Advances in Manufacturing IV

A rapidly developing technology, digital twins have the potential to completely change how products are designed and developed. Digital twins enable the simulation of the performance of various design concepts, the identification of potential issues with a design, and the optimization of the design for certain performance needs. However, in terms of digital twins and their utilization in product development, there are some gaps in the existing literature. There is also a lack of a standardized digital twin framework. There is currently no structure for creating and utilizing digital twins that is universally approved. Because of this, it is challenging for many organizations to share and work together on digital twin initiatives. Another aspect to consider is the huge investment of putting digital twin technology into practice. Implementing digital twin technology can be costly, particularly for large and complicated products. This may prevent small and medium-sized firms from adopting the technology. Real-time data integration is also lacking. For digital twins to be accurate, the physical product must provide real-time data. Getting real-time data from sensors and other devices might, however, be challenging to include into the digital twin. This paper examines the bibliographic research gaps concerning the use of digital twins specifically for new product design and development. The report also addresses some solutions and how the limitations might affect the development of digital twin technologies in the future.

The quality of production, the performance of deliveries and the success of the business depend to a large extent on the people who work in production. It is why, despite the growing robotisation and digitisation in the manufacturing industry, human capital remains one of the strengths of today's organisations. In this context, education and the professional training of workers in the activities performed are crucial for any company to prosper and maintain its position in an increasingly competitive and changing environment. In some industries, such management of operator skills training is even more important due to the complexity of production processes and work changes caused by different volumes of activities or demand variability. If employees have to rely on low-quality work instructions, they are less efficient, make more mistakes and are less satisfied with their work. Therefore, it is in the interest of companies to avoid this quality deficiency. The manuscript is dedicated to digitalisation, specifically e-workstations in a manufacturing company, where it is important to minimise worker errors and time wasting.

Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using Artificial Intelligence (AI) and Machine Learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.

Augmented and Virtual Reality are two intertwined concepts. These are distortions of the reality humans live in, either partially, for the first case, or creating a totally virtual environment. Both are related to the fourth industrial revolution. Although these are topics that were born during the past century, they have gained increasing relevance in the last decades. This paper performs a systematic literature review of the evolution of Augmented and Virtual reality. The past is explained through a brief overview of older developments, while the present is presented through a bibliometric research, coupled with case studies applied in various industries. Finally, future paths and conclusions are presented on the last section of the paper, which include a look at the tendencies of both technologies, that seems to lie in more mobility and convenience. The authors conclude that both Augmented and Virtual reality have a big potential to grow and become an important part of various industries.

In this paper, we evaluate several aspects of mixed reality (MR) and its use in Industry 5.0. In the introduction, we define the term Industry 5.0 and its connection with mixed reality as a key educational tool. Next, we devoted ourselves to the scientific interpretation of the terms Industry 5.0 and mixed reality. We then moved on to software and hardware tools in the field of MR and summarized the methods and materials in dealing with the topic of mixed reality in Industry 5.0. We assess the importance of MR in Industry 5.0 education, emphasizing its ability to provide authentic and immersive learning experiences for university students. Finally, we discuss ways to evaluate the results of MR implementation in education, including quantitative and qualitative methods. With this article, we cover a wide range of topics related to mixed reality and its applications in Industry 5.0 and education. By implementing the proposed methodology, we will provide students with a competitive advantage in their application in the smart factories of the future.

The development of innovative Industry 4.0 technologies and widespread digitalization have enabled manufacturers to find innovative ways to optimize production operations. One way to assess the transformation of an enterprise is the ADMA method, which involves assessing the digital maturity of the factory. The audit carried out as part of the creation of the company's roadmap aims to determine its maturity in seven areas. The roadmap recommends technologies for each transformation area and the benefits that can be obtained from their use. Various Lean Manufacturing methods can be significant at each of these stages. The article assigns Lean Manufacturing methods to each transformation, which support the development of the company in their area. Based on a literature review 15 Lean Manufacturing methods were selected, such as: Hoshin Kanri, 5S, Single-minute exchange of dies (SMED), Total Productive Maintenance (TPM), Kaizen, Modern QFD, Standardization, Plan For Every Person, Training Within Industry (TWI), Value Stream Map (VSM)/MAKIGAMI, POKA-YOKE, System PULL, One-Piece Flow, Just-in-Time (JIT), Heijunka.

The work includes an overview of the possibilities and needs for implementing the exploitation decision-making process regarding the subsystems of the urban engineering system in the conditions of implementation and operation of the SmartCity concept. Based on discussions on key operational problems, in the first part of the article, the SmartMaintenance concept was proposed. Its implementation allows to maintain the exploitation potential of technical systems at an appropriate level. The second part of the article includes a proposal of scenario method as a solution of exploitation policy modeling problem. Scenarios and computer support tools built on this basis enable effective assessment of the implementation of exploitation processes, which is particularly important in long-term conditions of significant decision uncertainty. A set of supporting tools has been developed to implement the SmartMaintenance concept proposed and described in the article. The final part of the article describes the operation of one of the most important such tools - the SMOPE. This tool helps create scenarios and also contains mathematical algorithms to evaluate them.

The paper introduces a method for online detection of root-cause machine and bottleneck identification in manufacturing systems, aiming to enhance reliability and productivity. The study addresses the interconnected nature of manufacturing systems, emphasizing the impact of equipment faults on overall performance of the line. Existing fault propagation methods are discussed, highlighting the need for a comprehensive approach considering the entire production process. The proposed root-cause algorithm utilizes a digital shadow concept, analyzing machine states and historical data to identify the primary source of faults. The study focuses on machine workstates, distinguishing between internal and external causes, thus determining the root-cause machine affecting the entire line. A Gantt chart-based approach considers the relative timing of events, enhancing accuracy in root-cause determination. Bottleneck detection methods are presented, including analysis of active periods and an arrow method based on machine blockages and starvations. The study showcases a cloud-based system, LogiX, for real-time data processing and visualization, integrating Industry 4.0 principles. A case study evaluates the proposed methods using an 8-h shift period in a bottle filling&packaging line. Root-cause analysis achieved an 89.23% detection efficiency, demonstrating practical applicability. Bottleneck detection methods, both active period analysis and the arrow method, identified the Labeller as the potential bottleneck. In conclusion, the paper provides a valuable contribution to manufacturing system optimization, offering a systematic on-line approach to root-cause identification and bottleneck detection. The proposed methods exhibit promising results, with potential applications in enhancing overall equipment effectiveness and production efficiency.

In nearly every domain today, additive technologies are advancing, particularly in the realms of technical execution and materials. The outcome of these processes is a faster, more economically efficient, and more precise production of models tailored to meet the material requirements arising from their intended purposes. However, all this progress shares a common denominator - the methodology of creating models in planar layers. All Fused Deposition Modeling (FDM) devices, regardless of their construction type, are capable of actively engaging the ‘Z’ axis in the printing process, not only after completing all perimeters at a specific height. Current software solutions now enable the modification of the process of generating planar layers into a form wherein non-planar surfaces are created in models through active movement along the ‘Z’ axis during the entire creation phase. The result of this activity is a model with coarse planar layers that serve as a foundation for creating the eventual non-planar surface. This publication intends to elucidate the process of modifying G-code for printing in non-planar layers for specific samples intended for further research. These samples aim to explore potential alterations in force distribution within the non-planar layers of various material types in the subsequent research phase.

Since Covid and the following bottlenecks in material supply companies are highly sensitive regarding the vulnerability of their supply chains. Delayed deliveries, failures of sub-suppliers, and supply shortages are only a few of the problems arising over the last years in global markets. This work deals with the potential of graph database systems in terms of increasing transparency and resilience in manufacturing supply chains. For the research, an open-source solution is tested for its functionality so that small and medium-sized enterprises (SMEs) can also benefit from the advantages of such systems. The goal is to find out if such systems are suitable in the case of multi-tier supply chains. The more sub-suppliers there are between the manufacturer and the raw material producer, the more difficult it is for a company to manage the entire supply chain. In this work, the supplier network is exemplarily visualized using a NoSQL database focusing on graph-based data models. Queries are designed and tested facilitating the user to search for specific criteria and critical situations within the supplier network. The results in this research suggest that there is considerable potential for increasing resilience as supply chain managers can use such tools to act based on the results instead of reacting when problems are already materializing along supply chains.

Selecting the right values for an algorithm’s parameters usually is a difficult, critical, and time-intensive endeavor. Though parameters can be traditionally adjusted in an empirical way, recent research indicates that automated tuning methods can frequently identify superior parameter configurations. In this sense, Racing Techniques emerge as a method of tuning hyperparameters and finding the best configurations for an algorithm. It is a time and resource efficient method. One of the more explored techniques in racing is the F-Race.ones. This work is a systematic literature review. The first section provides the context surrounding Racing Techniques. The second section explores the literature currently available on overall Racing Techniques and F-race, introducing all the essential concepts. The third section presents the research problem. The fourth section relates case studies of other works, being complemented by a bibliometric review that shows the growth of the concepts presented. The fifth and last section completes the work with the future paths for Racing Techniques, where opportunities arise in different fields of study, and provides the conclusions drawn.

The present business landscape, particularly in the era of Industry 4.0, is characterized by dynamic development, global collaboration, and intense competition for customers. To navigate these challenges successfully, companies must establish mechanisms for close and effective cooperation. Concurrently, ongoing projects for the development and production of innovative products are imperative. However, the seamless integration of various business and IT systems used daily by a company is essential to achieve these goals. Among the pivotal IT systems, alongside established CAx and ERP solutions, Product Lifecycle Management (PLM) systems play a crucial role.

Implementing a PLM system poses a significant challenge for enterprises, as each implementation project follows a unique path. There is no universally established and proven method for successful PLM system implementation, given the diverse applications and varying levels of complexity and integration with other systems within an organization. Identifying the benefits derived from PLM systems is intricate due to their extensive application across different company areas.

The literature lacks concrete information, including figures, on the effects of implementing PLM systems. This paper aims to address this gap by presenting the characteristics of PLM systems and their role in modern enterprises. It delves into the implementation procedure, offering examples and effects in design, construction, technology departments, and the supply chain of enterprises operating globally, including those in Poland.

The free market means that companies have to be very vigilant and react quickly to changes. Growing competition can pose a threat to a company’s operations, which is why companies must constantly make sure that products meet market expectations while maintaining appropriate functional and quality requirements at the right price. It is therefore very important to supervise production processes and improve them. The production capacity and economic effects of industrial enterprises depend to a large extent on the technology used, organization and management. In terms of means of production, broadly understood automation comes to the rescue, in which robotization is becoming more and more important. The article presents the results of research on the use of vision systems and manipulators in industry 4.0. A robotic system consisting of a robot, a vision system and a feeder was selected and tested to pick up delicate products and put them into appropriate containers. Adding the robot to the production line will allow, in addition to automatic packaging of products, to monitor the number of manufactured product packages per a given unit of work shift time on an ongoing basis. The robot program additionally allows you to view statistics about the takt.

The rapid evolution of additive manufacturing, commonly known as 3D printing, has spurred the need to determine when this technology should be favored over conventional or subtractive manufacturing techniques. Selecting additive manufacturing for a given part hinges on its complexity, as it must offer an economic advantage over traditional manufacturing methods.

This paper introduces a method for assessing the geometric complexity of parts intended for additive manufacturing. While the prevalent approach in most industries relies on surface area to volume ratio as an indicator of complexity, our study highlights its limitations and potential for misleading manufacturers. In response, we propose a novel method that leverages machining surface generation and scrap ratio to evaluate part complexity. The effectiveness of this method is demonstrated through validation with real-world industrial parts and virtual manufacturing assessments, proving its superiority to the traditional surface area to volume ratio.

One of the key advantages of our proposed method is its potential for automation, with the capability to analyze stereolithography (STL) files and estimate part complexity features. This automation has the potential to significantly reduce the cognitive load on additive manufacturing experts, making the technology more accessible and efficient. Overall, our research contributes to the critical decision-making process for selecting the most suitable manufacturing method, ensuring that additive manufacturing is chosen when it offers a distinct economic advantage driven by part complexity.

The paper presents an exploratory investigation concerning the usage of AI tools in automotive companies in order to streamline their risk management processes. A risk identification procedure is performed at organizational and process levels, and a comparative analysis is undertaken between the classical approach for developing proper mitigation measures and the AI-supported manner of doing the same. Some of the most popular tools in this field are employed and studied, such as large language models, data analytics and knowledge representation. The differences and changes are analyzed from the point of view of their effectiveness, efficiency and adaptability within the existing manufacturing frameworks in the automotive industry.

Planning in dynamic and changing environment is not easy. Requires considering both external (market) factors and internal (technological) factors, recognizing synergies and trade-offs between them and exploiting their potential. The article contains an attempt to describe the impact of changes taking place in the environment of the production planning process and in the enterprise, i.e. factors such as innovations, technical progress, producers’ strategies, on the development of the enterprise. Components of the production planning process determinants such as market research, introduction of new products to the market or use of new technologies were taken into account and characterized based on review of the literature. References to innovations, their types and importance were presented as well, showing innovations as results of market research and reason for technological changes and product development. The conclusions from the analysis were used to prepare a summary of the article and conclusions were presented in the context of contemporary concepts such as Sustainable Development, Industry 4.0, and Industry 5.0.

The article presents the developed heuristic-based scheduling algorithm dedicated for MTO-MTS hybrid production. The developed algorithm allows to maximize the machines load by production tasks taking into account economic and organizational conditions such as the materials availability, costs of storage, the value of frozen funds and the probability of selling or using a given part in the production process while meeting deadlines for the implementation of production tasks resulting from customer orders. In order to verify the proposed solution, a number of experiments were carried out, both using real data from a manufacturing company from the automotive industry as well as simulation data. The obtained results confirmed the thesis, and based on the experiments, conclusions were drawn about the effectiveness of the proposed method.

Technological advancements and market changes force the pursuit of continuous improvement of processes implemented in the enterprise. This is largely due to the increasing demands of customers regarding the price of products, their quality, durability, and delivery time. Society’s changes, including the ongoing Industrial Revolution 4.0 and 5.0, also have an impact on this, affecting the development of logistics. Logistics is one of the essential elements in the functioning of companies, crucial for maintaining their competitiveness, responsible for the fluidity and efficiency of goods delivery. Thanks to technological advancements and changes in international trade, logistics is becoming increasingly complex, while its execution capabilities are growing. However, the mere existence of a logistics department does not automatically guarantee better financial results. To assess the effectiveness of logistics in a company, it is necessary to systematically conduct detailed analyses of related processes. Only such an examination allows answering the question of whether logistics fulfills its role in the company’s structures, which is the aim of this article. In it, an analysis and evaluation of the impact of implementing selected technology on the development of the internal supply chain processes are presented, in this case, concerning the warehousing process. The obtained results are presented using a manufacturing company as an example. A research hypothesis was adopted, assuming that the application of selected Industry 4.0 technological solutions will increase the efficiency of the analyzed supply chain.

In the recent years, flexibility has become more important within our industrial systems. This yields to more complex decision making during this industrial system’s design. In this work, we present a method to support this decision making by presenting a set of indicators that enable the quantitative comparison of a set of industrial system designs for a pulse line, based on the number of stations selected. To achieve this, two well-known problems in process optimization have been utilized jointly: the Simple Assembly Line Balancing Problem and the Resource Constrained Project Scheduling Problem. A preliminary study was conducted on an exemplary production line from the aeronautical industry. Subsequently, we progressed to develop valuable insights encompassing both problems. To achieve this, two metrics were introduced: lead time and the number of hours worked. These metrics facilitated the generation of data illustrating how the number of hours worked varies with cycle time, lead time, and the number of stations. The outcome is a set of indicators and graphs that provides more precise and well-founded information, helping in the decision making of which approach is more suitable based on the specific needs and constraints of each project.

The study aims to investigate the technology management (TM) sphere in metal processing companies engaged in a business cluster. This study presents SEM modelling using the PLS method on data obtained from a survey of companies involved in a business cluster, which allowed to examine the impact of factors important for cooperation on individual stages of TM. This study implies that business cluster engagement can play a beneficial role in the TM sphere in the studied companies. Engagement in the cluster can facilitate knowledge acquisition, leading to strategic and operational advantages. The cluster can play an especially beneficial role when it comes to the initiation of work on a technology – a search for new technologies, and when existing technologies are developed. Companies can obtain precious knowledge from/thanks to participation in a business cluster. Knowledge supporting and allowing innovations appears to play a primary role in achieving advantages from cluster membership; however, this is not applied to most companies in the studied cluster. The SEM model proves that cooperation between business partners in a cluster structure really matters.

This paper delves into Product-as-a-Service (PaaS) business models, with a primary focus on proactively managing strategies designed to ensure the requisite service level availability for Multi-Function Printer (MFP) offerings. Within this framework, we introduce an all-encompassing system that combines predictive analytics, automated resource allocation, and adaptable service scaling to address potential disruptions and enhance the optimization of service delivery. The core issue addressed in this research falls within the realm of resource allocation problems, specifically concentrating on formulating a strategy for designing lease offers that adhere to predefined risk thresholds related to potential failure to provided resources. Through a case study and the outcomes of computer experiments, we illustrate the efficacy of our approach, grounded in constraints programming, in elevating PaaS availability while efficiently utilizing resources. These findings hold considerable value for consistently delivering reliable services in the ever-evolving digital landscape.

Serial-parallel production systems are often used in industrial practice, especially when high process reliability must be guaranteed. The article proposes a simulation model of a serial-parallel production system that was created based on Tecnomatix Plant Simulation software. For the developed model, throughput tests were carried out for various allocations of inter-operation buffer capacity and production flow control strategies. Based on the research results, a methodology for rapid prototyping of serially parallel production systems with specific throughput was proposed. The results of the conducted research can be helpful in quickly determining the assumptions for the construction of serial-parallel production systems in order to achieve specific throughput.

The stability of production processes is critical for companies to deliver products to customers on time and to specification. In mass production, where a large number of machines and equipment are used, processes are multistage and product variety is high, the possibilities for effective analysis depend largely on the level of implementation of information techniques that can increase the availability of process data. In turn, increased data availability provides a more complete picture of production processes, the changes that are taking place and their impact on quality and stability. With the increasing number of information techniques available, the amount of data generated by the manufacturing industry (process records, events, images, parameters) is expected to grow exponentially. However, access to data alone is not sufficient. To make the data useful for Root Cause Analysis (RCA), it must be analyzed, interpreted and visualized. This paper presents a comparison, evaluation and selection of the type of control cards used to monitor and control the quality performance of manufactured products. It was assumed that the values monitored on the control cards should be able to be represented as sets of non-conforming products (product fractions) in the whole population of manufactured products.

The content of the paper is the application of Industry 4.0 technologies throughout the entire product life cycle in the example of the automotive industry. Particular attention was paid to the necessity but also to the difficulties of integrating various phases of the product life cycle. A model for the integration of product life cycle phases using PLM IT systems and the product structure models created by them is presented. The paper consists of 6 sections. The first one presents the factors that enable, determine and limit the use of modern technologies in the automotive sector. The second section defines and explains the concepts of product life cycle and product life cycle management. Product life cycle management is a condition for the effective and efficient solution of many problems, especially those related to sustainable development. This requires the integration of activities carried out in various phases of the product life cycle. An example of an approach to solving this problem is discussed in section three. The content of the fourth section is a “map” of the applications of Industry 4.0 technologies against the background of the car life cycle. Section five discusses the possible impact of Industry 4.0 technologies on the future development of the automotive industry. Conclusions and summary are included in section six.

The paper is devoted to creating a methodological approach to apply root cause analysis for solving the specific materials science task. The case study determined the causes of the destruction of the elements of the exhaust tract of the gas pumping unit. Root cause analysis was used as a systematic process to identify and address the underlying causes of problems or incidents rather than just addressing their symptoms. In the first step of defining the problem, a destroyed element of the exhaust tract of the gas pumping unit was chosen to discover the causes of its failure. In the second step of collecting the data, the failure analytics of a destroyed element was carried out. In the third step, a mnemonic called 6Ms of production was used to identify as many causal factors as possible. In the fourth step, causal factors were analyzed in detail, and the root causes were determined. In the fifth step, the recommendations for implementing solutions to the abovementioned problem were given.

This article focuses on the study of the relationship between consumer awareness and footwear customization in the context of the idea of Industry 4.0. An analysis of the results of a survey was conducted, focusing on key aspects such as consumer preferences for personalized products and the level of understanding of Industry 4.0 terminology. 261 respondents participated in the survey, including 223 (85.4%) women and 38 (14.6%) men. The purpose of the article is to conduct a survey and formulate conclusions based on the research hypothesis. The purpose of the survey is to learn about and assess the level of consumer awareness of shoe buyers vs. its impact on the development of shoe customization. The results show significant relationships between social factors and consumer awareness, particularly highlighting the influence of gender, age, education, and place of residence on purchase preferences. The analysis aims to provide a deeper understanding of the factors determining consumer choices in the context of product personalization and the implications of Industry 4.0 ideas on consumer awareness.

Kaizen or change for the better continues to be a term that needs much to be explored in the industrial environment and in the academic arena. Understanding its implementation, especially in multinational manufacturing companies (EMMs), is vital to avoid errors and confusion. On the other hand, the EMMs that apply it must explore the degree of maturity or sustainability of the implementation of Kaizen techniques to achieve positive results in their operational performance. Therefore, the general objective of this study is: to analyze the impact of Kaizen techniques through the degree of maturity on operational performance in multinational manufacturing companies (EMM). A mixed methodology (qualitative and quantitative) to analyze the implementation of the Kaizen EMM, the quantitative result, speaking, indicates the most implemented techniques, and how those EMMs that have a greater degree of maturity and implementation present better results in their operational performance.

Innovation in organizations is a management approach that helps achieve high standards of competitiveness in today’s markets, in addition to the financial and economic success so sought after in the 21st century. Many organizations today are committed to making efforts of some kind of innovation; call it product innovation, process innovation or even business model innovation. In Mexico and the rest of Ibero America, some craft organizations have begun to make efforts of some kind of incremental one, also known by its Japanese name “Kaizen”. This paper presents a case study methodology based on an artisanal company that manufactures “Talavera” products in Puebla Mexico. The company applied incremental innovation in some of its artisanal processes to significantly improve its operations by reducing MUDA (waste) and errors in manual processes.

In the era of striving for sustainable production and organization of human work, projects with ergonomic objectives are being implemented in order to provide employees with the safest and most comfortable working conditions. The question of how to achieve a positive result from such projects is therefore being addressed. The research carried out and presented in this article analyzed the factors that contribute to the successful implementation of pro-ergonomic project management. These factors were assessed by experts in such projects and the results of these assessments were then analyzed using descriptive statistics and the convergence of expert opinions was examined using cluster analysis. The most important factors assessed by the experts were: “Ensuring full support of top management in the activities of the ergonomics” and “Effective involvement of employees in the analyzed workplaces in the implementation of pro-ergonomic projects (e.g. through surveys, interviews regarding the workplace)”. The results were used to develop a method for implementing pro-ergonomic projects in manufacturing companies, the assumptions of which can be adapted depending on the project input data and company capabilities. The results of study can be used in manufacturing companies in different industries interested in pro-ergonomic project management.