A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers.

The brake system requires careful attention for continuous monitoring as a vital module. This study specifically focuses on monitoring the hydraulic brake system using vibration signals through experimentation. Vibration signals from the brake pad assembly of commercial vehicles were captured under both good and defective conditions. Relevant histograms and wavelet features were extracted from these signals. The selected features were then categorized using Nested dichotomy family classifiers. The accuracy of all the algorithms during categorization was evaluated. Among the algorithms tested, the class-balanced nested dichotomy algorithm with a wavelet filter achieved a maximum accuracy of 99.45%. This indicates a highly effective method for accurately categorizing the brake system based on vibration signals. By implementing such a monitoring system, the reliability of the hydraulic brake system can be ensured, which is crucial for the safe and efficient operation of commercial vehicles in the market.

Digital Twin-Driven Tool Condition Monitoring for the Milling Process.

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.

Comparison of Tool Wear, Surface Roughness, Cutting Forces, Tool Tip Temperature, and Chip Shape during Sustainable Turning of Bearing Steel.

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.

Dynamic Behavior Modeling of Natural-Rubber/Polybutadiene-Rubber-Based Hybrid Magnetorheological Elastomer Sandwich Composite Structures.

This study investigates the dynamic characteristics of natural rubber (NR)/polybutadiene rubber (PBR)-based hybrid magnetorheological elastomer (MRE) sandwich composite beams through numerical simulations and finite element analysis, employing Reddy's third-order shear deformation theory. Four distinct hybrid MRE sandwich configurations were examined. The validity of finite element simulations was confirmed by comparing them with results from magnetorheological (MR)-fluid-based composites. Further, parametric analysis explored the influence of magnetic field intensity, boundary conditions, ply orientation, and core thickness on beam vibration responses. The results reveal a notable 10.4% enhancement in natural frequencies in SC4-based beams under a 600 mT magnetic field with clamped-free boundary conditions, attributed to the increased PBR content in MR elastomer cores. However, higher magnetic field intensities result in slight frequency decrements due to filler particle agglomeration. Additionally, augmenting magnetic field intensity and magnetorheological content under clamped-free conditions improves the loss factor by from 66% to 136%, presenting promising prospects for advanced applications. This research contributes to a comprehensive understanding of dynamic behavior and performance enhancement in hybrid MRE sandwich composites, with significant implications for engineering applications. Furthermore, this investigation provides valuable insights into the intricate interplay between magnetic field effects, composite architecture, and vibration response.

Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning.

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.

Development of Online Tool Wear-Out Detection System Using Silver-Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations.

This paper deals with the design and development of a silver-polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver-polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability.

Risk factors and prevalence of work-related musculoskeletal disorders in metropolitan bus drivers: An assessment of whole body and hand-arm transmitted vibration.

BACKGROUND: Metropolitan bus drivers have higher prevalence of work-related musculoskeletal disorders (WMSDs) due to their nature of work and working environment. OBJECTIVE: To identify the prevalence of WMSDs and associated risk factors and to conduct real-time testing to evaluate Whole Body Vibration (WBV) and Hand-Arm Vibration (HAV) in buses based on the ISO standards to assess the vibrations levels at different speeds. METHODS: Participants in this study were 370 full-time male bus drivers from the north and south zones of 13 depots of Bengaluru Metropolitan Transport Corporation (BMTC), Bengaluru, south India. Information regarding WMSDs symptoms during the previous 7 days and 12 months were collected by Modified Nordic Musculoskeletal Questionnaire (MNMQ). WBV and HAV testing was performed and vibration levels were compared with ISO-2631-1 (1997) and ISO-5349-1-2001 standards. It was found that 68.7% of participants reported WMSDs. RESULTS: Several individuals and work-related factors were found to be statistically significant with WMSDs. From the Gini impurity measure, vibration and road types (Asphalt pavement and Rough road) were considered as vital risk factors associated with WMSDs. CONCLUSION: From the WBV and HAV evaluations, it was found that for buses on asphalt pavement at > 60 km/h, the vibration level was higher compared to a lower speed. The vibration level exceeded the Exposure Action Value (EAV) on rough roads at all speeds (20km/h, 40km/h and 60km/h) and in several situations considered based on assumptions the vibration level exceeded the Exposure Limiting Value (ELV).

Occupational physical risk factors and prevalence of musculoskeletal disorders among the traditional lacquerware toy makers of South India.

BACKGROUND: The majority of handicraft workers in India falls under the informal sector, which plays a prominent role in the employment generation. Artisans in handicraft sectors encounter various hazards and risks causing occupational diseases. OBJECTIVE: The key objective of the study is to identify the prevalence of musculoskeletal disorders and occupational risk factors among the artisans involved in making traditional lacquerware toys in Karnataka and Andhra Pradesh, South India. METHODS: The subjects considered in this study are 177 artisans who work in mechanized lathes at Channapatna of Karnataka and Etikoppaka of Andhra Pradesh, South India. The information regarding the reported work-related musculoskeletal disorders (WMSD) symptoms from 7 days to 12 months are collected through modified Standardized Nordic Questionnaire and by direct observations. Moreover, the intervention of WMSD in their day-to-day life and the overall comfort of their body are also determined. The questionnaire survey is conducted through face-to-face interviews and by direct field study. RESULTS: From the statistical analysis, it is found that about 76.83%of the study population (77.4%male and 74.28%female) has self-reported WMSDs. The prevalence of WMSD is most common in the age group of 30-40 years. Physical factors like workplace adaptability, stress at work, body postures, health status, body mass index, active and enough breaks during work and body condition at the end of work have a significant association with WMSD. CONCLUSION: In this study, many of the work-related and lifestyle/health-related factors show a significant association with WMSD in artisans. The sub-standard working environment and the nature of work expose artisans to many occupational risks in their day-to-day life. To mitigate the occupational risks and musculoskeletal disorders, the workspace needs to be redesigned ergonomically.