Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components
Background. A pressing challenge in mechanical engineering is increasing labour productivity during the “cutting” operation. This topic is highly relevant because cutting tool capabilities continue to lag behind the technical potential of modern automated turning equipment. The solution is to apply...
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| author | Petrova, Desislava Balabanova, Ivelina Georgiev, Georgi Lengerov, Angel |
| author_facet | Petrova, Desislava Balabanova, Ivelina Georgiev, Georgi Lengerov, Angel |
| author_institution_txt_mv | [
{
"author": "Desislava Petrova",
"institution": "Technical University of Gabrovo, Gabrovo, Bulgaria "
},
{
"author": "Ivelina Balabanova",
"institution": "Technical University of Gabrovo, Gabrovo, Bulgaria"
},
{
"author": "Georgi Georgiev",
"institution": "Technical University of Gabrovo, Gabrovo, Bulgaria"
},
{
"author": "Angel Lengerov",
"institution": "Technical University of Sofia, Plovdiv, Bulgaria"
}
] |
| author_sort | Petrova, Desislava |
| baseUrl_str | https://ees-journal.com/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-06-30T15:36:43Z |
| description | Background. A pressing challenge in mechanical engineering is increasing labour productivity during the “cutting” operation. This topic is highly relevant because cutting tool capabilities continue to lag behind the technical potential of modern automated turning equipment. The solution is to apply innovative methods and software in production management and mechanical engineering.
Purpose. The aim is to analyse and optimise the processing modes of complex parts and manage the implementation of innovations in mechanical engineering to achieve higher efficiency.
Findings. A comparable tendency was identified in the second classification category of the probabilistic neural network model, characterised by improved performance indicators. For the model with comparatively lower classification performance, both the first and second output categories achieved the same accuracy of 90.0%. An assessment of the structural and technological characteristics of components with intricate profile geometries indicates that machining efficiency is strongly influenced by the complexity of part geometry and the diversity of manufacturing procedures when automated multifunctional equipment is applied. The final values of the “weight coefficients W” and “bases B” were determined, and the resulting matrix structures support compliance with the minimum Mean-Squared Error (MSE) criterion while increasing the reliability of predictive outcomes in evaluating production risk for mechanically engineered components and systems.
Implications. The evaluation of machining-mode selection demonstrates that identifying optimal manufacturing conditions for components with sophisticated profile surfaces processed on automated systems remains a major engineering and economic challenge. Existing approaches for parametric optimisation insufficiently incorporate technological constraints. As the application of materials with specific physical and mechanical characteristics expands, along with the increasing geometric complexity of components and the wider implementation of multifunctional automated systems, technological production planning increasingly depends on the effective determination of cutting parameters and tool geometry, thereby contributing to improved manufacturing performance. |
| doi_str_mv | 10.61954/2616-7107/2026.10.2-9 |
| first_indexed | 2026-07-01T01:00:34Z |
| format | Article |
| fulltext |
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
124
Research Article
UDC 621.9:658.51
JEL: M11, O32, D24
RESOURCE MANAGEMENT AND INNOVATIVE
OPTIMISATION OF MACHINING MODES IN THE
PRODUCTION OF COMPLEX-SHAPED
COMPONENTS
Desislava Petrova *
Technical University of Gabrovo,
Gabrovo, Bulgaria
ORCID iD: 0000-0002-8970-5722
Ivelina Balabanova
Technical University of Gabrovo,
Gabrovo, Bulgaria
ORCID iD: 0000-0002-0835-1732
Georgi Georgiev
Technical University of Gabrovo,
Gabrovo, Bulgaria
ORCID iD: 0000-0001-5130-8652
Angel Lengerov
Technical University of Sofia,
Plovdiv, Bulgaria
ORCID iD: 0009-0003-5475-2216
*Corresponding author
E-mail: des_petrova@abv.bg
Background. A pressing challenge in mechanical
engineering is increasing labour productivity during the
“cutting” operation. This topic is highly relevant because
cutting tool capabilities continue to lag behind the technical
potential of modern automated turning equipment. The
solution is to apply innovative methods and software in
production management and mechanical engineering.
Purpose. The aim is to analyse and optimise the
processing modes of complex parts and manage the
implementation of innovations in mechanical engineering to
achieve higher efficiency.
Findings. A comparable tendency was identified in the
second classification category of the probabilistic neural
network model, characterised by improved performance
indicators. For the model with comparatively lower
classification performance, both the first and second output
categories achieved the same accuracy of 90.0%. An
assessment of the structural and technological characteristics
of components with intricate profile geometries indicates that
machining efficiency is strongly influenced by the complexity
of part geometry and the diversity of manufacturing
procedures when automated multifunctional equipment is
applied. The final values of the “weight coefficients W” and
“bases B” were determined, and the resulting matrix
structures support compliance with the minimum Mean-
Squared Error (MSE) criterion while increasing the reliability
of predictive outcomes in evaluating production risk for
mechanically engineered components and systems.
Implications. The evaluation of machining-mode
selection demonstrates that identifying optimal manufacturing
conditions for components with sophisticated profile surfaces
processed on automated systems remains a major engineering
and economic challenge. Existing approaches for parametric
optimisation insufficiently incorporate technological
constraints. As the application of materials with specific
physical and mechanical characteristics expands, along with
the increasing geometric complexity of components and the
wider implementation of multifunctional automated systems,
technological production planning increasingly depends on
the effective determination of cutting parameters and tool
geometry, thereby contributing to improved manufacturing
performance.
Keywords: Manufacturing Efficiency, Mechanical
Engineering, Optimisation, Production, Risk Assessment.
Received: 05/03/2026
Revised: 27/05/2026
Accepted: 10/06/2026
Published: 30/06/2026
DOI: 10.61954/2616-7107/2026.10.2-9
© Economics Ecology Socium, 2026
CC BY-NC 4.0 license
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
125
1. Introduction.
Innovation obsolescence occurs due to
ongoing changes driven by continuous
technological improvements, as innovations
serving identical production functions become
obsolete. There are certain interrelations and
interdependencies (a constellation) between
innovation development and obsolescence that
characterise the process of innovation
obsolescence.
The sustained trends of economic
globalisation drive the rapid adoption of
advanced technologies and automated
manufacturing systems to improve
productivity, enhance product quality, increase
process flexibility and sustainability, and
reduce the time required to bring products from
concept to market.
Worldwide industrial development
demonstrates that manufactured products are
becoming more sophisticated in both structural
design and functional performance requirements
(Dahmani et al., 2021; Demirova, 2019).
Contemporary production concepts, therefore,
require a substantially different approach to
industrial manufacturing systems
(Aleksandrova et al., 2025a; Petrova et al.,
2025).
Currently, mechanical processing is the
most common method for producing various
types of parts, including those with complex
profiled surfaces. Despite the development and
widespread implementation of methods for
obtaining accurate blanks using concentrated
energy flows for shaping (electrochemical,
electrophysical, electron-beam processing,
etc.), the proportion of surfaces subjected to
mechanical processing remains sufficiently
high.
The analysis of the literature and
production data (Aleksandrova et al., 2025b;
Dahmani et al., 2021) indicates that 80-85% of
component blanks are processed by cutting, and
the labour intensity of these operations
accounts for approximately 60% or more of the
total labour intensity of component
manufacturing. Therefore, determining the
appropriate modes of operation for the
instruments is an important technical and
economic task.
Its importance increases with the
introduction of wide-scale automation of
machine-building production, the application of
multi-operation automated Computer Numerical
Control Machines (CNC) lathes, and the use of
new materials characterised by poor
machinability during cutting (Marinov, 2024).
Cutting is the most effective dimensional
processing method across productivity, cost,
energy consumption, environmental
friendliness, processing accuracy, and quality.
In the coming decades, cutting will remain the
primary technological method for dimensional
processing (Mitev, 2024). The following factors
can explain this:
- The increasing use of materials with
special physical and mechanical properties,
characterised by low machinability, increases
the cost of producing the part.
- Complicated configuration of the
components, with simultaneous strict
requirements for accuracy and quality of the
processed surfaces.
- Multi-purpose automated CNC lathes are
used to process complex profile surfaces, which
require a special approach to selecting
mechanical processing modes.
In the production of components
combining complex surfaces (disks, shafts,
rings, vanes, etc.), mechanical processing
operations predominate, particularly turning.
Machining operations constitute more than 50%
of the labour intensity of aircraft engines. The
final mechanical dimensional processing of
parts is a perspective on a foreseeable period of
development in mechanical engineering
technology (Aleksandrova et al., 2025a). An
actual problem in machining production is
increasing the labour productivity of the
"cutting" operation. This topic is highly relevant
because cutting tool capabilities continue to lag
behind the technical potential of modern
automated lathes. When the allowance is
removed or when a constant shear layer section
is used, cutting speed is the primary factor
affecting productivity. However, changing this
parameter alters the nature and intensity of wear
on the cutting tool, temperature in the cutting
zone, processing accuracy, properties of the
surface layer material, and other indicators
(Liao, 2023; Tapie et al. 2012).
Economics Ecology Socium e-ISSN 2786-8958
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Therefore, from a technical perspective,
increasing the efficiency of the machining
process through cutting should be considered a
complex management measure, with all main
factors ensured using mathematical methods of
calculation and optimisation.
A significant part of the developments in
cutting mode optimisation is based on
determining the variables influencing the cutting
process using empirical methods under
conditions aligned with the main goals of
increasing productivity and reducing the cost of
cutting tools, with set requirements for accuracy
and surface roughness.
Vachev (1998) noted that considerable
attention has been paid to the development of
empirical formulas to facilitate optimisation
processes. In addition, the cutting modes were
optimised using the developed empirical
formulas that account for various variables
influencing the cutting process (Velchev et al.,
2014). A review of the literature shows that
there are currently no universal analytical
dependencies that reflect the relationships
among all factors affecting the cutting process
(Salapateva & Lengerov, 2025). It is important
to note that, given the parameters of the cutting
modes, it is not possible to establish the
relationships between many influencing factors
using empirical formulas. In this regard,
enterprises used simplified metal-cutting
machines and a limited range of materials for
cutting tools and workpieces.
With the development of mechanical
engineering, especially the automation of
metalworking equipment, it is necessary to
consider a much larger number of variables than
those used in existing empirical formulas when
calculating the cutting modes. This necessitates
the development of new methods for
determining cutting modes and is a prerequisite
for creating complex dependencies that can be
presented as a table or nomogram.
The analysis shows that determining the
cutting modes using tables and nomograms is
analogous to using formulas, but is simpler and
thus feasible. However, it should be noted that
general mechanical engineering regulations
serve only as prerequisites for establishing load
norms and do not guarantee the necessary
flexibility.
These shortcomings can also be noted in
relation to the recommendations for companies
producing cutting tools.
The creation and implementation of
automated systems for the design of
technological processes for the component
manufacturing, as well as for the automation of
these processes, pose complex challenges for
the theory of cutting. They concluded by
formalising the basis for deep physical research
into all the interrelationships of the cutting
process, determining its performance,
accuracy, quality, and, most importantly, its
reliability.
The calculation task for determining the
processing conditions is complicated by the
need to comprehensively assess input
parameters when selecting a cutting mode
combination, including the tool's durability,
required accuracy, surface quality, and mode-
specific factors. All of these are affected
differently by temperature and cutting forces,
which together determine the tool longevity,
processing accuracy, and surface-layer
characteristics. It is necessary to choose the
depth, feed, and cutting speed in a complex
manner, ensuring compliance with the
requirements for the workpiece and tool, as well
as the operating conditions of the equipment
used.
2. Methodology.
The solution to this complex technological
task is possible through advanced mathematical
models that account for the interrelationships
among the main parameters of the machining
process. The created mathematical models must
be distinguished by sufficient accuracy and
universality to yield solutions on computing
equipment of a different class, and the emphasis
in the development of mechanical processing
technologies will be on solving technical tasks
related to the automation of processing (Petrova
et al., 2025).
When choosing a variant of a
technological process, including a variant for
preserving a specific operation, the principle of
optimality or the principle of ensuring the most
advantageous conditions under the set
technological limitations must be observed.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
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For a precise selection of a technological
procedure applied in machining operations of
parts, it is necessary to determine the value of
the sought-after technological parameter, which
will ensure the greatest efficiency of the process
at the specified quality of the produced output,
productivity, costs of working capital, and
technological and organisational-technical
capabilities for fixed assets. With such a
solution to a given task, it is necessary to
perform it considering the relationship between
the specified requirements for the part (surface
roughness, depth and degree of overlap, residual
stresses, dimensional accuracy, etc.) and
processing modes (tool geometry, brand of the
tool material, cutting modes, etc.).
Calculations of the relationship between
technological processing conditions are carried
out, in particular, the cutting modes, the
physical and mechanical properties of the
workpiece and tool materials and other
parameters, and the initial characteristics of the
cutting process, such as wear resistance of the
tool, productivity, cost of processing, and
characteristics of the quality of the treated
surface (Metev et al., 2025).
Many models have been derived from
experimental data. However, they only allow
the determination of the optimal cutting speed
and the corresponding relative magnitude of the
tool surface wear. Under certain requirements,
the resulting models may be related to the
physical basis of a process. That is, these
models in their entirety cannot be used to solve
technological tasks.
2.1. Innovative Development on
Technology.
The dependencies are obtained based on
the analysis of the results of a theoretical study
of the method according to the theory of
similarity and also sufficiently reliably evaluate
the physical phenomenon associated with the
chip separation process, on the one hand, and on
the other hand, with the formation of the
material of the surface layer of the processed
part (Petrova et al., 2025).
The influence of technological innovation
is reflected in improvements to existing
machines and equipment and in the construction
of fundamentally new machines.
Recent industrial practice shows a
growing tendency toward modular
standardisation and unification of machine
components. This approach supports the rapid
configuration of machines for different
technological applications while maintaining
common functional characteristics, reflecting
the concept of mechatronic manufacturing
systems.
Research into the patterns of technological
progress and enhancement enables identification
of the time interval during which a particular
machine generation provides maximum
efficiency for a manufacturing process, as well
as the transition period required to introduce
new machine types into production (Petrova et
al., 2025).
This is also the place to focus on the
management and optimisation of the processing
modes of complex parts, as well as on
implementing innovations in mechanical
engineering to achieve higher efficiencies.
It has been established that the
extraordinary variety of options for performing
operations in the mechanical processing of parts
requires general solutions to the problem of
selecting mode conditions when using process
technology (Demirova, 2019). The proposed
options cannot be determined solely using
experimental research methods. The
methodology and program for calculating the
technological conditions of processing, as well
as the parameters of the surface layer quality
and the processing accuracy, do not account for
cutting tool wear, the technological cost of
processing, and the performance of the
operation in the considered modes. The
proposed models are general, as they were
obtained through theoretical studies.
2.2. Technical Standardisation of
Cutting Modes.
Another argument is that the calculation
of the cutting modes is basically technical
standardisation, and that is why the determined
speed, feed, depth of cut, wear resistance of the
tool, intermediate allowances, and tolerances
providing a minimum or a maximum of the
optimality criterion, taking into account all the
restrictions, are reported within the technical
standardisation.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
128
Thus, the developed methodology and
optimisation algorithms address the task of
improving technical norming and determining
basic time norms. The development of
technological processes for manufactured parts
within a given enterprise is a complex task, with
corresponding requirements that must be met as
a semi-finished product is transformed into a
finished product that meets all requirements for
its purpose. Before comparing the different
variants of the technological process, internal
optimisation for each variant must be performed
(Metev et al., 2024).
Currently, a significant number of
methods for choosing cutting modes for
individual mathematical treatments have been
developed, and they can be divided into three
groups depending on the way they are solved to
find a conditional maximum (a classic example
is determining the optimal cutting speed), to use
mathematical programming and for optimal
management. The analysis of these methods
shows that all of them have certain
shortcomings. The task of determining the
cutting modes should be understood precisely as
an optimal design and management problem.
Models can be used with exponents, such
as in the system setup size part, the tool wear
rate, and the cutting power. For example, the
parameters of the cutting process affect the
changes in physicochemical phenomena
accompanying processing, as well as their
intensity. Technological optimisation is
structural (intended to optimise the sequencing
of transitions and operations during mechanical
processing) and parametric (to optimise process
parameters for individual operations).
2.3. Technological Optimisation and
Production Management.
The main features of technological
optimisation, reflecting the goals in the
production of engineering products, are as
follows (Skorkin et al., 2019):
Technological solutions, starting from
the choice of the initial workpiece, the sequence
of processing, the choice of the cutting tool, and
the cutting modes.
The structure of any technological
solution is determined by the large number of its
constituent elements and their connections.
Choosing an optimal technological
solution at the intermediate stages is an
undefined task.
Despite the noted complexity, sufficiently
complex mathematical models can be developed
to assess the durability of cutting tools for heat-
resistant alloys, titanium alloys, and high-
strength steels.
Analysing the content of these model
dependencies, we should note that:
By offering these models for the
different groups of processed materials, the
physico-mechanical properties of the materials
within each group are not considered.
The proposed models are first-order
dependencies, where the range of change of the
variable parameters is not given; the
dependence of durability on the elements of the
cutting mode is a non-monotonic function.
The offered models are not sufficiently
complex and do not allow solving tasks in
external and internal technological optimisation
when processing complex profile parts.
As already noted, optimising the
technological parameters is a complex
optimisation task due to the multivariate nature
of the technological processes, the complexity
of the configurations of the processed parts, etc.
In real production conditions, this task is also
complicated by the fact that, during processing,
the cutting mode parameters and the geometry
of the cutting tool change (Velchev et al., 2014).
Therefore, the best option for mechanical
processing should be selected, considering the
accepted optimality criteria and integrating it
into the variable control of the cutting process.
It has been proven that spatial and temporal
variability related to the geometric shape of the
processed surface, variable-depth processing,
allowance fluctuations, and changes in the
cutting process geometry are important during
processing.
However, it should be noted that
stationary cutting processes do not exist,
especially when machining difficult-to-machine
special steels and alloys.
This is because tool wear occurs rapidly,
and the temperature-load conditions that
determine tool reliability and surface-layer
material quality change sharply during the
processing of even a single part.
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Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
134
4. Conclusions.
Based on the research and analysis
conducted above, we can summarize the
following conclusions.
From a geometric perspective, the
classification of surfaces based on the variety of
"their forms and ways of obtaining" cannot be
scientifically justified. However, systematising
the approach to surface formation will enable
the correct solution of tasks related to modelling
complex profiled surfaces and the technological
methods for their production.
The class of parts with complex rotational
shapes includes cylindrical parts (smooth and
stepped). Therefore, all methods for processing
cylindrical parts can be considered as
technologies for processing rotary profile
surfaces. Components characterised by complex
rotational geometry consist of combinations of
basic surfaces formed by straight and curved
elements, including one or several geometric
configurations such as cylindrical, frontal,
conical, spherical, elliptical, or parabolic
surfaces. Each elementary rotational surface can
be represented mathematically for theoretical
evaluation, while the coordinated motions of the
generating and guiding mechanisms support the
effective implementation of appropriate
manufacturing methods.
Different technological strategies are
available for rough machining of complex-
shaped components without additional
allowances, allowing efficient operation in
partially automated production environments.
In most cases, processing workpieces with
a complex rotational shape is a multipass
process, with work moves performed parallel or
perpendicular to the workpiece axis and
equidistant from the workpiece contour. Cutting
modes when turning parts with complex
rotational shapes are non-stationary. When
using CNC lathes and adaptive control systems,
the cutting conditions vary from one working
stroke to the next.
Analysis of design and manufacturing
characteristics of parts with complex-shaped
components shows that the complexity of their
geometry, together with the diversity of
manufacturing procedures, strongly influences
the effectiveness of machining operations
performed on multifunctional automated
equipment equipped with digital-program-
controlled (DPC) systems.
Given the quantity of materials with
specific physical-mechanical properties, the
complex configuration of parts, and the use of
automated multifunctional equipment, the
primary objective of technological production
planning is to select cutting modes and tool
geometry, thereby optimising the task and
increasing production efficiency.
The investigation of machining-mode
selection issues indicates that defining optimal
production conditions for components with
complex profile surfaces on automated
equipment remains a major technical and
economic challenge. At present, there is still a
shortage of parametric optimisation
methodologies that adequately incorporate
technological constraints.
The final “weight coefficients W” and
“bases B” were calculated. The indicated matrix
objects ensure that the minimum MSE criterion
is met and that the forecast results are the most
reliable when analysing the level of production
risk for complex mechanical engineering parts
and structures.
Conflict of Interest Statement.
The authors declare no conflict of interest.
Funding Disclosure.
This research received no external
funding.
Data Availability.
Data are available from the authors upon
reasonable request.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
135
REFERENCES
Aleksandrova, I., Metev, H., & Kolev, N. (2025a). Determining the number of cutting tools,
ensuring a continuous work cycle in CNC turning machines with robotic loading. 2025
International Conference Automatics and Informatics (ICAI), 384–388. IEEE.
https://doi.org/10.1109/ICAI67591.2025.11324509
Aleksandrova, I., Metev, H., & Kolev, N. (2025b). Multi-purpose optimization of the turning
process on CNC machines with robotic loading. 2025 International Conference Automatics
and Informatics (ICAI), 378–383. IEEE. https://doi.org/10.1109/ICAI67591.2025.11324854
Dahmani, N., Benhida, K., Belhadi, A., Kamble, S., Elfezazi, S., & Jauhar, S. K. (2021). Smart
circular product design strategies towards eco-effective production systems: A lean eco-
design industry 4.0 framework. Journal of Cleaner Production, 320(128847), 128847.
https://doi.org/10.1016/j.jclepro.2021.128847
Demirova, S. (2019). Turning knowledge into innovation and innovation into an effective product
concept. 2019 International Conference on Creative Business for Smart and Sustainable
Growth (CREBUS). https://doi.org/10.1109/CREBUS.2019.8840107.
Liao, H. (2023). The processing method of CNC lathe processing technology for complex parts.
Journal of Engineering Mechanics and Machinery, 8(3), 7-12.
https://doi.org/10.23977/jemm.2023.080302
Marinov, M. (2024). Vector-matrix computer method for drafting circling-point curves and
centering-point curves of Burmester. Environment Technology Resources Proceedings of the
International Scientific and Practical Conference, 2, 440–443.
https://doi.org/10.17770/etr2024vol2.8072
Metev, H., Aleksandrova, I., & Kolev, N. (2025). Multi-objective optimization of cutting
parameters for CNC turning 42crmo4 hardened steel using utility function. Environment
Technology Resources Proceedings of the International Scientific and Practical Conference, 4,
217–223. https://doi.org/10.17770/etr2025vol4.8413
Metev, H., Krumov, K., & Vlahova, B. (2024). Economic aspects of the modular tools. AIP
Conference Proceedings, 3016, 060001. https://doi.org/10.1063/5.0184231
Mitev, I. (2024). Optimizing the quantity of liquid phase at the sintering of powder construction
materials from system Fe-Cu-Sn. AIP Conference Proceedings, 3016, 060017.
https://doi.org/10.1063/5.0184238.
Petrova, D., Balabanova, I., & Georgiev, G. (2025). Digital Program Control and Complex
Component Processing in Performance-Driven Production Engineering. Economics Ecology
Socium, 9(4), 129–139. https://doi.org/10.61954/2616-7107/2025.9.4-9
Salapateva, S., & Lengerov, A. (2025). Cutting modes in multi-pass turning on CNC lathes.
Environment Technology Resources Proceedings of the International Scientific and Practical
Conference, 4, 369–373. https://doi.org/10.17770/etr2025vol4.8424
Skorkin, A., Kondratyuk, O., Lamnauer, N., & Burdeinaya, V. (2019). Improving efficiency of
machining the geometrically complex shaped surfaces by milling with a fixed shift of the
cutting edge. Eastern-European Journal of Enterprise Technologies, 2(1 (98), 60–69.
https://doi.org/10.15587/1729-4061.2019.163325
Tapie, L., Mawussi, B., & Bernard, A. (2012). Topological model for machining of parts with
complex shapes. Computers in Industry, 63(5), 528–541.
https://doi.org/10.1016/j.compind.2012.02.005
Vachev, A. (1998). Synthesis and analysis of kinematic cutting schemes for rotating tool and
workpiece. Plovdiv, Bulgaria: Technical University of Plovdiv.
Velchev, S., Kolev, I., Ivanov, K., & Gechevski, S. (2014). Empirical models for specific energy
consumption and optimization of cutting parameters for minimizing energy consumption
during turning. Journal of Cleaner Production, 80, 139–149.
https://doi.org/10.1016/j.jclepro.2014.05.099
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| institution | Economics Ecology Socium |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-01T01:00:34Z |
| publishDate | 2026 |
| publisher | Dr. Viktor Koval |
| record_format | ojs |
| resource_txt_mv | ees-journalcom/c5/66318f3ae9ade8babb975af9e7701ec5.pdf |
| spelling | oai:ojs2.www.ees-journal.com:article-3482026-06-30T15:36:43Z Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components Petrova, Desislava Balabanova, Ivelina Georgiev, Georgi Lengerov, Angel Manufacturing Efficiency, Mechanical Engineering, Optimisation, Production, Risk Assessment. Manufacturing Efficiency, Mechanical Engineering, Optimisation, Production, Risk Assessment. Background. A pressing challenge in mechanical engineering is increasing labour productivity during the “cutting” operation. This topic is highly relevant because cutting tool capabilities continue to lag behind the technical potential of modern automated turning equipment. The solution is to apply innovative methods and software in production management and mechanical engineering. Purpose. The aim is to analyse and optimise the processing modes of complex parts and manage the implementation of innovations in mechanical engineering to achieve higher efficiency. Findings. A comparable tendency was identified in the second classification category of the probabilistic neural network model, characterised by improved performance indicators. For the model with comparatively lower classification performance, both the first and second output categories achieved the same accuracy of 90.0%. An assessment of the structural and technological characteristics of components with intricate profile geometries indicates that machining efficiency is strongly influenced by the complexity of part geometry and the diversity of manufacturing procedures when automated multifunctional equipment is applied. The final values of the “weight coefficients W” and “bases B” were determined, and the resulting matrix structures support compliance with the minimum Mean-Squared Error (MSE) criterion while increasing the reliability of predictive outcomes in evaluating production risk for mechanically engineered components and systems. Implications. The evaluation of machining-mode selection demonstrates that identifying optimal manufacturing conditions for components with sophisticated profile surfaces processed on automated systems remains a major engineering and economic challenge. Existing approaches for parametric optimisation insufficiently incorporate technological constraints. As the application of materials with specific physical and mechanical characteristics expands, along with the increasing geometric complexity of components and the wider implementation of multifunctional automated systems, technological production planning increasingly depends on the effective determination of cutting parameters and tool geometry, thereby contributing to improved manufacturing performance. Background. A pressing challenge in mechanical engineering is increasing labour productivity during the “cutting” operation. This topic is highly relevant because cutting tool capabilities continue to lag behind the technical potential of modern automated turning equipment. The solution is to apply innovative methods and software in production management and mechanical engineering. Purpose. The aim is to analyse and optimise the processing modes of complex parts and manage the implementation of innovations in mechanical engineering to achieve higher efficiency. Findings. A comparable tendency was identified in the second classification category of the probabilistic neural network model, characterised by improved performance indicators. For the model with comparatively lower classification performance, both the first and second output categories achieved the same accuracy of 90.0%. An assessment of the structural and technological characteristics of components with intricate profile geometries indicates that machining efficiency is strongly influenced by the complexity of part geometry and the diversity of manufacturing procedures when automated multifunctional equipment is applied. The final values of the “weight coefficients W” and “bases B” were determined, and the resulting matrix structures support compliance with the minimum Mean-Squared Error (MSE) criterion while increasing the reliability of predictive outcomes in evaluating production risk for mechanically engineered components and systems. Implications. The evaluation of machining-mode selection demonstrates that identifying optimal manufacturing conditions for components with sophisticated profile surfaces processed on automated systems remains a major engineering and economic challenge. Existing approaches for parametric optimisation insufficiently incorporate technological constraints. As the application of materials with specific physical and mechanical characteristics expands, along with the increasing geometric complexity of components and the wider implementation of multifunctional automated systems, technological production planning increasingly depends on the effective determination of cutting parameters and tool geometry, thereby contributing to improved manufacturing performance. Dr. Viktor Koval 2026-06-30 Article Article Peer-reviewed Article application/pdf https://ees-journal.com/index.php/journal/article/view/348 10.61954/2616-7107/2026.10.2-9 Economics Ecology Socium; Vol. 10 No. 2 (2026): Economics Ecology Socium; 124-135 Економіка Екологія Соціум; Том 10 № 2 (2026): Economics Ecology Socium; 124-135 2616-7107 2616-7107 10.61954/2616-7107/2026.10.2 en https://ees-journal.com/index.php/journal/article/view/348/300 Copyright (c) 2026 Economics Ecology Socium https://creativecommons.org/licenses/by-nc/4.0 |
| spellingShingle | Manufacturing Efficiency Mechanical Engineering Optimisation Production Risk Assessment. Petrova, Desislava Balabanova, Ivelina Georgiev, Georgi Lengerov, Angel Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title | Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title_alt | Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title_full | Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title_fullStr | Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title_full_unstemmed | Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title_short | Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components |
| title_sort | resource management and innovative optimisation of machining modes in the production of complex-shaped components |
| topic | Manufacturing Efficiency Mechanical Engineering Optimisation Production Risk Assessment. |
| topic_facet | Manufacturing Efficiency Mechanical Engineering Optimisation Production Risk Assessment. Manufacturing Efficiency Mechanical Engineering Optimisation Production Risk Assessment. |
| url | https://ees-journal.com/index.php/journal/article/view/348 |
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