=Paper=
{{Paper
|id=Vol-3896/short12
|storemode=property
|title=Software module for project analysis in mechanical processing and welding of frame structures
|pdfUrl=https://ceur-ws.org/Vol-3896/short12.pdf
|volume=Vol-3896
|authors=Volodymyr Shanaida,Valeriy Lazaryuk,Ruslan Skliarov
|dblpUrl=https://dblp.org/rec/conf/ittap/ShanaidaLS24
}}
==Software module for project analysis in mechanical processing and welding of frame structures==
https://ceur-ws.org/Vol-3896/short12.pdf
Software module for project analysis in
mechanical processing and welding of frame
structures
Volodymyr Shanaida1,∗,Valeriy Lazaryuk 1,† and Ruslan Skliarov 1,†
1
Ternopil Ivan Puluj National Technical University, Ruska str., 56, Ternopil, 46001, Ukraine
Abstract
A preliminary analysis of the technical project is crucial for aligning the expectations of all
participants. Developing and using software modules for operational planning shows promise. These
modules allow the project executor to assess the technological support required for the project and
determine whether other participants need to be involved in specific technological operations. It is
important to evaluate the effectiveness of using both primary and ancillary technological equipment,
as well as the availability of employees with the required qualifications. Evaluating the project
involves analyzing the geometric parameters of the product's components, which are obtained from
the results of 3D modeling. The proposed mathematical models for project analysis enable financial
assessment and help determine the overall time spent on the project. The software module analyzes
user input and can be adapted to changing production conditions.
Keywords
database, mathematical modeling, information systems, tooling, welding operation 1
1. Introduction
The modern production of industrial products relies on a functional and cost analysis for
project implementation. Typically, planning departments conduct a consolidated analysis of the
cost of order fulfillment, especially in mechanical engineering. However, this approach often
overlooks the specific needs of production. For industrial enterprises with a well-established
base of metal-cutting machines and other metal-processing equipment, this approach is more
justified. It allows for a more efficient use of equipment and concentration of operations. The
selection of qualified employees for performing technological operations also plays a crucial
role. However, industrial bases with low equipment usage tend to prefer technical projects with
significant differences in financial indicators for process organization and implementation
coverage.
For small and medium-sized businesses, using aggregated analysis, such as analyzing the
mass of finished products, may not always be justifiable. When production capacities are at their
1
ITTAP’2024: 4th International Workshop on Information Technologies: Theoretical and Applied Problems, October 23-
25, 2024, Ternopil, Ukraine, Opole, Poland
∗
Corresponding author.
†
These authors contributed equally.
shanayda_v@tntu.edu.ua (V. Shanaida); lazaryuk@gmail.com (V. Lazaryuk); kalibr2011@gmail.com (R. Skliarov)
0000-0002-9743-9110 (V. Shanaida); 0000-0003-3731-2828 (V. Lazaryuk); 0000-0001-6112-964X (R. Skliarov)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
maximum, it's important to consider factors like the time taken for each operation, the sequence
of technological operations, labor costs, potential risks, minimal material procurement costs,
and expenses for consumables and auxiliary materials. These factors carry more weight for
small businesses compared to larger ones, as they can have a bigger impact on the overall
financial performance of the business.
We have determined that it is crucial to develop autonomous functional modules for
technical and financial analysis. The importance of this issue is evident due to the specialization
of its operation, particularly its focus on executing a specific project. A formal description of the
project's elements enables a functional and cost analysis of each technological operation.
Consequently, this allows for accurate prediction of the project's cost and execution time.
2. Related works
Many countries have experienced significant economic changes due to the COVID-19
pandemic and russia's military invasion of Ukraine. As a result, manufacturing companies are
seeking to reduce costs to stay competitive during these crises. This includes optimizing
production facility maintenance costs. One way to achieve this is through effective maintenance
planning, integrated production and maintenance planning, and by improving machine
durability. This can be accomplished by redistributing the workload of production equipment
and ensuring equal load factors for technological equipment [1].
Effective production planning involves using a variety of input data indicators. These
indicators can be either typical or original, depending on the specific production conditions.
Advanced design systems incorporate planning modules based on Industry 4.0 principles. The
traditional hierarchical approach to production planning and control (PPC), as described by
Rahmani M. et al. [2], limits the use of production feedback data in tactical production planning.
Another important factor to consider is the cost of analyzing production processes. In some
cases, developers create functional modules to improve the productivity of PPC processes and
organize production more effectively. They utilize cutting-edge technologies, including the
Internet of Things, big data analytics tools, and machine learning running in the cloud or on
edge devices, as mentioned by Olumide E. et al. [3].
It is widely recognized that the planning of production processes is a crucial aspect of
organizational work within an enterprise. This stage is an essential step in decision-making
before the commencement of production activities. The primary objective of such planning is to
achieve maximum profit while minimizing production costs [4]. The planning of production
processes is defined as the process of producing goods and services over a specific period, taking
into account various resources such as labor, materials, and equipment. In many mathematical
models, the production planning model itself is developed based on multiple variables and
parameters, which can aid in making production decisions as efficiently as possible [2-4].
The production process planning system involves separate studies for each stage. These
studies are based on mathematical models for typical and non-typical algorithms [5], which has
attracted interest among specialists in developing, researching, and improving these models. For
instance, the task of forming the Master Calendar Plan, central to the MRP standard, is
presented as a linear programming task. This algorithm is chosen due to the linear nature of
specified limitations on production capacities and materials [6]. When there are strict
restrictions on production equipment capacity, the plan for replenishing raw materials is shifted
to earlier intervals in the planning, and only after that is based on restrictions on consumed
power and productivity of the technological equipment. Several strategies for scheduling
production replenishments are proposed [7]. The developed algorithms are available in the form
of Microsoft Excel templates for use to enhance understanding of the MRP II standard [6].
Considerable attention is devoted to the analysis methods used in algorithms for planning
production processes and implemented in mathematical models [7, 8]. Scientists have
investigated various algorithms for dealing with Advanced Planning and Scheduling (APS) in
the presence of uncertainty. These algorithms are classified into five main categories: stochastic
mathematical programming, fuzzy mathematical programming, modeling, metaheuristics, and
evidential reasoning. Based on advanced research trends and identified shortcomings, potential
research directions are discussed [8].
The software module proposed by us has been closely studied in the context of traditional
production planning issues [9]. The study used an Integer Linear Programming (ILP) model to
predict the monthly output of a batch of each product to minimize production costs in a plant.
The authors developed the model as a Python program and solved it using the simplex
algorithm. The model calculates the lowest monthly cost and number of batches by efficiently
utilizing both human and material resources. It also considers factors such as overtime costs,
periods of labor and machine downtime, and additional labor hours, all of which are added to the
monthly production costs [9]. The article describes effective solutions for improving traditional
production planning problems and minimizing production costs by considering multiple
constraints using operations research. The study accurately determines the difference between
monthly production costs and costs calculated using techniques of other common production
planning systems [9, 10].
After conducting preliminary analysis, it has been shown that proposed algorithms and
schemes for operational planning of production processes heavily rely on mathematical models
[4, 6, 7, 9]. Predictive mathematical modeling is a crucial and potent tool for enhancing
production processes [11], offering the best comprehension of principles when predicting the
possible execution options of the technological process. The authors delve into an examination
of the fundamental principles of mathematical modeling within the production process, the
mechanism for developing predictive models, and the exploration of challenges that arise during
the application of mathematical modeling [11].
The production process can be defined as the application of physical and/or chemical
processes to modify the structure, properties, and appearance of source materials in order to
produce parts or products [12]. Manufacturing often involves combining several elements to
create assembled products. When preparing for the release of new products, it is important to
establish a common understanding of production development, planning, and control, including
typical products like just-in-time and cost-effective production [12]. These indicators are crucial
for analyzing efficiency in production processes. Production efficiency is greatly affected by
maintenance, which is often overlooked when the focus is on production planning. The
symbiotic relationship between technical activities and production activities is a critically
important factor that is not taken into account when planning activities [13].
To effectively manage production processes with a rapidly changing product range, digital
production systems must be actively used. These systems allow for flexible adjustments to
production and service planning in response to both internal and external changes [14].
However, managing data generated by production systems and processes in dynamic
production environments requires the development of new methods and systems. It is
important to categorize information flows into technological (technical) and decision-making
flows [15].
After analyzing the existing literature, we found support for our proposal to develop a
specialized software module designed for the real-time analysis and cost calculation of specific
technical projects. This software module should be adaptable to various production conditions.
3. Proposed methodology
During the research, linear regression of the general type, non-linear regression of the
general type, 3D modeling, methods for forming a relativistic database and discrete set analysis
algorithms were used.
4. Results
We have conducted market research on software products for analyzing and planning
production processes. Individual automated design systems can incorporate autonomous or
integrated modules for planning production processes. To ensure their successful operation, it is
essential to acquire the CAD-CAM-CAE system itself and complement it with planning and
analysis modules. These systems have a complex structure and require a special level of training
for the system operator. In some cases, engineers and scientists have developed specialized
software modules for specific productions [6]. We identified the necessity to develop and
explore the functionality of a software module that could be adjusted to production conditions
across a wide range of products. Initially, a software module for analyzing machining processes
and welding technological operations was created [5]. Subsequently, mathematical models and
a structure for financial analysis and production process planning were developed.
Among other requirements for the software module were its versatility and ease of use.
Additionally, the software module for operational analysis should be accessible on a PC or
laptop for an average statistical user.
Over several months, we conducted consultations with representatives from manufacturing
companies. During these meetings, we identified a list of common issues that affect most
production systems during production planning and preparation. Representatives from the
companies provided suggestions for how to improve the organization of functional and
financial analysis of production preparation processes, as well as preliminary analysis of the
business plan.
Through discussions of various input data parameters and expected results, we determined
the most efficient structure for the functional components of the software module. Below is a list
of the minimum required functional blocks for these components:
1. Formation of a library of 3D models for each component included in the product's project
structure.
2. Save the geometric characteristics of the parameters of each component in the project
database.
3. Perform an initial assignment of technology operations for the mechanical processing of
each component in the product's project structure.
4. Calculate the manufacturing time for each component included in the product structure
from the project.
5. Calculate the minimum time required for producing the necessary number of parts
before commencing assembly operations.
6. To provide an estimate of the time spent on assembly operations.
7. Calculate the number of production sites for assembly, welding, mechanical cleaning,
surface protection, and painting operations.
8. Investigate the execution time of each technological operation and the approximate
duration of the entire project.
9. Determine the required quantity of raw materials for implementing construction
solutions.
10. Perform an analysis of the load level of the primary technological equipment based on
power parameters and usage time. Also, estimate the financial costs for operating and
maintaining such equipment.
11. Perform an analysis of the utilization level of the workshop's auxiliary and
transportation equipment based on power parameters and usage duration. Also,
calculate the projected financial costs for operating and maintaining such equipment.
12. Determine the required amount of supplies needed to carry out the technological
operations.
13. To estimate the expenses involved in paying the salaries of the key employees and
support staff.
14. To determine the expenses associated with covering administrative costs.
Based on the research findings and recommendations from individual researchers [7, 9, 14], it
is suggested to develop a software module using the open architecture principle. This approach
allows users to customize databases to suit their specific needs, modify standard data tables, and
edit mathematical expressions within the software module's mathematical model. The module's
unique feature is its ability to perform specific mathematical operations based on the structure
of the search or analysis request.
4.1. Analysis of the structure of the software module
All data used for analysis is stored in tables, which are a structural component of the
database. Some tables store records that are specific to a particular production (Figure 1). The
data from these tables can be used as input for other tables. Data is exchanged through a special
component called a "Form." Information from one table can be used to describe a production
object in another table.
Every entry in the software module's table is encrypted. This encryption method allows you
to use individual records from the same table to create different queries and reports. Generally,
the software module is presented to the user through a main control form, which includes
buttons for calling up commands, creating new orders, reviewing previous orders, and
monitoring the technical project implementation process.
The design option for the order formation control panel (Figure 2) shows a linear structure
with several buttons for managing the accounting process. One way to improve this structure is
to use a mechanism to substitute numerical or symbolic data when the customer has already
contacted the manufacturer for a service earlier.
Figure 1: the table "Characteristics of the equipment" to describe the production equipment set.
The software module allows users to access various forms from the main panel. When data is
recorded in this panel, the system automatically stores it in corresponding tables, and users can
also print the data from the form. If users need to fill out other forms, they can use the command
to return to the Main Panel.
Figure 2: one of the forms for placing an order.
Each working panel may contain control buttons for switching to other panels, returning to
the previous data entry level, and accessing other forms for placing an order. After placing the
order, users can print the results of the financial and technical analysis. At this stage, the
Agreement participants have the opportunity to review the results, make corrections, and
promptly receive updated results.
4.2. The characteristics of mathematical support of the
software module
The software module includes mathematical descriptions for calculating perimeters and
cross-sectional areas of profiles used in frame structures [5]. Various technological parameters,
such as the main time for mechanical processing or welding, as well as auxiliary time and time
for transporting products around the shop, were calculated using established algorithms.
During this stage, adjustments were made to the mathematical expressions, taking into account
the specific production conditions of a particular shop or section of the shop. These adjustments
included incorporating coefficients to increase or decrease the calculated values. In some cases,
it is most effective to create an array of values for a variable parameter. These values can be used
for forming requests, and calculating time and financial costs.
5. Conclusions
Assessing a project's financial and technical aspects, it is essential to establish a strong
foundation for collaboration between the client and the contractor. Researchers have focused on
analyzing methods in production planning algorithms and have incorporated them into
mathematical models across various software platforms.
One of the most influential factors in financial and production analysis is examining the 3D
model of each component within the project's structure. Leveraging 3D modeling tools provides
comprehensive information about the part's geometric parameters and other characteristics.
Calculating cost indicators for order fulfillment and financial expenses to support the
operation of main and auxiliary equipment involves using adaptable mathematical expressions
tailored to specific production conditions. Special requests are employed for these calculations,
comprising a set of mathematical models. The system automatically selects the appropriate
mathematical model based on predefined criteria and constraints.
The software module for analyzing machining processes, assembly operations, and welding
is designed around an open structure database. This approach enables users to modify its
mathematical support to suit their individual needs and production conditions.
The database structure implemented in the software module for analyzing machining
processes, assembly operations, and welding has proven its effectiveness when compared to
other analysis systems. This efficiency is achieved by adapting the mathematical support to the
specific production conditions, taking into account the enterprise's specific work characteristics.
The module's capacity for modifying the mathematical apparatus and its versatility have made it
an effective tool for preliminary project cost estimation and implementation timelines.
References
[1] Mota, B.; Faria, P.; Ramos, C., Joint Production and Maintenance Scheduling for Total
Cost and Machine Overload Reduction in Manufacturing: A Genetic Algorithm Approach. IEEE
Access, 2023, 11, 98070-98081.
[2] Rahmani, M.; Syversen, Ø.A.M.; Romsdal, A.; Sgarbossa, F.; Strandhagen, J.O. In
Advances in Production Management Systems. Production Management Systems for Responsible
Manufacturing, Service, and Logistics Futures. Alfnes, E.; Romsdal, A.; Strandhagen, J.O.; von
Cieminski, G.; Romero, D., Eds.; Springer Nature Switzerland: Cham, 2023, pp 779-792.
[3] Oluyisola, O.; Bhalla, S.; Sgarbossa, F.; Strandhagen, J.O., Designing and developing
smart production planning and control systems in the industry 4.0 era: a methodology and case
study. Journal of Intelligent Manufacturing, 2021, 33.
[4] Christefa, D.; Mawengkang, H.; Zarlis, M., Data-Driven Decision Making In Large Scale
Production Planning. SinkrOn, 2022, 7, 2068-2071.
[5] Shanaida, V.; Skliarov, R.; Lazaryuk, V. Mathematical models for the analysis of the
parameters of channels in the planning of mechanical processing and welding operationsIn
Proceedings ITTAP’2023: 3rd International Workshop on Information Technologies:
Theoretical and Applied Problems: Ternopil, Ukraine, Opole, Poland, 2023, pp 43-54.
[6] Novinskyi, V.; Popenko, V., Formalization of the master production shedule formation
task in the MRP II planning system. Radio Electronics, Computer Science, Control, 2024, 167-179.
[7] Naibaho, T., Production Planning Models (Approaches) from the Perspective of
Mathematical Sciences. Communications on Applied Nonlinear Analysis, 2024, 31, 18-28.
[8] Jamalnia, A.; Yang, J.-B.; Feili, A.; Xu, D.-L.; Jamali, G., Aggregate production planning
under uncertainty: a comprehensive literature survey and future research directions. The
International Journal of Advanced Manufacturing Technology, 2019, 102.
[9] Chaudhary, S., Enhancing production efficiency through integer linear programming-
based production planning. Journal of Engineering Issues and Solutions, 2024, 3, 63-75.
[10] Afolalu, A.; Matthew, B.; Ongbali, S.; Abdulkareem, A.; Emetere, M.; Iheanetu, O.,
Overview impact of planning in production of a manufacturing sector. IOP Conference Series:
Materials Science and Engineering, 2021, 1036, 012060.
[11] Tarasov, N.; Khamula, O. In VI International Scientific and Practical Conference
«GRUNDLAGEN DER MODERNEN WISSENSCHAFTLICHEN FORSCHUNG»: Zurich,
Switzerland, 2024, pp 199-206.
[12] Afriansyah, A.; Mohruni, A., Production Planning and Control System with Just in Time
and Lean Production: A Review. Journal of Mechanical Science and Engineering, 2021, 6, 019-027.
[13] Geurtsen, M.; Adan, I.; Atan, Z., Planning of multi-production line maintenance. Journal
of Manufacturing Systems, 2024, 75, 174-193.
[14] Braghirolli, L.; Mendes, L.; Engbers, H.; Leohold, S.; Triska, Y.; Silva, M.; Odebrecht de
Souza, R.; Frazzon, E.; Freitag, M., Improving production and maintenance planning with meta-
learning-based failure prediction. Journal of Manufacturing Systems, 2024, 75, 42-55.
[15] Skliarova, N.; Myskevych, V.; Skliarov, R.; Shanaida, V. Formuvannia informatsiinoi
systemy dlia obliku roboty pidpryiemstva [in Ukrainian].In Materials of the ⅩⅠ scientific and
technical conference "Information models, systems and technologies" [IMSTT]; TNTU: Ternopil,
, 2023, p 114.