Functionally graded materials (FGMs) have emerged as a transformative class of engineered composites, characterised by continuous or stepwise variations in composition and microstructure that enable spatially tailored properties within a single component. This design flexibility offers clear advantages over conventional homogeneous materials and has driven increasing adoption across the aerospace, automotive, biomedical, and other high-performance sectors. Although research on FGMs has expanded rapidly over the past twenty-five years, the growing diversity of processing methods and applications calls for a consolidated assessment. Unlike previous reviews, this work integrates recent manufacturing advances with a comparative evaluation of tribological mechanisms across major FGM systems. The evolution of FGM manufacturing is examined, spanning conventional and additive manufacturing (AM) approaches, with the latter offering unprecedented control over gradient design, enabling precise, reproducible, and customisable architectures. The review synthesises recent findings on the tribological behaviour of metal–metal, metal–ceramic, and ceramic–ceramic FGMs, demonstrating that engineered gradients enhance wear resistance, friction stability, lubrication efficiency, and surface durability under dynamic loading. Optimised metal–ceramic FGMs achieve up to an 87

This paper presents a cloud-native cyber-physical CNC manufacturing framework that integrates collaborative CNC operation with a tool wear condition monitoring (TWCM) case study for supervisory decision support. The framework combines edge–cloud connectivity, secure data exchange, and data-driven analytics to enable condition-informed scheduling and coordination across distributed manufacturing resources. As a representative analytics service, a vibration-based TWCM pipeline is implemented for CNC turning, where laser Doppler vibrometry signals are decomposed using variational mode decomposition (VMD) and processed through an AutoML-based regression workflow to estimate flank wear. Tool-health outputs are published to the cloud and linked to supervisory scheduling logic, supporting actions such as tool-change recommendations and job-queue updates. The system is demonstrated on CNC turning of AISI 1045 steel using carbide inserts in a multi-node edge–cloud testbed. The results show accurate wear estimation and illustrate how tool-health intelligence can be integrated into a collaborative cloud environment for decision support. The proposed framework does not implement autonomous machine-level closed-loop control, but provides an extensible pathway toward condition-aware CNC collaboration aligned with Industry 4.0 principles.

Sustainable production strategies are becoming more essential in the leather industry to minimize environmental impact and enhance process efficiency. The proposed study investigates the comparative analysis of ultrasonic assisted machining and CO2 laser assisted machining for leather cutting focusing on sustainable leather processing. In recent times the ultrasonic cutting has emerged as a promising alternative for precision leather cutting. This technique makes use of high frequency vibrations to cut through leather materials with minimum resistance that improves edge quality and significantly reduces the material waste. The cutting trials were carried out on buffalo leather with a thickness of 1.4 mm. The surface roughness and kerf width were analyzed as a key process parameter for this investigation to produce optimal input parameters. The proposed study also explores image processing techniques to quantify surface roughness. Experimental results in leather cutting demonstrate that ultrasonic cutting was performed by varying delay time of (0.1-0.4 s), cutting time (0.02-0.12 s) and shaking time (0.02-0.08 s) significantly reduces thermal damage and maintaining average surface roughness of 0.008 μm and narrow kerfwidth of 0.2899 mm. CO2 laser cutting was carried out by varying power (20-30 W), cutting speed (10-30 m/min) and Standoff Distance (1.5-1.9 mm) produces significant thermal damage evident by carbonization at cut edges with an average surface roughness of 0.012 μm and kerf width of 0.1391 mm. The ultrasonic cutting consumes less energy compared to laser machining resulting in lower overall emissions and significantly reduced carbon footprint. As sustainability becomes an essential concern in the global industrial sector this study highlights the benefit and drawbacks of each technique emphasizing the adaptability of ultrasonic machining for greener industrial practices. These findings contribute to the sustainable manufacturing by demonstrating the potential of ultrasonic cutting for cleaner more efficient leather processing with reduced environmental impact.

This paper considers a distributed decision-making approach for manufacturing task assignment and condition-based machine health maintenance. Our approach considers information sharing between the task assignment and health management decision-making agents. We propose the design of the decision-making agents based on Markov decision processes. The key advantage of using a Markov decision process-based approach is the incorporation of uncertainty involved in the decision-making process. The paper provides detailed mathematical models along with the associated practical execution strategy. In order to demonstrate the effectiveness and practical applicability of our proposed approach, we have included a detailed numerical case study that is based on open source milling machine tool degradation data. Our case study indicates that the proposed approach offers flexibility in terms of the selection of cost parameters and it allows for offline computation and analysis of the decision-making policy. These features create and opportunity for the future work on learning of the cost parameters associated with our proposed model using artificial intelligence.

Fault-tolerant control of industrial robotics network and highly connected machines setup for manufacturing in a production line is crucial. In this paper, we will discuss the recent advancements in fault-tolerant control strategies for collaborative robots with focus on the communication network faults. Cyber-attacks on robotic cyber physical systems (CPS) in the context of fourth industrial revolution constitutes a threat to the modern production systems which requires real-time detection so that the damage to the physical layer could be avoided. By selecting appropriate features for the deep neural network (DNN), it has been found that, an accuracy of 94.64% can be achieved for classifying malicious attacks. Thus, artificial intelligence (AI) can play a substantial role in securing future industrial manufacturing systems from cyber-threats thus avoiding down time in the production lines and large scale manufacturing operations.

This work proposes an advanced Tool Wear Condition Monitoring (TWCM) approach that integrates machine learning with variational mode decomposition to predict tool wear progression during turning. AISI 1045 steel was machined using a TNMG carbide insert. The generated vibration signals were acquired and analyzed using variational mode decomposition to extract correlations between tool wear behavior and machining dynamics. An AutoML approach was applied to identify VMD modes with strong correlations with flank wear (VB). Power spectral density (PSD) analysis was then performed on the selected modes to capture frequency variations induced by tool wear. Several machine learning models, including an ensemble model, were trained using the extracted features. AutoML qualified two VMD modes as highly correlated with flank wear. The ensemble model achieved an R² value of 0.98, demonstrating the predictive capability. The integrated approach accurately predicts flank wear from vibration signals, confirming its effectiveness for tool wear monitoring. The findings also highlight the benefits of ensemble learning for achieving accurate predictions.

Fault detection is crucial for ensuring the safety, reliability, and efficiency of additive manufacturing systems. This involves detecting faults in both the physical components (e.g. sensors, actuators) and the network infrastructure that connects them. Monitoring and analyzing data from various sensors on the robotic production system can help detect anomalies using a digital twin. Irregularities in sensor readings can indicate hardware malfunctions or physical faults. An effective fault detection strategy in industrial robotic CPS requires a combination of sensor data analysis, redundancy, modeling, machine learning, network monitoring, and cyber security measures. This paper describes an integration approach to consider digital twin based monitoring system for 3D printer which can improve system reliability, and ensure the safe and efficient operation of manufacturing systems in the perspective of industry 4.0. Utilizing data analytics and machine learning algorithms within the digital twin to forecast potential failures can allow proactive maintenance schedule by avoiding downtime. Moreover, closed loop monitoring of the work piece is possible through camera and sensor feedback to modify G-code in real time to compensate manufacturing imperfections.

Our extensive history of embracing AI technological advances demonstrates that AI may be a useful tool if humans learn to use it intelligently, and that concerns about it replacing human occupations may be unwarranted. Indeed, a range of remarkable new AI approaches are fast transforming diverse human experiences and fundamentally disrupting our lives, but not without some drawbacks. This study reflects on how new engineers view AI’s influence on trust and ethical attitudes. Data-driven perceptions drive educated debates, education initiatives, and legislative decisions aimed at effectively addressing non-scientific AI concerns. This contributes to improving the future of AI-based learning through transdisciplinary research that considers the evidence of ethical challenges raised by AI misapplication. Our analysis of quantitative data from a survey of 715 recently graduated engineers from diverse fields, who often use information technologies, reveals that many believed AI-related difficulties were scientifically uncertain. According to this study’s findings, the observed variance in the trend relating to reduced fear of job losses due to AI (R2 = 0.1121) suggests that specialties heavily impacted by crucial decision making have a lower level of fear. This provides strong evidence for an optimistic path to AI breakthroughs boosting the level of confidence in and acceptance of AI across many industries.

Laser powder bed fusion (LPBF) is a pivotal additive manufacturing process characterized by selective melting and solidification of powder layers to form complex parts. The microstructure of the final part plays a crucial role in determining its properties, which are heavily influenced by process parameters such as laser power, scanning speed, powder layer thickness, and material properties. Understanding the influence of process parameters on melt pool geometry is crucial for minimizing defects due to unmelted powder regions and optimizing hatch spacing. Additionally, the microstructure plays a vital role, as process parameters can lead to the formation of columnar grains, promoting anisotropy in the fabricated parts. Therefore, understanding microstructural characteristics is key to ensuring the reliability of manufactured parts. Predicting the microstructure and melt pool geometry in laser powder bed fusion (LPBF) processes via finite element modeling is computationally demanding and time-intensive. To mitigate these challenges, we propose the development of a machine learning model capable of accurately predicting melt pool geometry and microstructure based on process parameter variation, thereby reducing time in the identification of optimal process parameters for further experimental investigation. This study utilizes a physics-based numerical model within ANSYS Additive to predict melt pool geometry and microstructure in the LPBF process. The model's accuracy was validated using singletrack metal additive manufacturing (AM) experiments extracted from relevant literature. A comprehensive database was compiled by numerical simulation, including melt pool geometry, microstructure, process parameters, and material IDs. This dataset was then employed to train a data-driven model, with process parameters and material IDs serving as inputs and melt pool width, depth, and microstructure images as outputs. Additionally, the images were resized to 100x200 pixels for computational efficiency. Various neural network architectures, including Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN), were utilized to enhance the predictive capabilities of the model. These architectures were trained and fine-tuned to achieve precise predictions that closely matched the actual dataset. The study showcased the efficiency of neural networks in accurately predicting melt pool geometry and microstructure, significantly reducing prediction times compared to conventional numerical simulations by several orders of magnitude. Future research direction will focus on incorporating physics constraints into the loss function to accelerate the training process, thereby reducing the number of epochs required. However, the development of a rapid data-driven model with minimal mean absolute error is vital for enhancing part optimization and promoting the widespread adoption of MAM.

Metal additive manufacturing (MAM) processes have revolutionized manufacturing and design, offering unprecedented freedom to create intricate and complex parts. Research has demonstrated that in laser powder bed fusion (LPBF) of metal additive manufactured parts, the microstructure and surface can be influenced by various process parameters. However, the influence of laser pulse parameters in LPBF remains relatively unexplored. Laser pulse parameters significantly affect the microstructure and melt pool evolution in metal powder bed additive manufacturing processes. Control over these variations is crucial for achieving desired material properties and part quality. Adjustments in pulse parameters, such as power, width, and interval, can alter grain size, orientation, and subcellular structure, thus impacting mechanical properties. Moreover, optimizing laser energy density by controlling pulse parameters can mitigate defect formation, enhancing density and mechanical properties. Hence, exploring precise control over pulse width and interval during manufacturing contributes significantly to achieving high-quality components. In this study, a meso-scale numerical model was employed to investigate the influence of pulse parameters, such as pulse width and interval on the thermal history and melt pool evolution in LPBF. The physics-based model incorporates key phenomena such as heat transfer via radiation & convection, phase change, recoil pressure, and density-driven melt pool flow. These physical phenomena play a crucial role in the surface finish and microstructure of fabricated parts, affecting the formation of defects such as balling, keyhole, and spattering. A discrete element model (DEM) was employed to construct the powder bed, while the finite volume method (FVM) simulated the thermal-fluid behavior using an initial condition derived from an STL file. Validation of the numerical model against existing literature has confirmed its capacity to accurately predict melt pool behavior, including its influence on surface roughness, as well as temperature distribution and cooling rates across different laser source pulse width and interval settings. Additionally, it can also pave the way for future research directions, including the exploration of in situ hybrid processes involving multiple lasers for surface processing and enhancement. The evolution of computational models promises to facilitate more sophisticated control strategies, ultimately enhancing outcomes and efficiency in metal additive manufacturing processes, paving the way for tailored and optimized LPBF MAM parts.

In the realm of additive manufacturing, powder bed fusion (PBF) is recognized as an innovative and highly effective technique for manufacturing titanium-based materials. With an understanding of and regulation for the complex microstructural evolution that occurs during the PBF process, several previous studies have been conducted to enhance the reliability, efficiency, and performance of the PBF through investigation and optimization of microstructure evolution in PBF. These studies have examined various aspects, including feedstock materials, process parameters, and post-processing techniques, in order to gain a comprehensive understanding and control over the progression of microstructure in the PBF. Process parameters are widely acknowledged as critical determinants in the PBF process for titanium-based materials, significantly influencing the quality in 3D printed components. Previous studies have provided an in-depth discussion of the effects of process parameters, such as laser power, scanning speed, and hatching space, on the microstructure evolution of Ti-based materials. The primary objective of this review paper is, therefore, to provide a comprehensive and clear explanation of recent efforts, with a particular focus on investigating the complex evolution of microstructures in Ti-based materials during the PBF process. This thorough discussion is devoted to providing a comprehensive understanding of the effects of process parameters on the evolution of microstructures in Ti-based materials.

Among several kinds of metal matrix composite materials (MMCs), such as silicon-based reinforced aluminum matrix (SiCp/Al) composites have become the most valuable composite material due to their various applications in industries, sports equipment, electrons, and automotive. Due to the presence of hard ceramic reinforcements, the SiCp/Al composite is considered a difficult-to-cut material, which leads to significant hindrances in machining operations together with increased tool wear, cutting force, and degradation of machined surface quality. The present review is focused on the recent advancements in turning process of metal matrix composites. An attempt is made to comprehensively analyze and identify the influencing factors on the machinability of metal matrix composites (MMCs). The main purpose of this review is to cover the topics such as the recent trends in turning and hybrid turning processes of MMCs, tool wear and its mechanisms, tool selection, the effect of cutting parameters, surface integrity, SiCp/Al composite properties and reinforcement effect, chip formation mechanisms, and different modeling approaches used in particle-reinforced MMCs machining process. Finally, some research gaps and future directions are suggested that could lead to efficient machining of particle-reinforced MMCs.

This comprehensive review aims to achieve and develop an automated platform to acquire and process data for error compensation in CNC manufacturing. The Industrial Internet of Things (IIoT) is of interest in manufacturing and cloud manufacturing. An IIoT-based condition monitoring solution with multiple sensors can predict the potential variability of the part specifications in the in-process or possible defects by acquiring large data to be processed with prognostic tools in the cloud server. This includes data acquisition and management from various sensors in progress, data collection, and data transfer to the cloud. The latter may include warnings or altering some operating parameters, for example, spindle speed, feed rate, and depth of cut. This paper discusses monitoring techniques and includes a comprehensive description of machining process parameters and their ranges of variability based on experimental results extracted from the studies on materials under several process conditions for both milling and turning processes. This literature review assists in identifying appropriate sensors and their respective ranges of operations. Furthermore, the relationship between the process and monitoring parameters is further studied to understand the parameter selection and combination better. This development will serve efficiently as a cyber-physical production system under cloud manufacturing management and can be extended to non-I4.0-ready machines via IIoTs.

Ultraprecision positioning to better than nanometer in accuracy is of a great interest in various applications. The paper reports on recent pre-rolling experimental measurements of the moment friction during the phase of pre-rolling, i.e., at the start and at the finish. To discuss the case properly, the experiments are set for pure rolling without any external effect nor any slippage at the contact patch. The pendulum was designed to secure pure pre-rolling without slippage. Beside the fact that the experiments have shown extremely low values of rolling coefficients in the order of (x 10-7), it is possible to establish the appearance of real hysteresis curves showing the dependance of the friction moment on the displacement. The sensitivity of the measurement of rolling friction moments is in the order of 10-10 Nm, the measurement error does not exceed 10% with 125 nm minimum displacement of the ball peel adhesion density evaluated as 2.2 x 10-4 J/m2.

The development results of a single-point contact system set up as a pendulum to study the laws of rolling resistance to contacting bodies at a distance significantly reduced compared to the elastic contact spot size. The designed device uses a physical pendulum sustained by only one ball on a flat polished surface. The problem of stability of the pendulum swing plane is solved. A phenomenological theory of rolling resistance is described. The surface tension of solids on the contact zone, parameters of the frequency-independent internal friction and the pressure of the adhesion forces are found.

Ultra-precision positioning to better than nanometer in accuracy with low level of vibration is of great interest to various applications such as actuators for precision positioning of mobile stages in CNC machines, robotics in medical applications towards large scale devices like very large telescopes (VLT). The paper reports on recent pre-rolling experimental measurements of moment friction during the phase of pre-rolling i.e., at start and at finish. The development resulted in a single-point contact system set up and double points contact setup in a form of a pendulum generating pure pre-rolling and to study the laws of rolling resistance to contacting bodies at a distance significantly reduced compared to the elastic contact spot size. Phenomenological effects have been observed during both extensive tests. It is noted that the single point pendulum posed several technical challenges to secure perfect balancing and hence allow for pre-rolling to occur and precisely measured characteristics. The surface tension of solids on the contact zone, parameters of the frequency-independent internal friction and the pressure of the adhesion forces are found. The sensitivity of measuring rolling friction moments is of the order of 10-10 Nm, the measurement error does not exceed 10% with for example 120 nm minimum displacement of the ball Peel adhesion density extremely low. The swing of the pendulum with a maximum period has a stable swing plane. In the deep pre-rolling (DPR) zone before full rolling, there is an effect of a sharp decrease in the swing period of the pendulum with a decrease in the swing amplitude. In this case, the rolling friction also decreases and tends to its minimum final value, determined by the work of adhesion forces on separation. In the study of rolling resistance in the DPR zone, it is necessary to measure not only the reliance of the swing amplitude on the time, but also the reliance of the swing period of the pendulum on time. The developed phenomenological theory and measurement procedure allowed us for the first time to build a simple instrument for direct measurements with high sensitivity and accuracy of the surface energy density of the adhesion forces (or surface tension) in the case of a solid body, and the parameters of internal frequency-independent friction and the pressure generated by adhesion forces.

In the last interim guidance, the WHO advised the use of masks in communities, during home care, and in healthcare settings in areas with reported cases of COVID-19. This advice was intended for individuals in the community, public health and infection prevention and control (IPC) professionals, healthcare managers, healthcare workers (HCWs), and community health workers. As the two primary routes of transmission of the COVID-19 virus are respiratory droplets and contact, face masks have become potential safety tools in public places. Subsequent contact with the face, eyes, nose, and mouth following contamination is detrimental. However, during this pandemic, physicians and nurses have suffered the consequences of wearing face masks for several hours. Therefore. a full-face mark, which ensures filtered breathing during the day, is critical. The proposed smart mask model protects the respiration of mask wearers and monitors their body temperature, sneezing attacks, and social distancing. Moreover, it registers their geographical location after ensuring ID registration. The proposed mask can be used both indoors and outdoors. Moreover, data can be processed locally for alarms related to temperature and social distancing. The remaining data are sent to a cloud for post-processing to record the histories of mask wearers in all parameters, including their geographical dynamic location, to track the possible spread of contamination. The prototype and measurement results demonstrate the practicality and potential utility of mass numbers.

This review is an attempt to explore the challenges that need to be addressed to fully utilize the potential of ceramic-based functionally graded cutting tools (FGCTs). The various aspects covered in the review include the most recent experimental and numerical work related to FGCTs, the current research trends and the need for these tools, the identification of potential material combinations, synthesis techniques and their limitations, and finally a presentation of the most recent work. To find general tribological performance, various wear mechanisms involved in the cutting process are explored. Some recent experimental and numerical works related to the self-lubricating phase in functionally graded structure and the need for self-lubricating ceramic tools, identifying potential high-temperature solid lubricants, and their limitations are also discussed. More recent and dominating fabrication methods are also discussed in detail along with a brief review of some promising methods. The implementation of numerical modeling and computational frameworks validated through experiments is found to lead to the design and development of cost-effective and efficient FGCTs. Finally, some research gaps are identified and future directions for innovative FGCT materials are proposed.

The potential impacts of machine learning and artificial intelligence (AI) on society are receiving increased attention owing to the rapid growth of these technologies during the fourth industrial revolution. Thus, a detailed analysis of the positive implications and drawbacks of AI technology in human society is necessary. The development of AI technology has created new markets and employment opportunities in vital industries, including transportation, health, education, and the environment. According to experts, the rapidly increasing improvements in AI will continue. As part of humankind's continual efforts to create more prosperous technological growth, automation and AI are changing people's lives and are widely considered to be game-changers in a variety of industries. This study presents a review of how automation and AI may affect businesses and jobs. To determine some of the prospective long-term consequences of AI on human civilisation, this study investigates a variety of connected primary impacting potentials, including job losses, employees' well-being, dehumanisation of jobs, fear of AI, and examples of autonomous technology developments, such as autonomous-vehicle challenges. A diverse methodology of narrative review and thematic pattern was used to add to transdisciplinary or multidisciplinary work, particularly in the theoretical development of AI technologies.

Global competitiveness creates a challenge for manufacturing companies to maintain their market share with dynamic customer requirements. Capital investment in machinery does not allow facility expansion to accommodate large orders from customers but to reconfigure the manufacturing enterprise. Distributed manufacturing (DM) is embraced in order to increase facility utilization by decentralizing production. An enterprise in charge of a DM network allows customers to choose the best manufacturers available for their order based on their track record, which is available through historical and online performance data. Furthermore, manufacturers as members of this network may receive orders based on their past performance. Industry 4.0 with all necessary Industrial Internet of Things (IIoT) enables the online monitoring of production key parameters of manufacturers subscribed to a DM network. We develop a new network model of manufacturers teamed under specific terms and conditions to support a group of customers who have specific needs. The proposed model, known as the continuous supervised model, is created with the ARENA simulation software. We demonstrate the effectiveness of our model by contrasting it with the standard practice approach. To ensure the best possible performance, we continuously monitor the cost, quality, delivery time, and production rate indicators of the various manufacturers and update their performance ranking for current and future orders. Furthermore, using the analytic hierarchy process (AHP) approach, a single performance measure based on the four indicators is developed. Implementing the proposed model showed an improvement in the average performance by 51.3%.