In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system.

Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their pivotal role lies in augmenting tool lifespan, minimizing machine downtime, and elevating productivity, thereby contributing to industry growth. However, the efficacy of CNC machine TCM hinges upon multiple factors, encompassing system type, data precision, reliability, and adeptness in data analysis. Globally, extensive research is underway to enhance real-time TCM system efficiency. This review focuses on the significance and attributes of proficient real-time TCM systems of CNC turning centers. It underscores TCM’s paramount role in manufacturing and outlines the challenges linked to TCM data processing and analysis. Moreover, the review elucidates various TCM system variants, including cutting force, acoustic emission, vibration, and temperature monitoring systems. Furthermore, the integration of industrial Internet of things (IIoT) and machine learning (ML) into CNC machine TCM systems are also explored. This article concludes by underscoring the ongoing necessity for research and development in TCM technology to empower modern intelligent industries to operate at peak efficiency.

This comprehensive review explores recent developments in Proton Exchange Membrane Fuel Cells (PEMFCs) and evaluates their alignment with the ambitious targets established by the U.S. Department of Energy (DOE). Notable advancements have been made in developing catalysts, membrane technology advancements, gas diffusion layers (GDLs), and enhancements in bipolar plates. Notable findings include using carbon nanotubes and graphene oxide in membranes, leading to substantial performance enhancements. Innovative coatings and materials for bipolar plates have demonstrated improved corrosion resistance and reduced interfacial contact resistance, approaching DOE targets. Nevertheless, the persistent trade-off between durability and cost remains a formidable challenge. Extending fuel cell lifetimes to DOE standards often necessitates higher catalyst loadings, conflicting with cost reduction objectives. Despite substantial advancements, the ultimate DOE goals of USD 30/kW for fuel cell electric vehicles (FCEVs) and USD 600,000 for fuel cell electric buses (FCEBs) remain elusive. This review underscores the necessity for continuous research and innovation, emphasizing the importance of collaborative efforts among academia, industry, and government agencies to overcome the remaining technical barriers.

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

Tool condition monitoring (TCM) is crucial in the machining process to confirm product quality as well as process efficiency and minimize downtime. Traditional methods for TCM, while effective to a degree, often fall short in real-time adaptability and predictive accuracy. This research work aims to advance the state-of-the-art methods in predictive maintenance for TCM and improve tool performance and reliability during the milling process. The present work investigates the application of Deep Learning (DL) and Reinforcement Learning (RL) techniques to monitor tool conditions in milling operations. DL models, including Long Short-Term Memory (LSTM) networks, Feed Forward Neural Networks (FFNN), and RL models, including Q-learning and SARSA, are employed to classify tool conditions from the vibration sensor. The performance of the selected DL and RL algorithms is evaluated through performance metrics like confusion matrix, recall, precision, F1 score, and Receiver Operating Characteristics (ROC) curves. The results revealed that RL based on SARSA outperformed other algorithms. The overall classification accuracies for LSTM, FFNN, Q-learning, and SARSA were 94.85%, 98.16%, 98.50%, and 98.66%, respectively. In regard to predicting tool conditions accurately and thereby enhancing overall process efficiency, SARSA showed the best performance, followed by Q-learning, FFNN, and LSTM. This work contributes to the advancement of TCM systems, highlighting the potential of DL and RL techniques to revolutionize manufacturing processes in the era of Industry 5.0.

A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers.