Optimization Of Production Process Research Articles

Investigations into temperature measurement in a laser-based heating process of optical, machined components

The manufacturing of optical components is subject to constant efforts to optimise production processes in order to achieve high surface qualities under the most economical conditions possible. This includes the refinement of existing technologies or development of completely new production technologies. One possible approach is the combination of conventional machining processes for optical components like diamond turning, grinding or polishing with laser-based processes to thermally influence the surface for improved machining or surface properties. For this, knowledge of the thermal interactions of the laser on the component surface is needed, which in turn requires its metrological acquisition. In this work, measurements were carried out using various methods for the controlled heating of glass surfaces by an infrared laser (λ=1070 nm). Among other things, a clear correlation between the samples surface roughness and the laser absorption is found.

Ecosystem approach to agricultural industry: Integration of participants and optimization of production

The integration of ecosystem business into the development of the agro-industrial complex of the digital economy represents an important stage in improving the production and economic efficiency of agriculture. The development of digital technologies in agriculture, such as agricultural drones, artificial intelligence systems and the Internet of things, opens up new opportunities for optimizing production processes, managing resources and improving product quality. An ecosystem approach to business in agriculture allows us to unite various participants in the agro- industrial complex - from farmers and equipment manufacturers to scientific institutions and financial organizations - into a single network that facilitates the exchange of information, optimization of production and the creation of innovation. This helps to increase the efficiency of resource use, reduce costs and improve the competitiveness of the entire agricultural sector. A key element of successful implementation of the ecosystem approach is the development of digital infrastructure in rural areas, including access to broadband Internet and digital educational resources. This will enable rural residents to have access to modern technology, training and information, thereby improving their productivity and quality of life.

Thermodynamic assessment and production process optimisation of waste cooking oil for sustainable biodiesel production

Thermodynamic assessment and production process optimisation of waste cooking oil for sustainable biodiesel production

Smart manufacturing system based on industrial internet of things

The primary challenge in the manufacturing industry today is the subpar productivity and efficiency in production processes. This predicament can be attributed to the widespread utilization of suboptimal production systems and the underutilization of advanced technology in manufacturing. Furthermore, the lack of accurate production data, limited employee involvement in production processes, and the high costs associated with production and machine maintenance have emerged as significant concerns in the contemporary manufacturing landscape. In response to these challenges, the adoption of Smart Manufacturing Systems (SMS) based on the Industrial Internet of Things (IIoT) has become crucial to address these issues andenhance overall production quality and efficiency. The SMS IIoT system empowers companies to gather real-time data from various industrial devices and machines. This data is subsequently analyzed to optimize production processes, resulting in increased efficiency and productivity. With this system in place, companies can closely monitor production processes in real-time and promptly detect and address defective or non-compliant products at the onset of production. The research results show an availability value of 88%, indicating that the machine operates 88% of the scheduled time. A Performance value of 100% indicates that the engine is operating at the desired maximum speed. OEE is calculated as the product of Availability, Performance, and Quality of Rate. With a value of 88%, OEE reflects the overall level of effectiveness of the machine in the production process. This paper highlights the significance of IIoT-based Smart Manufacturing Systems in addressing the prevailing challenges encountered by the manufacturing industry.

Performance Analysis of Object Placement Systems Based on Siemens S7-1200 its’ Evaluation of Effectiveness and Accuracy

The evolution of industrial automation systems has led to innovative solutions for efficient and precise object manipulation. This study introduces a groundbreaking "Object Placement System Based on Siemens S7-1200," designed to enhance object positioning tasks in manufacturing environments. The system's core control unit is the Siemens S7-1200 Programmable Logic Controller (PLC), seamlessly integrated with a network of sensors and actuators for coordinated operations. Operators can easily define target positions and parameters for object placement through a user-friendly human-machine interface. The PLC facilitates real-time data exchange with sensors, enabling accurate object detection and adjustments during placement. The system's implementation involves meticulous PLC programming, sensor integration, and interface design. Experimental results validate its capability to achieve precise and repeatable object placements. The Siemens S7-1200 is a versatile PLC supporting controlled positioning of electrically driven axes and technology objects, tailored for small automation systems requiring simple or advanced Human-Machine Interface (HMI and communication capabilities. The object placement system leverages the capabilities of the S7-1200 PLC for integrated sensor and actuator coordination, ensuring accuracy and repeatability. The study concludes that the innovative system utilizing the Siemens S7-1200 PLC underscores its potential for optimizing production processes and enhancing operational efficiency in industrial automation systems. The demonstrated ability to achieve precise and repeatable object placements highlights the viability of the Siemens S7-1200 platform for advancing object manipulation in industrial settings. The S7-1200 system manual provides specific information about the operation and programming of the PLC. Additionally, the system is supported by the TIA Portal programming software, which offers flexibility and ease of use.

Research of relationships between the technical parameters of industrial manipulators

Industrial manipulators have to become an integral part of modern construction production, playing a key role in the automation and optimization of production processes. When designing and using industrial manipulators, it is important to know the various parameters that interact with each other and affect the overall productivity and efficiency of the production line in which industrial manipulators operate. In the design of the robot process and construction production, it is necessary to know the technical requirements that need to be met by the robot system. Therefore, when designing industrial robots and manipulators, it is necessary to take into account the shape, dimensions, and weight of the building structures with which the industrial robot will work, as well as their physical and mechanical properties. These parameters will determine the general scheme and layout of the robot's components, the design of the clamping device and clamping force, the type of drive and its control principle. This paper is devoted to the identification of regularities between the main technical parameters of reach and lifting capacity of general-purpose industrial manipulators. Regression analysis with the use of hyperbolic and cubic splines is applied. The regression equations were solve based on the minimization of the total squared deviation of the correlation function.
 The results of the work will be useful for developers of industrial robots and researchers who are engaged in the development of robotic technological lines and productions.

Differential geometry approach in optimization of the business production process

In this work we show a new approach to the optimization of the production process – from a differential geometry point of view. It is known ([2]) analytical conditions of profit maximization and minimization of the cost in an enterprise. In the first part of this work, we show such a classical approach. In the second part of the work, we use geometrical methods to obtain a new geometrical approach to the production process.

Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

Modular chemical process simulators are widespread in chemical industries to design and optimize production processes with sufficient accuracy. However, convergence issues and entrapment in local optima during process optimization are still challenges to overcome. To circumvent them, surrogate models of first principles simulations have attracted attention as they are easier to handle, with hybrid surrogates combining data-driven surrogate models with mechanistic equations becoming particularly appealing. In this context, this work explores the use of Bayesian symbolic regression to construct and globally optimize hybrid analytical surrogate models of process flowsheets, where some units are approximated with tailored analytical expressions rather than with neural networks or Gaussian processes, which might be harder to globally optimize. Comparing with other prevalent black-box surrogate modeling & optimization approaches, such as kriging and Bayesian optimization, we find that our approach can find better solutions than those identified with pure black-box methodologies, yet model building is much more computationally demanding.

ANALYSIS OF FACTORS AFFECTING INNOVATIVE WORK BEHAVIOR IN THE DIGITAL ERA: CASE STUDY OF MANUFACTURING COMPANIES IN INDONESIA

climate on innovative work behavior in manufacturing companies in Indonesia. This research uses a literature and field design with a causal associative approach. This study uses a research instrument test consisting of validity and reliability tests. The quantitative analysis consists of a normality test, regression test, hypothesis test, correlation test, and coefficient of determination. This research concludes that transformational leadership, self-efficacy, and organizational climate have a positive and significant effect on innovative work behavior. In the ever-growing digital era, transformational leadership is important for manufacturing companies to face challenges and take advantage of emerging opportunities. In the digital era, self-efficacy helps employees cope with uncertainty and change, turn challenges into opportunities, and view technological change as a foundation for personal and professional growth. Furthermore, by adopting a pro-innovation organizational climate, manufacturing companies can more easily adapt to technological changes, optimize production processes, and exploit the full potential of the industrial revolution 4.0.

Decision making in operator-machine assignment problems: an optimization approach in U-shaped production lines

Decision-making is a complex problem in all fields of production, because decision makers have to find the most suitable solution alternative among the numerous, while the solution of the problem is limited by multiple, usually conflicting constraints. In the Industry 4.0 era, one of the most widely used production lines are the U-shaped production lines, which focus on the elimination of waste through a high utilization of workers. It means, that worker-machine assignment plays a significant role in the optimization of production processes in a U-shaped production line. The current investigation proposes a novel absorbing Markov chain (AMC) optimization approach to support the decision making regarding worker selection and assignment. The proposed approach integrates the AMC and the performance analysis of the production process. The numerical results validated the decision making model and showed that optimal worker-machine assignment can lead to about 20% production cost reduction, while the key performance indicators (KPI) of the U-shaped production line are significantly increased. The sensitivity analysis of influencing factors made it possible to identify the critical factors of this decision making problem, such as the qualification of operators, time of technological and logistics operations, and maintenance policy.

Optimizing production scheduling with the spotted hyena algorithm: A novel approach to the flow shop problem

The spotted hyena optimization algorithm (SHOA) is a novel approach for solving the flow shop-scheduling problem in manufacturing and production settings. The motivation behind SHOA is to simulate the social dynamics and problem-solving behaviors of spotted hyena packs in order to identify and implement optimal schedules for jobs in a flow shop environment. This approach is unique compared to other optimization algorithms such as WOA, GWO, and BA. Through extensive experimentation, SHOA has been shown to outperform traditional algorithms in terms of solution quality and convergence speed. The purpose of this study is to present the details of the SHOA algorithm, demonstrate its effectiveness, and compare its performance with other optimization approaches. The method used in this study includes extensive experimentation and comparison with other algorithms. The findings of this study show that SHOA is a promising tool for optimizing production processes and increasing efficiency. The implications of this study are that SHOA can be used as an effective tool for solving flow shop-scheduling problems in manufacturing and production settings.

Analysis of Management in Japan's Cultural and Creative Industry Based on Case Study Method

The Japanese cultural and creative industry has achieved immense success and influence globally, with its unique forms such as animation, manga, and games. However, rapid industry development has also brought about a series of challenges. This paper focuses on the Japanese cultural and creative industry, utilizing a case study approach to explore challenges including scheduling and contract issues, shortage of human resources, business practices, and introduces the strategies Japan is implementing to address these issues. These strategies involve enhancing contract standards and legal protections, cultivating and attracting talent, optimizing production processes, improving work environments, strengthening international collaboration, and fostering business professionals. These strategies aim to enhance the industry's competitiveness and innovation, shedding light on its current state and future directions. As technology advances and international collaboration deepens, Japan's cultural and creative industry will continue to showcase its uniqueness and inject new vitality into cultural heritage and economic growth.

Competency Development in Industrial Engineering and Related Academic Programs Regarding the Optimization of Production and Logistics Processes: The Case of Medellín

This study examines the correlation between the skills and competencies developed in engineering programs and those required by the most representative economic sectors in Medellín for optimizing their production and logistics processes. In general terms, this paper aims to contribute to the research on higher education institutions and the alignment of their programs with the expectations of businesses in emerging countries. Thus, the proposed research question is as follows: What is the systemic relationship between academia and businesses in Colombia regarding the development of skills and competencies related to the optimization of production and logistics processes? To address this question, the study methodology starts by defining the target population (Industrial Engineering and related academic programs) and the dialogue methods adopted. Subsequently, a morphological space is created to establish systemic interconnections among the study variables and factors. Finally, patterns and qualitative/quantitative relationships are identified and grouped into two drivers that support the optimization of production and logistics processes. These drivers, in turn, consist of five key driving variables: quality defects, work environment, information and communication technology usage, operation traceability, and logistics and planning. The results indicate a correlation of 60% between courses and job competencies, 85.71% between academic skills and job competencies, and 71.88% between courses and academic skills. These findings provide insights to enhance cooperation between the academic and business sectors and foster the development of new local competitive capacities.

Hydrogen Fuel Cell Integration: Automotive, Residential Power Generation & Portable Devices

Hydrogen fuel has emerged as a promising element in the quest for sustainable energy solutions across diverse sectors. This abstract distils the findings and insights from extensive research into hydrogen fuel integration, focusing on its applications in the automotive industry, residential power generation, and portable devices. In the automotive sector, hydrogen fuel cell vehicles (FCVs) offer a zero-emission alternative with extended driving ranges and rapid refuelling times, addressing some of the limitations of battery electric vehicles (BEVs). However, the successful integration of hydrogen as a transportation fuel hinges on the development of a robust refuelling infrastructure, cost reductions in fuel cell technology, and the optimization of energy-intensive hydrogen production processes. In residential power generation, hydrogen shows promise as a versatile energy carrier, capable of powering homes through fuel cells or combined heat and power (CHP) systems. To ensure sustainable residential hydrogen power, research efforts should continue to focus on efficient hydrogen production methods, such as advanced electrolysis powered by renewable energy sources. Furthermore, the integration of hydrogen in portable devices, including backup power systems and mobile electronics, is being explored. Overcoming challenges related to safe hydrogen storage, efficient energy conversion, and addressing energy density limitations are critical for expanding the utility of hydrogen in this domain. To drive further advancements, key research directions include the development of lightweight and high-capacity hydrogen storage materials, improvements in energy conversion efficiency, and enhanced safety measures for hydrogen-powered portable devices. Moreover, cross-sector collaboration between automotive, residential, and portable device applications is vital to optimize hydrogen production and utilization. Regulatory support and incentives play a pivotal role in promoting the adoption of hydrogen technologies, along with comprehensive life-cycle assessments to quantify their environmental impact.

MONITORING THE ORGANIZATION'S RECEIVABLES WITHIN THE FRAMEWORK OF CONTROLLING

Increasing the competitiveness of manufacturers of industrial products based on controlling is an important strategic task for many companies. Controlling is a management system that allows you to collect, analyze and use information about financial, operational and other performance indicators of an enterprise. One of the main reasons for introducing controlling is the need to make smart decisions based on reliable and up-to-date information. Controlling allows you to get a complete picture of the current state of the enterprise, identify potential problems and opportunities, and also predict future development. The use of controlling leads to optimization of production processes and increased efficiency in the use of resources. By analyzing production data, companies can identify bottlenecks in the production chain and take action to eliminate them.
 This allows you to increase productivity and product quality, as well as reduce costs. The article shows that the introduction of controlling is an important step to increase the competitiveness of manufacturers of industrial products. An algorithm for decision-making within the framework of controlling accounts receivable is presented, with signal points being formed. This allows companies to make informed decisions based on reliable information and optimize production and management processes.

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

AbstractBattery cell production is a key contributor to achieving a net‐zero future. A comprehensive understanding of the various process steps and their interdependencies is essential for speeding up the commercialization of newly developed materials and optimizing production processes. While several approaches have been employed to analyze and understand the complexity of the process chain and its interdependencies – ranging from expert‐ and simulation‐based to data‐driven – the latter holds significant potential for real‐time application. This is particularly relevant for inline process control and optimization. To streamline the development and implementation of data‐driven models, a holistic framework that encompasses all necessary steps – from identification of relevant parameters and generation of data to development of models – is imperative. This article aims to address this objective by presenting a comprehensive and systematic methodology, demonstrated for efficient cross‐process analysis in electrode manufacturing. Through the combined utilization of design of experiments methods, data‐driven models, and explainable machine learning methods, the interdependencies between production parameters and the physical, mechanical, and electrochemical characteristics of the electrodes were uncovered. These actionable insights are essential for enabling informed decision‐making, facilitating the selection of appropriate process parameters, and ultimately optimizing the production process.

The essence of information as an independent economic category

In a market economy, it is customary to distinguish four main factors of production: labor, capital, land and entrepreneurial talent (or entrepreneurial abilities). But it should be noted that modern production is unthinkable without the functioning of the fifth factor — information. The question of the relevance of the research lies in the ambiguity and evolutionism of the category «information». Not only is there no single definition of a category, but the essence of this definition is not always precisely defined. It is worth noting that today the issue of separating information from knowledge in the context of economic theory is also interesting. The author presents a historical structural and essential analysis of the category «information» in the conditions of digitalization and informatization of the current economic system. The necessity of distinguishing this category into an independent production factor determining the implementation of management and investment decisions is noted. The article presents the author's proposals, which have elements of novelty, on the analysis of the influence of information as a production factor on the efficiency of production processes. Taking into account this factor will help the organization to optimize production processes, improve product quality, reduce costs and increase competitiveness. The proposed directions will allow structuring and analyzing data, developing improvement strategies, implementing changes and monitoring their results, so that the company can constantly improve production processes and be more successful in the market. The article will be of interest to theoretical specialists for the formation of a new reproductive system in modern economic theory, which is transformed under the influence of internal and external factors, as well as to practitioners engaged in the optimization of production processes and resource planning.

Assessment of the Production Process Organization in the Metal Industry - Case Study

Abstract The article provides a detailed case study illustrating the practical application of BOST surveys while focusing on the third principle of Toyota. This particular principle underscores the importance of achieving an even distribution in production to mitigate fluctuations and ensure the smooth functioning of the production process. The article delves into a comprehensive analysis of how this principle is implemented in real-world scenarios, exploring its effects on production efficiency and elucidating the advantages gained from the elimination of downtime and the prevention of excessive inventory buildup. Moreover, the article offers a discussion on examples of applications across various industries, shedding light on practical strategies for implementing the third Toyota principle. The goal is to not only optimize production processes but also enhance flexibility and adaptability to evolving demand patterns. The article concludes by examining the pivotal role of this principle within the broader context of operational excellence and the development of more efficient production systems. The research in focus centers around a company operating in the metal industry. Through the utilization of BOST questionnaire surveys, production workers within the company expressed their perspectives, revealing which factors they deemed most crucial. The analysis of research results takes various forms, including tables, histograms, radar charts, and several statistical tools. The overarching objective of this analysis is to identify and present the most critical factors by constructing significance sequences based on the obtained results. The findings obtained for small and medium-sized enterprises align with results from similar tests conducted in different business settings, indicating a level of consistency and applicability across various organizational contexts.

АВТОМАТИЗАЦИЯ КОНТРОЛЯ КАЧЕСТВА НА ПРОИЗВОДСТВЕ С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ И КОМПЬЮТЕРНОГО ЗРЕНИЯ: РАЗРАБОТКА И АНАЛИЗ ЭФФЕКТИВНОСТИ СИСТЕМЫ

This article discusses methods for automating the quality control process in industrial enterprises using machine learning and computer vision. Special attention is paid to convolutional neural networks and their use for detecting defects in images. Specific examples of quality control system implementation in Python are given, describing the main steps in developing and using convolutional neural networks, as well as methods for evaluating system effectiveness. Overall, automating the quality control process using machine learning and computer vision is an effective way to improve product quality and optimize production processes.

Activated carbon supported Ni, Fe, and bimetallic NiFe catalysts for COx-free H2 production by microwave methane pyrolysis

The goal of this study was to test the effect of metal-impregnated carbon-based catalysts on the conversion of methane to hydrogen gas and solid carbon using microwave reactor technology. Monometallic and bimetallic catalysts on activated carbon supports (Ni/AC, Fe/AC, NiFe/AC) are compared during methane pyrolysis testing. Catalytic methane pyrolysis was carried out in a microwave reactor at reaction temperatures of 600 °C and 800 °C. For comparison, one of the catalysts (NiFe/AC) was tested in a conventionally heated reactor at 800 °C. The prepared catalysts were characterized by X-ray diffraction (XRD), while post-reaction catalysts were characterized by XRD and SEM. During reaction testing, the monometallic Ni/AC catalyst exhibited the best catalytic activity (CH4 conversion: 46.0 and H2 yield: 46.9 %) when reacted in the microwave reactor, however, it suffered from rapid deactivation from carbon deposition (carbon yield: 0.39 g C/g catalyst). The bimetallic NiFe/AC catalyst was slightly less active (CH4 conversion: 36.9 and H2 yield: 40.5 %) but it was more resistant to carbon formation (carbon yield: 0.27 g C/g catalyst) suggesting it may have greater long-term stability. The NiFe/AC catalyst was also the most energy efficient as it required the least microwave power to maintain the 800 °C reaction temperature compared to the other catalysts tested. Methane conversion of the bimetallic NiFe/AC at 800 °C under microwave irradiation was three times the conversion under conventional heating at the same reaction temperature. This work demonstrates the use of microwave-specific catalysts for catalytic methane pyrolysis in a microwave reactor, and can be used as a foundation for further methane pyrolysis process and catalyst optimization for COx-free H2 production.