Recently, onboard sensing and support devices have been used for the well-being of humans, animals, birds, plants and, more generally, biodiversity. The performance of these tools is closely linked to their electromagnetic environment, mainly artificially created by humans. Therefore, the presence of electromagnetic radiation linked to human activities near such tools constitutes a threat. The intelligent and sustainable manufacturing of these tools, which makes it possible to face such a threat, can be achieved through their design and optimization. This commentary aims to highlight the interaction of artificial electromagnetic radiation with onboard health tools involving living tissues in urban biodiversity (One Health concept) and the intelligent and sustainable construction and protection (Responsible Attitude concept) of these tools. The manuscript presents an overview of onboard devices, possible effects of electromagnetic radiation, durable construction and shielding, and analysis of electromagnetic compatibility integrity control. The main outcome of this contribution regarding sustainably designed onboard devices is that numerical analysis tools of electromagnetic fields could efficiently verify their integrity and the behavior of their necessary smart shields. These different themes are associated with examples of literature.
Lithium batteries pave way for rapidly reducing greenhouse gas emissions. Still there are concerns associated with battery sustainability, such as the supply of key battery materials like cobalt, nickel and carbon emissions related to their manufacture. While LiMn2O4 spinel is a common cathode material for Li-ion batteries that remove Co and Ni, studies on over-stoichiometric variants and their behavior across a broad potential range may be limited. Research in this area could provide valuable insights into the performance, stability and electrochemical characteristics of such cathodes, offering potential benefits for the development and optimization of Li-ion battery technologies. This study investigates the electrochemical behavior of Li-rich Li1+yMn2−yO4−δ (LMO, y ≈ 0.03, δ ≈ 0.01) spinel as a cathode in Li-ion batteries, focusing on the phenomenon of extra capacity under the extended operating voltage 1.5–4.8 V vs. Li+/Li. The nanostructured LMO sample synthesized by sol-gel method and calcined at 900 °C is characterized by X-ray diffraction, scanning and transmission electron microscopy and surface area measurements. The Li-rich spinel electrode delivers a specific discharge capacity of 172 mAh g−1 at 1st cycle. It retains 123 mAh g−1 at the 100th cycle (71.5% capacity retention) at current density of 100 mA g−1 current density (i.e., ~0.7 C rate). An excellent stability is obtained in the 1.5–4.8 V potential window, with a discharge capacity of 77 mAh g−1 after 500 cycles at the same current density, owing to the reduction of the Jahn-Teller effect by Li doping. These results contrast with the specific capacity of 85 mAh g−1 (1st cycle) and the capacity retention of 54.3% after 100 cycles, obtained when the cell operates in the narrow potential range of 3.0–4.5 V.
The scientific article analyzes the dynamics of textile industry production in the USSR and the Russian Federation from 1985 to 2022 years.The article provides a fairly complete overview of modern methods of forecasting the development of objects, mainly based on time series analysis, including issues of forecasting cyclic and discontinuous processes, forecasting multidimensional objects with a correlated system of indicators. Authors calculate the forecast until 2026 year based on a bank of mathematical forecasting models implementing various monotonic nonlinear transformations both along the ordinate axis and along the abscissa axis. The criterion of the minimum variance of the forecast error was used as a criterion for selecting a specific model from the bank. The scientific value of the article lies in the fact that, for the first time, it offers a criterion for choosing a mathematical model from a set of them, which uses the minimum estimate of the variance of forecast errors for this model. This work can be considered a step towards the creation of artificial intelligence since the selection of the optimal model for a specific time series allows to obtain a training sample for it, which is fundamentally impossible to obtain without it.
Laser Additive Manufacturing (LAM), an avant-garde technology in manufacturing, harnesses the precision of laser energy to fabricate intricate parts through the meticulous process of melting and subsequently depositing layers of metal powders. Among the esteemed materials employed, 316L stainless steel (316L SS) stands out for its unparalleled corrosion resistance, exceptional high-temperature tolerance, and remarkable creep strength, making it a ubiquitous choice in the aerospace, medical, and nuclear power sectors. LAM has distinguished itself in the fabrication of intricate 316L SS components, yet enhancing the metallurgical bonding strength within these structures remains a pivotal area of ongoing research. This research endeavor delves into the intricate microstructure and mechanical properties that characterize the interface between the LAM-produced 316L SS cladding layer and its substrate, further investigating how varying laser energy densities (E) subtly influence these properties within the additive manufactured components. Remarkably, the interface region exhibits a tensile strength of 615.1 MPa, surpassing that of both the deposited layer and the substrate by 5.4% and 7.4% respectively, underscoring a robust bond between the two layers. This investigation not only sheds light on the unique process capabilities and performance merits of LAM in crafting 316L SS cladding layers but also pioneers novel approaches and conceptual frameworks for bolstering the metallurgical bonding strength of this esteemed material. As such, it constitutes a treasure trove of insights for subsequent research endeavors and practical applications across related disciplines.
Technological innovations, education, business and society change quickly and often unpredictably. The fusion of artificial intelligence (AI), machine learning, augmented reality (AR), virtual reality (VR) and augmented reality (XR) opens a new era in which work, production, communication and thought processes are massively transformed. In this context, the challenge arises: How can small and medium-sized enterprises (SMEs) adapt to this accelerated change? This study highlights a path forward and introduces the concept of “SME 5.0” or “Hybrid SME” or “SME of Tomorrow” as a comprehensive solution to address the complexities of the digital age. In this integrated exploration of the X.0 Wave Theory and SME 5.0 Concept, the framework for human civilization’s evolution and technological shifts converges with a practical roadmap for small and medium-sized enterprises (SMEs) navigating the dynamic digital landscape. Acknowledging transformative waves in technology, economics, and societal structures within the X.0 Wave Theory, the study accentuates the ongoing nature of these shifts. It advocates for a long-term perspective, urging policymakers and industry leaders to consider potential future scenarios to devise strategies fostering innovation, competitiveness, and privacy safeguards. Simultaneously, the study introduces SME 5.0 as a holistic solution for SMEs, aligning with the transformative success envisioned by the X.0 Wave Theory. Proposing the Seven Pillars of Sustainability (7PS) framework tailored to SMEs, the concept emphasizes digitalization and sustainable technology. The title, “Harmonizing the X.0 Wave Theory and SME 5.0 Concept”, encapsulates the synergy between theoretical underpinnings and practical solutions. The subtitle, “Fostering Sustainable Collaboration, 7PS Engineering, and Overcoming Legal Challenges in the Digital Age”, provides a glimpse into the study’s focus on practical implications, sustainability, engineering, and legal considerations for SMEs in the rapidly evolving digital era.
This manuscript describes the research path when extending a maturity model. The initial model—ManuMaturity—was for manufacturing companies aiming beyond Industry 4.0. The extended OSME model covers data sharing within a supply chain, an open innovation ecosystem and sustainable manufacturing. The OSME maturity model has five maturity levels: traditional factory, modern factory, agile factory, agile cognitive factory and agile cognitive industry and seven dimensions (such as infrastructure, data, customer, business model, employee, sustainability and processes). The tool was experimented with in manufacturing companies on two occasions: with a set of manufacturing companies and a group of companies. In both cases, feedback was gathered from the respondents. The article follows the maturity assessment development phases such as scope, design, populate, test, deploy and maintain, and reports the software implementation of the maturity tool. With the help of the developed maturity model and the tool, it was possible to make assessments in case companies, where the tool and its results were commented mostly positively. The tool can be applied in various ways. For example, a group of people can jointly submit their common understanding and have a thorough discussion or a group of company representatives submit their responses and the variation is discussed afterwards.
With the development of the manufacturing industry, there is an increasing demand for high-efficiency processing, high-precision processing, and high-temperature processing. The characteristics of ceramic tools, such as high hardness and wear resistance, make them suitable for high-precision processing. Additionally, their excellent high temperature resistance perfectly meets the requirements of high temperature processing. However, ceramic tools have a relatively low strength and are prone to breakage, which limits their application in some high-strength machining fields. Their low toughness and brittleness also lead to easy cracking and reduced tool life, resulting in frequent tool changes that further limit processing efficiency. Therefore, improving the service life of ceramic tool materials is crucial to enhance processing efficiency and achieve significant economic benefits. With the development of material science, solid additives with toughening and strengthening properties have greatly improved the performance of ceramic tool materials and given ceramic tools new life-enhancing properties, such as lubrication and repair. By utilizing the combined action of one or more solid additives and employing surface coating technology, the service life of ceramic cutting tools is significantly extended. This makes the application of ceramic tools in industrial cutting more and more widely, and the demand is also growing rapidly. However, the mechanism and methods of various solid additives to increase the life of ceramic tool materials have not been systematically reviewed. The analysis of the composition and functional properties of ceramic tool materials was used as a basis to summarize the mechanism by which various solid additives improve the service life of ceramic tool materials, and to provide points for attention in their use. The aim is to assist researchers in designing and preparing new ceramic tool materials that can meet processing requirements. Finally, the research status, challenges, and prospects of enhancing the service life of ceramic cutting tools with solid additives are summarized, providing a foundation for further research.
This research paper explores the financial adoption challenges of the Industrial Internet of Things (IIoT) in industry. Previous studies have mainly concentrated on designing affordable IIoT devices, reducing operational costs, and creating conceptual frameworks to assess the financial impact of IIoT adoption. The objective of this paper is to investigate whether IIoT adoption’s financial benefits outweigh the initial costs in small and medium-sized enterprises (SMEs). The data from the Industrial Assessment Centers (IAC) database were analyzed, focusing on 62 U.S. manufacturing SMEs across 10 states and 25 Standard Industrial Classifications (SICs), evaluating projected IIoT implementation costs and anticipated cost savings. Results from the analyses reveal that statistically, the difference between implementation costs and savings is significant at a 95% confidence level. Practically, this indicates that SMEs, despite facing high initial costs, can expect these investments to be counterbalanced by substantial savings. From an engineering perspective, this finding raises awareness among SMEs that, beyond overcoming financial barriers, IIoT technologies serve as a strategic enhancement to operational efficiency and competitive positioning. This study acknowledges the limitations including reliance on estimated projections and a narrow industry focus. Future research should broaden the sample and explore the lifecycle costs of IIoT.
Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.
Nickel-based alloys has important application value in modern industrial field, but there are a lot of problems that are difficult to solve in traditional processing, and it is a typical difficult-to-process material. In order to improve the machinability of nickel-based alloys, scholars try to use a variety of non-traditional processing methods to explore and study the processing of nickel-based alloys. In these studies, ultrasonic vibration assisted processing technology and minimum quantity lubrication (MQL) processing technology can achieve remarkable results. The intermittent separation cutting characteristics of ultrasonic vibration assisted processing technology can improve the processing quality by changing the tool path, while minimum quantity lubrication processing technology can improve the lubrication effect of cutting, combining ultrasonic vibration assisted MQL processing leverages the benefits of both methods, resulting in improved machinability and expanded application of nickel-based alloys. Summarize the current research status on the machining mechanism of nickel-based alloys assisted by ultrasonic vibration and micro lubrication, and anticipate its developmental trends. This provides a reference for future research on the efficient machining mechanisms and practical applications of nickel-based alloys.
