Modern Production Research Articles

An elucidation of blended modeling from an industrial perspective

AbstractModel-Driven Engineering (MDE) has been widely adopted across various industrial sectors due to its ability to manage the complexity of modern engineering products. However, traditional modeling languages and tools are often limited to a single, specific concrete syntax, which poses challenges for the diverse stakeholders involved in the modeling process.. To address these limitations, the emerging field of blended modeling introduces the use of multiple concrete syntaxes, and in some cases, even multiple abstract syntaxes, for representing the same information. In this expert perspective, we present generalized, technology-agnostic concepts developed within a European research and development project focused on blended modeling. Specifically, we contribute a standardized terminology and ontology for blended modeling, along with a methodology for creating blended modeling environments. These concepts were developed through collaboration between academic and industrial partners, who aligned on the motivations and benefits of this approach. The insights gained from this project are not only relevant to blended MDE but also can be applied to traditional MDE practices.

MIMO ultra-local model-based adaptive enhanced model-free control using extremum-seeking for coupled mechatronic systems

Multiple degree-of-freedom (DOF) mechatronic systems, such as robots and robotic arms, play a crucial role in modern life and production. However, due to strong coupling, uncertain dynamics, and external disturbance, accurately modeling these systems is challenging, making traditional model-based control methods impractical. To address this, this paper proposes an extremum-seeking-based adaptive enhanced model-free control for multi-input multi-output (MIMO) mechatronic systems to realize robust trajectory tracking. Unlike previous model-free control methods that decouple and reorganize the MIMO system into several single-input single-output ultra-local models, this paper develops a MIMO ultra-local model with a non-diagonal gain matrix α to approximate the system dynamics within an ultra-short time window. Time-delay estimation (TDE), Proportional-Derivative (PD) control law and accuracy compensation compose an TDE-based enhanced intelligent PD control that ensures the closed-loop stability. Furthermore, an extremum-seeking (ES) technique is designed to optimize the gain matrix α to enhance control performance. The main contributions of this paper are the development of a model-free control framework based on the MIMO ultra-local model and the successful application of ES to optimize the non-diagonal gain matrix α. Stability analysis of the closed-loop system is conducted using Lyapunov theorem. Finally, numerical simulations on a 2-DOF robotic manipulator and co-simulation results on a 3-DOF PUMA 560 robotic manipulator validate the effectiveness and superiority of the proposed methods.

Cucumber productivity depending on the use of microbiological preparations

Relevance. In the modern production of greenhouse products, with hydroponic cultivation of crops, one of the safe ways to increase the quantity, quality of cucumber products and plant protection is the use of microbiological preparations.Methodology. The purpose of our work was to assess the effect of a complex of microbiological preparations on the productivity of cucumber hybrids in winter and spring turnover. The study was conducted in industrial greenhouses during 2021-2022. Long-fruited (Lohengrin F1) medium-fruited (Mewa F1 and Svyatogor F1) and short-fruited (Valigora F1) parthenocarpic cucumber hybrids were selected as research objects. The preparations were applied five times, at 30-day intervals throughout the entire growing period, the first application when planting plants in the production department. Measurements of economically valuable and chemical indicators were carried out weekly. Descriptive statistics and ANOVA methods were used for statistical data analysis.Results. As a result of the research, a positive stimulating effect of a complex of microbiological preparations on the accumulation of raw and dry biomass, an increase in yield and its quality in the studied cucumber hybrids was revealed. The final yield when applying microbiological preparations increased on average from 4.1 to 5.5 kg/m2 in all cucumber hybrids over the two years of study, while the yield of standard products in the total crop structure increased by 2.7% - 6.4%.

A Diagnostic Approach to Improving the Energy Efficiency of Production Processes—2E-DAmIcS Methodology

This article presents the issue of energy waste in manufacturing processes, focusing on reducing unnecessary energy consumption and CO2 emissions. A significant challenge in modern production is identifying and minimizing energy waste, which not only increases operational costs but also contributes to environmental degradation. An improvement methodology referred to as 2E-DAmIcS is proposed. A distinguishing feature of the methodology is a risk map of energy waste in the production process. Application of the methodology is demonstrated using the example of a lead–acid battery production process. It is shown that even small but well-diagnosed changes to the process make it possible to significantly reduce energy consumption. The proposed methodology offers practical tools for managers and decision-makers in various industries to systematically identify and minimize energy waste. It highlights the importance of cross-disciplinary collaboration among specialists in technology, energy consumption, and statistical analysis to optimize energy use. By applying this approach, companies can achieve both financial savings and environmental benefits, contributing to more sustainable production practices.

Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling

This study addresses the usage of data from industrial plate mills to calculate the mean flow stress of different steel grades. Accurate flow stress values may optimize rolling technology, but the existing literature often provides coefficients like those in the Hensel–Spittel equation for a limited number of steel grades, whereas in modern production, the chemical composition may vary by thickness, customer requirements, and economic factors, making it necessary to conduct costly and labor-intensive laboratory studies. This research demonstrates that leveraging data from industrial rolling mills and employing machine learning (ML) methods can predict material rheological behavior without extensive laboratory research. Two modeling approaches are employed: Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. The model comprising one GRU layer and two fully connected layers, each containing 32 neurons, yields the best performance, achieving a Root Mean Squared Error (RMSE) of 7.5 MPa for the predicted flow stress of three steel grades in the validation set.

MODELS OF CORRELATION FUNCTIONS OF NONSTATIONARY PROCESSES AND SEQUENCES FOR TECHNOLOGICAL SYSTEMS

Precision technological processes for the production of modern microelectronic products require compliance with the quality of source materials, working environments, and precise adherence to regimes. Due to natural fluctuations in the properties of materials and the environment, and variable states of all technological processes, the parameters of the product and technological processes cannot be described by deterministic laws. Due to the inevitable natural properties of fluctuations in the parameters of technological equipment and its operating modes, the state variables of all technological processes are random functions of space-time coordinates. In most cases, these accidents cannot be neglected, since they all affect the output parameters of the products. The most complex mathematical models of technological systems and processes in modern theory are random spatiotemporal fields, representing both input and output characteristics, as well as parameters of the systems under consideration. The purpose of this work is to model real-valued values of correlation functions of non-stationary random processes and sequences. When constructing a correlation theory of random processes and sequences, a complex representation is widely used, i.e. random functions of the form are considered: continuous or discrete time. This approach made it possible to construct a correlation theory of nonstationary random functions using the spectral theory of non-self-adjoint or unitary operators and to introduce the concept of complex spectrum. For applications of the correlation theory of nonstationary random functions and their modeling, it is convenient to deal with real-valued correlation functions. The construction of real-valued correlation functions can be carried out using the well-known fact that the real part of complex-valued correlation functions is also a correlation function (for the imaginary part this statement is unfair, since the imaginary part is a cross-correlation function of the real and imaginary parts of the corresponding random process or sequence) . The resulting models of correlation functions of non-stationary random processes and sequences can be used to construct algorithms for forecasting and filtering non-stationary random functions

Redefine manufacturing operations for modern production environments with the help of artificial intelligence enterprise information systems

Redefine manufacturing operations for modern production environments with the help of artificial intelligence enterprise information systems

Machine learning-driven prediction of tensile strength in 3D-printed PLA parts

Additive manufacturing (AM) has become a transformative technology in modern production, enabling complex geometric designs with minimal material waste. A significant aspect of AM, particularly in fused deposition modeling (FDM), is the need for precise prediction of mechanical properties, such as ultimate tensile strength (UTS), which is crucial for industrial applications. This study examines whether simple machine learning (ML) algorithms can accurately predict the UTS of 3D-printed polylactic acid (PLA) parts, and evaluates the effectiveness of ML techniques, especially ensemble methods, in enhancing prediction accuracy. To this end, the study compares simple ML algorithms to identify the most accurate model for predicting the UTS of 3D-printed PLA parts. Subsequently, an average ensemble technique combines four ML algorithms, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and light gradient boosting machine (LGBM), to predict UTS. In this technique, the average predicted UTS values of CatBoost, XGBoost, GBM, and LGBM are taken as the final predicted UTS value. Additionally, 11 ensemble configurations of these algorithms are analyzed to determine the optimized ensemble configuration. The results show that the CatBoost algorithm, with an R2 of 94.46%, achieved the highest predictive accuracy among individual ML algorithms. Moreover, the CatBoost-XGBoost-GBM-LGBM ensemble was the most accurate configuration, achieving an R2 of 98.05% with less than 10% error in predicting 37 external data points not included in the training and testing sets. This study advances predictive modeling in AM by demonstrating that ML, particularly ensemble techniques, can reliably predict material properties, paving the way for more robust applications of AM in industry

Building a Nest in Human-Built Spaces

This article seeks to understand how Finnish sows experienced human-built spaces in the first decades of the 1900s, during which commercial and modern swine production methods slowly began to transform their lives and living conditions. I approach the question of sows’ experiences by focusing on a particular behavior of sows (nest building) and how they performed it in modern piggeries. First, I outline the different features of farrowing pens, which were specifically designed for sows and their piglets and which were, consequently, the most common spaces sows experienced during nest building. Second, I analyze sows’ nest-building behavior in pens and how different spatial features and husbandry practices shaped its execution. I suggest that nest-building behavior can be seen as an example of sows’ own place-making processes and how they create meaningful places of their own in given circumstances. At the same time, these processes convey sows’ experiences and their own role in the production of those circumstances. Broadly, this article suggests that the analysis of animal behaviors at the intersection of spaces, materials, and practices creates possibilities to interpret animal experiences and how they have navigated their lives in specific historical circumstances.

Slow-Release Nitrogen Fertilizer Promotes the Bacterial Diversity to Drive Soil Multifunctionality

The application of slow-release nitrogen fertilizer not only economizes labor input, but also decreases the frequency of use of mechanical intakes, with significant implications in advancing modern intensive agricultural production. Whether slow-release nitrogen fertilizer application can influence the association between microbial diversity and soil multifunctionality remains controversial. This study analyzed the spatial variances of soil environmental factors, soil multifunctionality, and their correlations with bacterial and fungal communities under five nitrogen application rates. The key factors influencing the dominant microbial species and community structures at different spatial locations were determined by the slow-release nitrogen fertilizer application rate, and the driving factors and dominant species of soil multifunctionality were identified. In contrast to the control group, moderate slow-release nitrogen fertilizer application enhanced soil multifunctionality and ameliorated the resilience of microbial diversity loss at diverse spatial locations resulting from irrational nitrogen fertilizer application. The resilience of the fungal community to disturbances caused by fertilization was lower than that of the bacterial community. Bacterial diversity exhibited a significant correlation with soil multifunctionality, and the soil multifunctionality intensity under 240 kg ha−1 treatment increased by 159.01% compared to the CK. The main dominant bacterial communities and the dominant fungal community Ascomycota affected soil multifunctionality through slow-release nitrogen fertilizer application. Structural equation modeling and random forest analysis demonstrated that bacterial community diversity, particularly in bulk soil and the rhizosphere, community composition, and soil nitrogen form are the primary driving factors of soil multifunctionality. Results indicated that the microbial niche alterations induced by slow-release nitrogen fertilizer application positively affect soil multifunctionality.

Nanotechnology in Cosmetics: An Overview

Nanotechnology is revolutionizing the treatment of skin diseases in cosmetics, offering safe and targeted delivery of active medications and cosmetic ingredients. The use of carrier systems in nanotechnology improves skin penetration and sustained drug action. Formulators are using technology exclusive to cosmetic products, such as vesicular, particulate systems, emulsions, nano capsules, nanotubes, nanocrystals, and dendrimers. Nanomaterials are also used in hair care products and nail formulations. Nanomaterials are used in modern cosmetic products, particularly in sunscreens due to their ability to protect the skin from harmful UV radiation which is carcinogenic. This review explores nanotechnologies in the cosmetic industry and their potential as next- generation smarter carrier systems. It highlights the advances in nano cosmeceuticals and the applications of nanotechnology in cosmetics. It also focuses on the regulations of nanotechnology in cosmetics and describes various routes of exposure to nano particles in the human body. Keywords: Nanotechnology, cosmetics, nanoparticles, regulations, advances

The Convergence of SAP PP, RPA, and IBP: A Comprehensive Analysis of Integration Benefits and Challenges in Modern Manufacturing

The integration of SAP Production Planning (PP) with Robotic Process Automation (RPA) and SAP Integrated Business Planning (IBP) represents a significant advancement in manufacturing and supply chain management. This article examines the synergies, challenges, and potential benefits of combining these technologies in modern production environments. Through a comprehensive analysis of existing literature, case studies, and industry best practices, we propose a framework for seamless integration that enhances operational efficiency, improves forecast accuracy, and enables real-time decision-making. Our findings reveal that successfully implementing this integrated approach can substantially improve production planning, resource allocation, and overall supply chain performance. However, organizations face notable challenges regarding technical complexity, data synchronization, and change management. This article contributes to the growing body of knowledge on digital transformation in manufacturing by providing actionable insights and recommendations for practitioners. Furthermore, it identifies key areas for future research, including the role of artificial intelligence and machine learning in further optimizing these integrated systems.

Energy budget and economic analysis of modern and traditional wheat production systems in Kurdistan province of Iran

This study was conducted to analyze input-output energies and economic analysis of modern and traditional wheat production systems of Kurdistan province, Iran. Data were collected from 100 dryland and 100 irrigated wheat fields. The fields were selected randomly, and inquiries conducted in a face-to-face interview from May up to August next year. The results showed that total energy inputs, energy use efficiency, and specific energy were 49,956 MJ ha-1, 2.4, and 4.9 MJ kg-1 in modern irrigated wheat system, respectively, 19,064 MJ ha-1, 3.4, and 3.4 MJ kg-1 in traditional irrigated wheat system, 16,598 MJ ha-1, 1.7, and 6.7 MJ kg-1 in modern dryland wheat system, and 14,471 MJ ha-1, 1.99 and 5.75 MJ kg-1 in traditional dryland wheat system, respectively. The economic analysis revealed that the total cost of production and net return were 546.5 and 1,448.6 USD ha-1 in modern irrigated wheat, 206.4 and 844.7 USD ha-1 in traditional irrigated wheat, 230.5 and 237.6 USD ha-1 in modern dryland wheat and 185.2 and 266.1 USD ha-1 in traditional dryland wheat systems, respectively. The results of this study showed that the modern wheat production system uses more energy compared to that by the traditional wheat production system. Thus, the wheat producers are advised to adopt modern wheat production systems with some caution.

Experimental Investigations of the Influence of Spent Coffee Grounds Content on PLA Based Composite for 3D Printing

Nowadays Fused Deposition Modeling, a widely utilized additive manufacturing technology, is significantly transforming as modern production processes. Beyond basic uses to it role in sustainability, Fused Deposition Modeling offers processing potential for implanting circular economy by reducing virgin materials consumption and enhance the integration of waste food for sustainable 3D printing. This research paper investigated the production of new composite materials based on spent coffee grounds. In addition, PLA and SCG at various contents (0, 3, 5, 10, and 15 wt%) were dried and premixed, then processed into PLA/SCG composite pellets using twin-screw extrusion. These pellets were successfully converted into filaments and subsequently used for 3D printing. The effect of spent coffee grounds in PLA composites was investigated via physical and mechanical analysis of 3D printed samples. Regarding density measurements, results revealed that adding up to 5 wt% of spent coffee grounds increased the density while further additions led to a decrease which due to the printing parameters such as extrusion temperature and nozzle diameter. Considering the mechanical properties, the Young’s modulus increased once the spent coffee grounds content reached 3 wt% and then decreased. In the other hand, there was no enhancement in tensile strength and elongation at break which corroborating with density measurements. This mainly contributed to the changes in mechanical properties caused by printing parameters. This study demonstrates that coffee waste can be used as a filler in environmentally friendly composites for 3D printing, with a maximum SCG content of 15 wt%. This approach not only promotes the reuse of coffee waste but also reduces the cost of traditional PLA filaments.

From Frozen Ponds to Organized Competitions: The Growth of Skating and Ice Hockey in Korea, 1886–1938

The encounter of American Protestant evangelicalism and Japanese imperialism formed in Korean society in the late 19th and early 20th centuries became a steppingstone for the acceptance of modern winter sports in Korea. In particular, skates introduced by American Protestant missionaries and the Young Men’s Christian Association formed an imaginary space to counter Japanese imperialism in Korea during Japanese colonial era. Ice hockey introduced along with skating is a representative product that evolved in this process. The history of the introduction of American ice hockey to Korea also had a dual imperial influence between the United States and Japan, and in another direction, there was a voluntary acceptance process by Koreans who recognized ice hockey as a modern product.

EXPLORING CERAMIC REFRACTORY MATERIALS: CLASSIFICATION AND TECHNOLOGICAL INNOVATIONS

The article explores the primary types of ceramic refractory materials, focusing on their properties and applications in high-temperature industrial processes. Key technological advancements in refractory manufacturing are discussed, with an emphasis on enhancing material strength, chemical resistance, and durability. The analysis highlights the specific characteristics of each refractory type, including fireclay, magnesite, corundum, and silicon carbide, and their utilization across various industries such as metallurgy, energy, and glass production. Modern production and modification methods for refractories are presented, aimed at improving thermal resistance and reducing the operational costs of equipment.

The Effect of Digital Marketing-Upgrading and Product Virality on the Sustainability of MSMEs

This study aims to understand the role of consumer behavior in mediating the influence of virality, digital marketing, and product enhancement on the desires of MSMEs in East Java. This study expands our understanding of the impact of digital marketing, product enhancement, and virality on the desires of MSMEs in East Java, focusing on diverse consumer behavior. With a quantitative approach, this study uses a survey method with a questionnaire as the main instrument, involving 160 respondents from various cities in East Java, including Surabaya, Malang, and Mojokerto. The variables explained include digital marketing (X1) consisting of social media indicators, websites, and mobile marketing; product enhancement (X2) such as product quality and innovation; and virality (X3) which includes content creativity and the use of influencers. Consumer behavior (Y) focuses on satisfaction, loyalty, and interest, as well as MSME desires (Z) which are measured through profitability and public acceptance. The results of the analysis show that digital marketing has a positive effect on consumer behavior, which has an impact on increasing customer loyalty and satisfaction. In addition, product enhancement and virality significantly affect the desire of MSMEs, with consumers more interested in modern and popular products.

Crispy squid product development (Case Study: PT. Cupa-Cupa Snack) Pangandaran Regency

PT Cupa-Cupa Snack is a company in Pangandaran that processes fishery products into snack products, one of which is crispy squid. The number of crispy squid producers in Indonesia is a challenge for companies to develop products to produce innovations that suit market needs and maintain competitiveness. The purpose of this study was to analyze the product development carried out by the cupa-cupa snack company. The study was conducted from August to October 2024. The research method used was the survey method, with interview techniques and observations of crispy squid products that were analyzed descriptively and qualitatively. The results showed that the company has developed in various aspects, such as the addition of raw material suppliers from various regions, the use of faster and more modern production equipment and processes, changing the formulation of non-msg additives that are safe for consumption, using quality aluminum foil packaging, increasing flavor variants, and business-to-business marketing that maintains good and consistent digital presence techniques on various social media, such as Instagram, Facebook, and TikTok, to increase market share and company competitiveness.

AN INNOVATIVE APPROACH TO THE SOCIAL MANAGEMENT OF FILM PRODUCTION: THE ROLE INSTALLATION METHOD

The article analyzes the current state of film production in Russia. It considers changes in the domestic film market, including government support, increased production volumes, and the development of online platforms. It notes problems associated with increased production costs and lack of time to prepare for a role. The paper presents an innovative approach called “role installation” for managing the preparation of actors for filming and shows its possibilities in the context of modern mass film production. The method is aimed at quickly and effectively immersing the actor in the role, taking into account time constraints and directors’ requirements. The author emphasizes the importance of a sociological approach in film production, which helps producers, actors, and directors understand the context and social atom of the images they create. The article touches upon the issues of transforming acting education and the position of cinematography in shaping public consciousness. The “role installation” method can become an effective tool in solving modern problems of film production, motivation, and joint project management, contributing to the creation of high-quality and socially significant content.

Research on tomato disease image recognition method based on DeiT

Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production.