Single-crystal silicon (Si) and silicon carbide (SiC) are core semiconductor materials in communication, lighting, power generation, and transportation. However, their high hardness and wear resistance combined with low fracture toughness have posed significant challenges for high-efficiency and low-damage machining. Aqueous suspensions containing nanoparticle additives have recently been developed for sustainable manufacturing due to their satisfactory tribological performance and environmentally friendly nature. In this work, nanoadditives, including two-dimensional (2D) graphene oxide (GO) nanosheets and zero-dimensional (0D) diamond nanoparticles, were ultrasonically dispersed in water to formulate different GO-based nanosuspensions for achieving high-efficiency and low-damage abrasive machining. The experimental results indicated that GO nanosuspension was a suitable coolant for grinding Si, generating a ground surface of 32 nm in Ra, owing to its great lubricity and excellent resistance against mechanical abrasion. Diamond-GO hybrid nanosuspension demonstrated a synergistic effect in abrasion, lubrication and oxidation, which was thus appropriate for polishing SiC single crystals, leading to approximate 60% and 30% improvements in removal and roughness respectively, in comparison to a commercially available diamond suspension.
Grinding is widely used in orthopedic surgery to remove bone tissue material, but due to the complex and brittle structure of bone, it is prone to mechanical stresses that cause cracks and damage to the bone tissue. Furthermore, bone replacement materials typically have high hardness, strength, and brittleness, which lead to increased tool wear and damage, such as cracks and deformation during grinding. Therefore, ensuring the surface quality of bone and replacement materials during the grinding process has become a critical issue. This necessitates the development of grinding force models that consider various processing parameters, such as feed rate and cutting depth, to guide industrial production. However, currently, research on the grinding force prediction models for bone tissue and its replacement materials is relatively scarce, and there is a lack of corresponding grinding force model reviews for unified guidance. Based on this, this article focuses on bone grinding technology and, conducts a critical comparative analysis of the grinding force models for bone tissue and its replacement materials, and then summarizes the grinding force prediction models in the grinding process of bone tissue and bone replacement materials. First, according to the material types and material removal mechanisms, the materials are categorized into bone tissue, bio-inert ceramics, and bio-alloys, and the material removal process during grinding is analyzed. Subsequently, the grinding force prediction models for each material and the accuracy errors of each model are summarized. The paper also reviews the application of these grinding force prediction models, explaining how processing parameters such as feed rate and cutting depth influence grinding forces and their interrelationship. Finally, in light of the current issues in the grinding of bone tissue and replacement materials, potential future research directions are proposed, aiming to provide theoretical guidance and technical support for improving the grinding quality of bone tissue and its replacement materials.
Artificial Intelligence (AI) and Machine Learning (ML) are transforming manufacturing processes, offering unprecedented opportunities to enhance sustainability and environmental stewardship. This comprehensive review analyzes the transformative impact of AI technologies on sustainable manufacturing, focusing on critical applications, including energy optimization, predictive maintenance, waste reduction, and circular economy implementation. Through systematic analysis of current research and industry practices, the study examines both the opportunities and challenges in deploying AI-driven solutions for sustainable manufacturing. The findings provide strategic insights for researchers, industry practitioners, and policymakers working towards intelligent and sustainable manufacturing systems while elucidating emerging trends and future directions in this rapidly evolving field.
Ultrasonic vibration-assisted grinding (UVAG), which superimposes high-frequency, micro-amplitude ultrasonic vibration onto conventional grinding (CG), offers several advantages, including a high material removal rate, low grinding force, low surface roughness, and minimal damage. It also addresses issues such as abrasive tool clogging, thereby enhancing machining efficiency, reducing tool wear, and improving the surface quality of the workpiece. In recent years, the rapid development of advanced materials and improvements in UVAG systems have accelerated the progress of UVAG technology. However, UVAG still faces several challenges in practical applications. For example, the design and optimization of the ultrasonic vibration system to achieve high-precision, large-amplitude, and high-efficiency grinding remain key issues. Additionally, further theoretical and experimental studies are needed to better understand the material removal mechanism, the dynamics of grinding force, abrasive tool wear, and their effects on surface quality. This paper outlines the advantages of UVAG in machining advanced materials, reviews recent progress in UVAG research, and analyzes the current state of ultrasonic vibration systems and ultrasonic grinding characteristics. Finally, it summarizes the limitations of current research and suggests directions for future studies. As an emerging machining technology, UVAG faces challenges in many areas. In-depth exploration of the theoretical and experimental aspects of high-precision, large-amplitude, and high-efficiency ultrasonic vibration systems and UVAG is essential for advancing the development of this technology.
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.
