Double end face grinding machining is a highly efficient surface grinding technique. And grinding temperature is an important factor affecting the surface quality of workpieces. However, it is difficult to monitor the surface temperature of the workpiece in real time because of the covered contact between the grinding wheel and the upper and lower surfaces of the workpiece during the machining process. This paper aims to conduct a mechanistic analysis and experimental investigation of the machining process to address this challenge. Initially, the paper conducts an analysis of the kinematic mechanism, modal analysis, and the grinding force mechanism specific to the double end face grinding process. Afterwards, the mechanisms leading to the generation of grinding heat and the associated heat transfer mechanisms are explored in depth. The paper then proceeds to solve the instantaneous temperature field during double end face grinding by the finite element method (FEM). Furthermore, the micro and macro profile heights of the machined workpiece surfaces are measured and analyzed. The results show that the machined workpiece surface shows a high center and low edge. This is due to the fact that the temperature at the edge of the workpiece is higher than the center during machining, resulting in more material removal. Through these investigations, the study is able to determine the optimal process parameters for the machining process. This in turn improves machining efficiency and product conformity. And these findings not only guide practical production processes but also provide a foundation for future theoretical research in this area.
The past decade has witnessed an exodus toward smart and lean manufacturing methods. The trend includes integrating intelligent methods into sustainable manufacturing systems purposely to improve the machining efficiency, reduce waste and also optimize productivity. Manufacturing systems have seen transformations from conventional methods, leaning towards smart manufacturing in line with the industrial revolution 4.0. Since the manufacturing process encompasses a wide range of human development capacity, it is essential to analyze its developmental trends, thereby preparing us for future uncertainties. In this work, we have used a Bibliometric analysis technique to study the developmental trends relating to machining, digital twins and artificial intelligence techniques. The review comprises the current activities in relation to the development to this area. The article comprises a Bibliometric analysis of 464 articles that were acquired from the Web of Science database, with a search period until November 2024. The method of obtaining the data includes retrieval from the database, qualitative analysis and interpreting the data via visual representation. The raw data obtained were redrawn using the origin software, and their visual interpretations were represented using the VOSviewer software (VOSviewer_1.6.19). The results obtained indicate that the number of publications related to the searched keywords has remarkably increased since the year 2018, achieving a record maximum of over 80 articles in 2024. This is indicative of its increasing popularity. The analysis of the articles was conducted based on the author countries, journal types, journal names, institutions, article types, major and micro research areas. The findings from the analysis are meant to provide a bibliometric explanation of the developmental trends in machining systems towards achieving the IR 4.0 goals. Additionally, the results would be helpful to researchers and industrialists that intend to achieve optimum and sustainable machining using digital twin technologies.
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.
