This paper delves into the X.0 Wave/Tomorrow Age Theory, a comprehensive framework conceived, invented, introduced, and developed by Prof. Dr. Hamid Mattiello between 2010 and 2017, to analyze the evolution of human civilization through distinct epochs of knowledge, technology, and business (KTB). The theory segments history into transformative waves, from the first development (X.0 ≤ 1.0) and Agricultural Age (X.0 = 1.0) and the X.0 Wave/Tomorrow Age Theory (2.1 ≤ X.0 ≤ 2.2) spanning the 17th Century to 1870, to the current Age of Artificial Intelligence (X.0 = 4.0). It also projects into the anticipated Human Age (X.0 = 5.0) and Transhuman Age (X.0 = 6.0) and beyond (6.0 ≤ X.0). Each wave represents a revolutionary phase characterized by significant advancements that shape societies, industries, and technologies. The X.0 Wave Theory integrates these historical phases with the Seven Pillars of Sustainability (7PS) to evaluate their societal impacts. The paper explores how these waves influence future developments by examining historical roots, emerging technological paradigms, and socio-economic dynamics. It examines how advancements in AI, biotechnology, and virtual reality are reshaping industries and global business practices, while also addressing the ethical and sustainability considerations essential for navigating these changes. By forecasting future trends, confronting current challenges, and preparing for potential crises, the X.0 Wave Theory offers a robust framework for understanding and adapting to the rapid pace of technological evolution. This paper provides deep insights into how these transformative waves shape our past, present, and future, offering valuable perspectives for navigating the complexities of an increasingly digital and interconnected world.
In the mechanical cutting process, the surface defects of the workpiece are an important indicator of cutting quality and also reflect the condition of both the machine tool and the cutting tool. Effective detection of defects on the surface of the workpiece plays an important role in adjusting the processing conditions promptly, reducing losses, improving the utilization rate of the workpiece, and maintaining the normal operation of the equipment. To address the challenge of detecting surface defects on workpieces, an inspection method based on an improved Single Shot Multibox Detector (SSD) model is proposed. The method simplifies the detection model and reduces the computation by proposing a DH-MobileNet network instead of a VGG16 network in the SSD structure. The inverse residual structure is also used for position prediction, and null convolution is used instead of a down-sampling operation to avoid information loss. A scanning electron microscope was used to obtain the surface image of the workpiece. A dataset of workpiece surface defects was constructed and expanded, then used to train and test the model for detecting three common types of high-frequency defects: peel-off, chip adhesion, and scratches. The effect was compared with YOLO, Faster R-CNN, and the original SSD model. The detection results show that the method can detect the defects on the surface of the workpiece more accurately and quickly, which provides a new idea for defect detection in real industrial scenarios.
The Fourth Industrial Revolution, known as Industry 4.0 (I4.0), has introduced a completely disruptive pace compared to the rhythm of the three previous industrial revolutions. With a wide range of technologies, design principles, and a high potential to replace the human workforce, this industry presents aspects that urgently require greater attention. With a purpose close to meeting this need, Industry 5.0 (I5.0) emerges, a milestone not yet registered with historical facts but with great hopes for positive changes. While I4.0 maintains design principles for its complete activity, I5.0 has a supporting tripod for its operation. As I5.0 is still perceived as an evolutionary character of I4.0, it is expected that for the time being, it will use these same design principles for its activity and may later include new principles. Based on this context, this article seeks to contextualize, in a descriptive way, the functioning of design principles 4.0 for the imminent industrial context 5.0. The article uses a conceptual approach based on previously published literature on the subject of design principles in I4.0. Although the characteristics of I5.0 are not yet fully known, it is assumed that it has a more refined character than I4.0, so that points that presented a positive, significant, and already consolidated result are maintained for the new model. The distinctive feature of this article is its presentation of a textual analysis that breaks down the potential contributions of design principles in relation to the three core values of Industry 5.0: Sustainability, Human-Centricity, and Resilience.
Intelligent factories provide flexible and adaptive production processes, offering significant competitive advantages to manufacturers and are widely studied in industrial production. Information technology is recognized as a key factor influencing the production efficiency and intelligence of Intelligent factories. However, current research has primarily focused on the operational processes of intelligent factories, with limited analysis of information technology. To address this gap, this paper conducts a bibliometric analysis of information technology in intelligent factories, along with a review of its development and applications. Firstly, the data collection and visualization methods of bibliometrics are introduced. Secondly, bibliometric analyses are performed using platforms such as VOSviewer and Scimago to investigate co-authorship, co-citation, and contributions from countries and institutions in the field of information technology for intelligent factories. Finally, a framework for information technology in intelligent factories is established, summarizing its development in terms of information acquisition, transmission, processing, management, and control. This paper aims to assist scholars in understanding the development trends of intelligent factory technology and enhancing the informatization level of intelligent factories.
As a typical high-performance alloy, the excellent mechanical properties and stringent processing requirements of 30CrMnSiNi2A high-strength steel pose great challenges to high-quality and efficient processing. Currently, researchers have proposed methods such as improving cutting tool performance, minimal quantity lubrication (MQL), and applying external energy field to assist processing. However, due to the unregulated material properties, the further improvement of surface quality is limited, and there are problems of phase change and thermal damage in laser processing. Cold plasma jet (CPJ) is rich in active particles and has a low macroscopic temperature. It can effectively regulate material properties without causing serious surface damage. Therefore, a new 30CrMnSiNi2A machining approach adopting CPJ is proposed to improve the cutting process. The mechanism of its action on material properties and cutting process is revealed based on single-grain diamond scratching tests and micro-milling tests. The results show that CPJ can promote material fracture and improve material removal efficiency. The material removal efficiency R at 400 mN is increased from 0.433 before treatment to 0.895. Under the optimal processing parameters (feed speed Vf = 800 μm/s, spindle speed n = 40,000 rpm, and milling depth ap = 5 μm), compared with dry micro-milling, the cutting forces Fz, Fx and Fy in CPJ-assisted micro-milling are reduced by 26.5%, 24.8% and 31.3%, respectively. The surface roughness Sa is reduced by 19.3%, and the phenomena of plastic flow and burr are suppressed. The CPJ-assisted machining process proposed in this paper can regulate the material properties to improve the cutting process without causing serious damage to the material, providing a new approach for achieving high-quality and efficient processing of 30CrMnSiNi2A.
This paper reports, for the first time in the literature, a preliminary study to investigate the feasibility of utilizing waste algae powder (byproducts of biofuel manufacturing from algae) in binder jetting 3D printing to produce environmentally friendly products. In this study, the algae powder’s morphology and particle size distribution were characterized using scanning electron microscopy and particle size analyzer, respectively, and the flowability was assessed through apparent density and repose angle. The algae powder successfully printed the cylindrical, cubic, and gyroid parts on a binder jetting 3D printer. Results show that it is feasible to print parts with binder jetting 3D printing utilizing waste algae powder. The use of waste algae powder in additive manufacturing offers a novel approach to upcycling waste algae powder into valuable products for various applications such as packaging and construction.
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.
