes, as well as the consequences of changing the pulse parameters in each mode are given. It is found that the increase in the pulse duration with increasing the total energy consumed in the electrode gap, leads to the efficiency decrease of overall energy usage for the destruction of the workpiece material. The adequacy of the model is evaluated by comparison with the experimental data, which have been repeatedly confirmed on the equipment of various enterprises. The obtained results open a promising way to minimize energy costs for the processing of materials and control the characteristics of the treated surfaces both in terms of the formation of microgeometry and the depth of structure changes in the heat affected zone. This, in its turn, will allow to expand the limits of the erosion technology application by increasing economic benefits from the use of the described method. In addition, the above-mentioned creates preconditions for new effective designs of technological modes of subsequent electrochemical dimensional machining with a fixed wire electrode, since the depth of the heat affected zone is an important factor in determining the allowance which needs to be removed in electrochemical surface modification.