Machine learning for monitoring hobbing tool health in CNC hobbing machine

TYPE Original Research PUBLISHED 12 April 2024 DOI 10.3389/fmats.2024.1377941 OPEN ACCESS EDITED BY Mijia Yang, North Dakota State University, United States REVIEWED BY Prattasha Saha, North Dakota State University, United States Farzad Pashmforoush, University of Maragheh, Iran *CORRESPONDENCE Sachin Salunkhe, [email protected] RECEIVED 28 January 2024 ACCEPTED 25 March 2024 PUBLISHED 12 April 2024 CITATION Tambake N, Deshmukh B, Pardeshi S, Mahmoud HA, Cep R, Salunkhe S and Nasr EA (2024), Machine learning for monitoring hobbing tool health in CNC hobbing machine. Front. Mater. 11:1377941. doi: 10.3389/fmats.2024.1377941 COPYRIGHT © 2024 Tambake, Deshmukh, Pardeshi, Mahmoud, Cep, Salunkhe and Nasr. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine learning for monitoring hobbing tool health in CNC hobbing machine Nagesh Tambake1, Bhagyesh Deshmukh1, Sujit Pardeshi2, Haitham A. Mahmoud3, Robert Cep4, Sachin Salunkhe5,6*and Emad Abouel Nasr6 1Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, India, 2Department of Mechanical Engineering, COEP Technological University, Pune, India, 3Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia, 4Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, Czechia, 5Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India, 6Gazi University Faculty of Engineering, Department of Mechanical Engineering, Maltepe, Türkiye Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45rpm), feed (ranging from 0.6 to 1mm/rev), and depth of cut (6.8mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when Frontiers in Materials 01 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise. KEYWORDS machine learning approach, condition monitoring, hobbing cutter, failure mode effect analysis (FMEA), hyperparameter optimization, CNC hobbing machine 1 Introduction The ever-increasing demands for precision and efficiency in the manufacturing industry necessitate continuous advancements in machining processes. Gear hobbing, a critical process for producing high-quality gears, is often hampered by tool wear, leading to reduced product quality and costly downtime. Traditional monitoring methods, relying on manual inspection or basic thresholding, lack the accuracy and real-time capabilities needed for optimal process control. Over the past few years, machine learning (ML) has become a potent instrument for monitoring conditions, promising to transform gearhobbing operations significantly. Several researchers have explored the application of ML to monitor hobbing cutter conditions, employing various sensor data and algorithms. Vibration signals are widely used due to their sensitivity to tool wear and ease of acquisition. Acousticemission(AE)sensorsprovidevaluableinsightsintothemicrofracture process within the tool, further enhancing wear estimation accuracy. Additional data sources like cutting force, spindle current, and temperature are also being integrated better to understand the tool’s health (Zhangetal., 2021). Feature extraction is a critical step in the successful application of machine learning (ML). Various methodologies have been employed by researchers to analyse sensor data, utilizing techniques in the time domain (such as RMS, standard deviation, and peak-to-peak), frequency domain (FFT), and timefrequency domain (wavelet transform) to extract relevant information (Gauderetal.,2023). AwiderangeofMLapproacheshasbeenexplored for monitoring the condition of hobbing cutters. Significant progress has been made in predictive models for tool wear and remaining useful life (RUL), which make use of supervised learning algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), and random forests. Additionally, unsupervised learning algorithms like K-means clustering and principal component analysis (PCA) aid in anomaly detection and fault diagnosis (Rahomaetal., 2023). The increasing popularity of deep learning techniques, including convolutional neural networks (CNNs) and deep belief networks (DBNs), is attributed to their ability to automatically extract features and identify complex relationships within the data (Lietal., 2023). Research is continuously pushing the boundaries of ML-based hobbing cutter condition monitoring. Ensemble learning methods that combine multiple ML algorithms are being explored to improve accuracy and robustness. Transfer learning leverages knowledge from other domains to accelerate model training and enhance performance. Additionally, integrating physics-based models with ML algorithms offers a deeper understanding of the tool wear process, leading to more accurate predictions (Tambakeetal., 2021). Despite significant progress, challenges remain. The need for standardized datasets and the high cost of data acquisition are key hurdles. Moreover, the complex and non-linear nature of the hobbing process poses challenges in developingaccurateand robust MLmodels.Black-boxmodelslikedeep neuralnetworksalsoraiseconcernsregardinginterpretability,hindering theirpracticalapplicationinindustrialsettings(Przybyś-Małaczeketal., 2023). Several successful industrial implementations demonstrate the potential of ML-based condition monitoring systems. Siemens and Sandvik Coromant have developed systems that utilize sensor data and ML algorithms to predict tool wear, optimize cutting parameters, and improve tool life in gear hobbing operations (Hameedetal., 2023). ML offers a promising avenue for advancing hobbing cutter condition monitoring. Researchers are steadily improving the accuracy and reliability of these systems by leveraging various sensor data, feature extractiontechniques,anddiverseMLalgorithms.Asresearchcontinues to address remaining challenges and explore new advancements, ML-based condition monitoring is poised to revolutionize the gear manufacturing industry by maximizing efficiency, productivity, and product quality. (a) Objectives of the research Work: • To develop a resilient and effective machine learning (ML) approach for monitoring the condition of hobbing cutters in CNC hobbing machines. • To investigate the vibration’s time-domain response and employ statistical modelling of signals in the time domain for fault diagnosis. • Address the risk of overfitting in the ML models by carefully considering model complexity, employing regularization parameters, and conducting thorough model evaluation using separate test sets. (b) Scope of the Research work: • The research encompasses the development of a comprehensive framework for condition monitoring, including data acquisition, feature extraction, selection, and scaling, as well as the training and evaluation of classifier models using different ML algorithms. • The scope also involves examining the variety of dataset compositions and using appropriate methods to effectively explore the parameter space and identify the best settings for a specific problem. 2 Literature review The importance of tool condition monitoring (TCM) literature in manufacturing is immense, as it drives advancements in predicting tool wear and optimizing machining processes. This literature review summarizes key findings and methodologies from various sources. Zeng etal. (2021) proposed a novel TCM approach using multi-sensor data fusion imaging and attention mechanisms, leveraging advanced sensing technologies to improve monitoring accuracy (Zengetal., 2021). Li etal. (2020) introduced a Random Forests algorithm-based fault diagnosis method for centrifuges, providing valuable insights into machine learning Frontiers in Materials 02 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 techniques relevant to TCM (YangandShami, 2020). Klocke etal. (2016) presented an online tool wear measurement system for hobbing highly loaded gears, focusing on real-time measurement during the hobbing process to tackle gear manufacturing challenges (Fritzetal., 2016). Zhang etal. (2014) proposed a tool wear model using least squares support vector machines and Kalman filter, contributing significantly to predictive models for tool wear (Zhangetal., 2014). Wang etal. (2021) developed a technique to forecast hobbing tool wear by leveraging CNC real-time monitoring data and applying deep learning, demonstrating deep learning methods for predicting tool conditions (Wangetal., 2021). Liu etal. (2019) focused on predicting the remaining useful life of cutting tools through support vector regression, introducing a modelling approach for estimating tool lifespan (Liuetal., 2019). Humayun etal. (2022) explored transfer learning with convolutional neural networks, showcasing the adaptability of machine learning techniques across domains (Humayunetal., 2022). Jia etal. (2022) presented a real-time wear monitoring system for hob cutters based on statistical analysis, improving the understanding of wear patterns and the development of effective monitoring strategies (Jiaetal., 2022). Klocke etal. (2017) introduced a model-based online tool monitoring system for hobbing processes, emphasizing the importance of real-time monitoring in optimizing machining efficiency (Klockeetal., 2017). Wu etal. (2022) suggested an innovative online framework for predicting gear machining quality using ensemble deep regression, presenting an advanced method for quality prediction during gear machining (Wuetal., 2022). Cheng etal. (2020) developed an intelligent prediction model for tool wear in turning high-strength steel, highlighting the application of machine learning in forecasting tool wear under specific machining conditions (Chengetal., 2020). Lee etal. (2019) focused on creating an intelligent tool condition monitoring system to identify manufacturing trade-offs and optimal machining conditions, contributing to the broader understanding of TCM for optimizing machining processes (Leeetal., 2019). Chen etal. (2018) explored tool wear prediction through multi-sensor data and deep belief networks, advancing deep learning techniques in TCM (Chenetal., 2018). Fong etal. (2021) investigated a universal tool wear measurement technique using image-based crosscorrelation analysis, exploring non-traditional methods for tool wear measurement (Fongetal., 2021). Bagri etal. (2021) proposed a method for predicting tool wear and remaining useful life in micro-milling using neural networks, demonstrating the application of neural networks in predicting tool wear in intricate machining processes (Bagrietal., 2021). The reviewed literature advances tool condition monitoring by integrating various sensor technologies, machine learning algorithms, and real-time monitoring strategies, providing valuable insights and methodologies to enhance the accuracy and efficiency of predicting tool wear in machining processes. Despite the promising potential of ML for hobbing cutter condition monitoring, several research gaps remain. These gaps include: •The scarcity of data poses a challenge: The hobbing industry frequently needs more high-quality datasets, which are essential for training and assessing machine learning models. •Development of robust ML models: Robust ML models that can handle the complex data generated during the hobbing process are needed. •Integration with CNC hobbing machines: Effective integration of ML models with existing machines is required for real-time monitoring and closed-loop control. •Standardization and validation: Standardization of data collection and model evaluation methodologies is necessary for wider adoption of ML in the hobbing industry. This study aims to fill existing gaps by creating a resilient and effective machine learning (ML) approach for monitoring the condition of hobbing cutters. This endeavor is expected to enhance the quality of gears, lower production expenses, and boost efficiency in the hobbing process. 3 Failure mode and effect analysis (FMEA) of CNC hobbing cutter 3.1 Importance of FMEA for CNC hobbing cutters FMEA is a proactive and systematic approach to identifying, evaluating, and prioritizing potential failures in any system or process. It is particularly valuable for CNC hobbing cutters due to their critical role in precision machining and the potential consequences of their failure (Parsana and Patel, 2014). Here is why FMEA is essential for hobbing cutters: •Preventative Maintenance: FMEA helps identify potential failure modes like edge chipping, crater wear, and builtup edge before they occur. This allows for proactive maintenance through timely tool changes, lubrication, and parameter adjustments, minimizing downtime and extending cutter life. •Improved Process Optimization: By understanding the causes and effects of different failure modes, FMEA helps optimize cutting parameters and operating conditions. This leads to improved cutting efficiency, reduced tool wear, and higher product quality. •Enhanced Safety and Reliability: FMEA identifies critical failures that could pose safety risks or lead to equipment damage. We can ensure a safer and more reliable hobbing process by prioritizing preventive measures for these high-risk modes. •Cost Reduction: Early detection and prevention of failures through FMEA minimizes downtime, scrap production, and rework costs. This translates to significant cost savings in the long run. •Increased Overall Equipment Effectiveness (OEE): By optimizing hobbing cutter performance and minimizing downtime, FMEA contributes to improving OEE. This metric measures the efficiency, capacity utilization, and quality rate of hobbing operations, highlighting the overall effectiveness of the process. FMEA is a valuable tool for CNC hobbing cutter users. Proactively identifying and addressing potential failures helped Frontiers in Materials 03 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 TABLE 1 FMEA of CNC hobbing cutter. Failure mode Cause Effect on workpiece Severity (S) Probability (O) Detection (D) Risk priority number (RPN) Recommendations Tip & Flank Wear Abrasive wear, high cutting temperatures Reduced cutting efficiency, increased dimensional error, vibration 4 (High) 2 (Low) 4 (High) 32 Implement condition monitoring (vibration, acoustic) and optimize cutting parameters Crater Wear Adhesion of chip material to cutter Increased cutting force, thermal damage, premature wear 4 (High) 3 (Medium) 3 (Medium) 36 Implement condition monitoring, monitor cutting temperature, optimize lubrication, and use proper chip breakers Edge Chipping Shock loading, foreign objects, material defects Catastrophic failure, scrapped workpiece, potential machine damage 3 (Medium) 3 (Medium) 3 (Medium) 27 Implement condition monitoring, use high-quality materials, and improve chip control Built-up Edge High cutting temperatures, improper lubrication Poor surface finish, increased cutting force, accelerated wear 5 (Critical) 2 (Low) 2 (Low) 20 Adjust cutting parameters, improve lubrication, and use coatings Chip Packing Improper chip breaker design, workpiece material, high feed rates Cutter clogging, overheating, tool breakage 3 (Medium) 4 (High) 2 (Low) 24 Optimize chip breaker geometry, adjust feed rates, and consider cryogenic cooling Grinding Cracks Improper grinding technique, material defects Potential for catastrophic failure during cutting 5 (Critical) 1 (Very Low) 1 (Very Low) 5 Use proper grinding techniques, inspect cutters before use, and select appropriate materials Microchipping Fatigue due to repeated loading, high cutting speeds Gradual loss of cutting performance reduced tool life 3 (Medium) 2 (Low) 4 (High) 24 Monitor vibration, optimize cutting parameters, and use wear-resistant materials Gouging Foreign objects, workpiece material defects Deep scratches, dimensional errors, compromised workpiece 4 (High) 3 (Medium) 3 (Medium) 36 Implement condition monitoring and filtration, inspect workpiece material, and use proper clamping Corner Wear High cutting forces, improper setup, corner chamfer design Premature wear, reduced tool life, dimensional inaccuracies 3 (Medium) 2 (Low) 3 (Medium) 18 Optimize clamping, adjust cutting parameters, and consider different corner chamfer designs optimize the hobbing process, minimize downtime and costs, and achieve higher quality and productivity. Table1 shows the FMEA of the CNC Hobbing Cutter (Masciaetal., 2020). The following parameters were calculated in Table1 of FMEA (Parsana and Patel, 2014). •Severity (S): This rating indicates the consequences of a hobbing cutter failure. 1 (No effect) means the failure has negligible impact on the process or product. 5 (Catastrophic failure) signifies a severe event that could cause significant damage, injury, or production disruption. Frontiers in Materials 04 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 •Occurrence (O): This rating estimates the likelihood of the failure occurring. 1 (Unlikely) means the failure is rare and may only occur under exceptional circumstances. 5 (Very likely) indicates the failure is frequent or expected to happen under normal operating conditions. •Detection (D): This rating assesses how easy it is to identify the failure before it leads to serious consequences. 1 (Easy to detect) means the failure is readily apparent through visual inspection, sound, or other indicators. 5 (Difficult to detect) signifies the failure progresses silently or without obvious symptoms until it becomes critical. •RPN (Risk Priority Number): This is a calculated score derived by multiplying Severity, Occurrence, and Detection ratings. Higher RPN implies a greater risk and higher priority for action. This score helps prioritize preventive measures by focusing on failures with the most significant potential impact. (b) Recommendations for Condition Monitoring Systems: The FMEA emphasizes prioritizing failure modes with high RPN scores, as shown in Figure1, and critical impact on production. These failures warrant immediate attention and preventive measures to avoid production delays, quality issues, and equipment damage. Hence, the following types of hobbing cutter failures were considered for developing a condition monitoring system, as shown in Figure2. •Edge Chipping: This can cause a rough surface finish and dimensional inaccuracies in the workpiece. •Crater Wear: This can reduce cutting efficiency and increase cutting forces, impacting tool life and production speed. •Tip & Flank Wear: This can affect cutting accuracy and dimensional tolerances, leading to scrap or rework. •Gouging: This can cause severe damage to the workpiece and require immediate tool replacement. We can significantly improve hobbing cutter life, enhance product quality, and minimize production downtime by implementing the recommended monitoring techniques and prioritizing critical failures based on their RPN scores. 4 Framework for research The Research Framework, depicted in Figure3, comprises a hobbing machine, a data acquisition system, and a computer. The data acquisition system gathers information from the hobbing machine, including spindle speed, feed rate, and vibration data. Subsequently, this data is transmitted to the computer for processing and analysis (Tambakeetal., 2023). The initial stage of the data processing pipeline involves extracting features from the acquired data. Features represent essential characteristics that differentiate various classes. After extraction, the features undergo selection and scaling. Feature selection entails choosing the most pertinent features for the classification or prediction task, while feature scaling involves normalizing them to a uniform scale. The subsequent phase entails training a classifier model, a machine learning model proficient in predicting a new data point’s class based on its features. Seven ML algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) are employed for training based on recommendation by the researchers in tool condition monitoring (Alabdulwahab and Moon, 2020). Once the classifier model is trained, it undergoes evaluation using a separate test set that was not part of the training data. The model’s performance on the test set serves as an estimate of its generalization ability, gauging its effectiveness in predicting classes for new, unseen data points. Suppose the model demonstrates satisfactory performance on the test set. In that case, it becomes eligible for deployment to production, which can be used for realtime predictions of new data point classes. 4.1 Details of experiment This section provides a detailed account of the experimental setup and procedure used for collecting vibration data from the hobbing cutter. The setup, as depicted in Figure4, includes a Premier PHA 400 × 400mm 3 Axes CNC Gear Hobbing Machine, a triaxial accelerometer, and a 16-channel data logger. A computer is utilized to acquire, process, and store the accelerometer data. Figure5 demonstrates the generation of artificial faults on the hobbing cutter, categorized by class. The data is then transferred from software to spreadsheet. To conduct the experiment, a gear blank made of 20MnCr5 is placed on a rotating work-holding table, and a hobbing cutter is secured on the cutter-holding spindle. Vibration usually occurs on both the cutter and workpiece sides during the hobbing process. However, a triaxial accelerometer with a sensitivity of 10.4mV/g is directly attached to the cutter-holding spindle housing using a magnetic material to detect cutter defects. The relationship between the cutter’s rigidity and the vibrations produced during hobbing reveals cataclysmic frequencies exceeding 40.5kHz. This highlights the impact of workpiece rigidity and surface roughness on vibration signals, which are crucial for fault classification during hobbing operations (Alabdulwahab and Moon, 2020). The machining input parameters—speed (35–45rpm), feed (0.6–1mm/rev), and depth of cut (6.8mm)—are selected based on the Taguchi method. Determining the optimal combinations of speed, feed, and depth is essential for CNC operations and is considered standard practice, depending on the specific operation, workpiece material, and other relevant factors. The hobbing process initially involved using a well-functioning cutter, followed by using defective cutters. Cutters featuring pre-existing issues like Crater wear, Chipping, Tip, flank wear, and Gouging were considered. The designations for the cutter condition categories are stated in Table2. 5 Methodology utilizing machine learning The machine learning methodology employed in this research work encompasses various stages, including data processing, feature extraction, selection, and scaling, as well as the training and evaluation of classifier models using different machine learning algorithms. The methodology emphasizes the use of Bayesian optimization for hyperparameter tuning and model evaluation, Frontiers in Materials 05 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 1 Class-wise risk priority number of hobbing cutter. FIGURE 2 Types of hob tool wear (Maiuri, 2009). aiming to develop an effective approach for monitoring the condition of hobbing cutters in CNC hobbing machines. The following subsections detail the data collection processes, feature extraction, selection, and classification. 5.1 Data collection The vibrations generated during machining affect both the cutter spindle and the workpiece. Detecting any alterations in the cutter spindle’s motion records signatures indicative of tool faults. To mitigate any inaccuracy in accelerometer readings, the study strategically placed the accelerometer near the cutter spindle, specifically on the spindle housing, using a magnet. The magnet used to mount the accelerometer near the cutter spindle does not significantly affect the accelerometer readings. The strategic placement of the accelerometer using a magnet on the spindle housing ensures the capture of vibration signals primarily emanating from the tool, reducing the likelihood of disruptions from other elements of the hobbing machine. The Frontiers in Materials 06 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 3 Framework for research. FIGURE 4 Details of experiment. Frontiers in Materials 07 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 5 Class-wise artificial Faults created on Hobbing Cutter. TABLE 2 Labels for the cutter condition category. Operation no. Cutter condition Label 1 Healthy Healthy 2 Crater wear Crater 3 Chipping Chipping 4 Tip and flank wear T & F wear 5 Gouging Gouging positioning of the accelerometer with a magnet is specifically designed to minimize interference and ensure the accurate detection of vibration signals associated with the tool’s condition. Therefore, the magnet mounting method is carefully chosen to optimize the responsiveness to tool wear and enhance the accuracy of the accelerometer readings. Additionally, a commercial high-capacity and high-frequency range Data Acquisition System (DAQ) for precise measurements. During each task, the DAQ is triggered to record vibrations, utilizing a combination of hardware and software linked to transducers, sensors, and actuators. The DAQ facilitates collecting, storing, and distributing data related to environmental changes in real-world systems. As exemplified by Arduino and Raspberry Pi, open-source platforms now integrate DAQ functions, functioning as mini-computers with limitations such as lower frequency ranges, limited runtime, and accuracy. This study employs a commercial high-capacity and high-frequency range DAQ for precise measurements, converting system parameter changes into an electrical form understandable by a computer. The hardware includes an analog-to-digital converter interfaced with the input component, and a triaxial accelerometer sensor is connected to DAQ to create intelligent systems (Shewaleetal., 2018;Patangeetal., 2019;Patange and Jegadeeshwaran, 2020). The computer acquires, conditions, processes, and stores accelerometer data communicated from software to spreadsheet at a sampling rate of 40.5kHz for 20s. Hence for each condition 802,000 data points were collected. Certain machining turns are excluded to eliminate vibrations from uneven work surfaces. The acquired data is then analysed in MATLAB, extracting statistical features. Figure6 illustrates the vibration response for each cutter condition. Time-domain charts illustrate the immediate effects of faults caused by repetitive and cyclical signals on machining operations. A comprehensive examination of these charts elucidates the influence of defects associated with multi-point cutting tools on machining procedures. Understanding this distinct pattern in vibration signatures is crucial. Utilizing a decision tree algorithm incorporating timerelated attributes like Mean, Median, Standard error, Kurtosis, Impulse factor, Maximum, Mode, Variance, Standard deviation, and Skewness are efficient in classifying tool conditions (Yang and Shami, 2020). This primary study focuses on signal characterization in the time domain, allowing for easy real-time deployment without complex mathematical computations. Consequently, the study reports a fault diagnosis approach employing statistical modelling of signals in the time domain. Figure6 shows a class-wise time domain vibration response curve for a CNC hobbing cutter, considering five conditions: Healthy, Crater, Chipping, Tip and flank wear (T&F wear), and Gouging. The graph (Figure6) depicts a time-based representation, where the horizontal axis corresponds to time measured in seconds, and the vertical axis corresponds to the vibration amplitude. As seen from Table3, the mean vibration amplitude increases for the Chipping, Tip, and flank wear, and Gouging conditions, compared to the Healthy condition. This indicates that these conditions are associated with increased vibration, which was used to indicate tool wear. The Healthy state is distinguished by an exceptionally low vibration response, registering an average of 0 and a standard deviation of 0. In contrast, the Crater state displays a slightly elevated vibration response, featuring an average of −0.01 and a standard deviation of 0.01. The chiming state shows a further increase in vibration response, indicating an average of −0.2 and a standard deviation of 0.1. Transitioning to the Tip and flank wear state, it exhibits the highest vibration response, with an average of 0.2 and a standard deviation of 0.1. The Gouging state reflects a vibration response akin to the Tip and flank wear condition, with an average of −0.2 and a standard deviation of 0.1. The Frontiers in Materials 08 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 6 Class-wise time domain vibration response curve for a CNC hobbing cutter. TABLE 3 The mean, standard deviation, and maximum vibration amplitude for each condition. Cutting tool conditions Mean vibration amplitude (g) Standard deviation (g) Maximum vibration amplitude (g) Healthy 0 0 0 Crater −0.1 0.05 −0.15 Chipping −0.2 0.1 −0.3 Tip and flank wear 0 0.1 0.2 Gouging −0.2 0.15 −0.45 standard deviation serves as a gauge of the fluctuation in the vibration response. A higher standard deviation suggests greater variability, potentially indicating issues with the cutter. Statistically, the distinctions in vibration response among the five conditions are noteworthy. According to a one-way ANOVA test, the F-statistic is 10.22 (p-value <0.01), signifying a less than 1% probability of obtaining these outcomes if the null hypothesis (stating no difference in vibration response among the five conditions) holds. A post hoc test (Tukey’s HSD test) shows that the vibration response for the Tip and flank wear and Gouging conditions is significantly different from the vibration response for the Healthy condition (p-value <0.01). This means these two conditions were reliably distinguished from the Healthy condition based on the vibration response. The vibration response for the Crater and Chipping conditions is not significantly different from that for Healthy conditions (pvalue >0.05). This means these two conditions cannot be reliably distinguished from the Healthy condition based on the vibration response alone. 5.1.1 Diversity of datasets composition The dataset used in the study represents a diverse range of scenarios in gear manufacturing by capturing a comprehensive set of vibration data from CNC hobbing cutters under various conditions. The dataset’s composition reflects the diversity of tool conditions, including healthy, crater, chipping, tip and flank wear, and gouging. This diversity allows for the representation of a wide range of potential scenarios that can occur during the gear manufacturing process, providing a holistic view of the different states of the hobbing cutters. Thedataset size is substantial, capturing around 802,000 acceleration data points at successive intervals throughout each machining process. This large dataset size is essential for training and evaluating machine learning models effectively. It enables the models to learn from a wide range of instances, ensuring that they can generalize well to unseen data and accurately classify different tool conditions. Additionally, the dataset’s size allows for the detection of even the smallest signal variations, providing a robust foundation for developing accurate Frontiers in Materials 09 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 13 (A): Confusion matrix of decision tree model (B): Confusion matrix of ensemble model. reasons behind the selection of above seven algorithms for this specific application is as follows. • Decision trees offer suitability for this task due to their capacity to manage both numerical and categorical data, potentially found in the features of the CNC hobbing cutter. Additionally, they offer interpretability, facilitating comprehension of the decision-making process for tool health monitoring (Patangeetal., 2022). • Naive Bayes is recognized for its simplicity and efficiency in handling high-dimensional data, often encountered in condition monitoring applications. It boasts rapid predictions Frontiers in Materials 16 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 TABLE 5 Comparison of various ML Model Performance. Model Precision Recall F1-score Accuracy Decision Tree 1.0000 1.0000 1.0000 1.0000 Ensemble 0.9301 0.9344 0.9323 0.9322 Naive Bayes 0.9267 0.9261 0.9264 0.9264 SVM 0.9187 0.9255 0.9211 0.9217 Efficient Linear 0.8685 0.8456 0.8569 0.8570 Kernel 0.8065 0.8197 0.8130 0.8130 Neural Network 0.9009 0.9042 0.9026 0.9025 and resilience against irrelevant features (Madhusudana etal., 2016). • Support Vector Machine (SVM) proves effective in managing high-dimensional data and can address nonlinear relationships between features. Its utility shines when complex data patterns hint at tool health issues (Widodo and Yang, 2007). • Efficient linear algorithms like linear regression or logistic regression can be effective when feature-tool health relationships are roughly linear. Kernel algorithms such as kernel SVM excel in capturing non-linear data relationships (Jungetal., 2022;Nyangaresietal., 2022). • Ensemble methods such as Random Forest or AdaBoost enhance model robustness and generalization by amalgamating multiple models for more accurate predictions (Mianetal., 2024). • Neural networks, especially deep learning models, show promise in feature classification for condition monitoring due to their capacity to autonomously discern intricate data patterns and relationships (Luoetal., 2018). These algorithms were chosen for their adeptness in handling CNC machine sensor data characteristics like high dimensionality, non-linearity, and the necessity for real-time processing. Performance evaluation and comparison can be conducted using metrics such as accuracy, precision, recall, and F1 score, among others. 5.5.2 ML model performance Confusion Matrices of Two best ones are presented below: o Decision Tree: This model has the highest True Positives (TP) and True Negatives (TN), which means it correctly identifies both normal and failing conditions most of the time. It also has the lowest False Positives (FP) and False Negatives (FN), which means it rarely misclassifies normal conditions as failing or vice versa. This makes it the best overall model for this application as shown in confusion matrix Figure13A. oEnsemble: This model has good TP rates for both normal and failing conditions as shown in confusion matrix Figure13B, and it has lower FP and FN rates compared to the SVM and Efficient Linear models. However, it is still not as accurate as the decision tree. The following Table5 shows Comparison of various ML Model Performance in which Precision, Recall, F1-score and accuracies were calculated from TP, FP and FN rates of confusion matrices of seven algorithms to select best ML algorithm for classifying faults in a CNC Hobbing Cutter. oNaive Bayes: This model has a good TP rate for normal conditions, but a lower TP rate for failing conditions. It also has a higher FN rate for failing conditions, which means it may miss some failing cutters. This could lead to increased downtime and maintenance costs. oSupport Vector Machine SVM: This model has a good TP rate for both normal and failing conditions, but it also has higher FP and FN rates compared to the decision tree. This means it may misclassify some normal conditions as failing and vice versa. oEfficient Linear: This model has lower TP rates for both normal and failing conditions compared to the other models. It also has higher FP and FN rates, which means it is more likely to misclassify both normal and failing conditions. oKernel: This model has the lowest TP rates and the highest FP and FN rates of all the models This means it is the least accurate model and is not recommended for this application. oNeural Network: This model has similar performance to the Ensemble model, with good TP rates and lower FP and FN rates compared to the SVM and Efficient Linear models. However, it is still not as accurate as the decision tree. 6 Result and discussion 6.1 Variations in various ML model performance The following Figure14 shows variations in various ML Model Performance for selecting best ML algorithm for classifying faults in a CNC Hobbing Cutter. Frontiers in Materials 17 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 14 Variations in various ML Model Performance. •Decision Tree: This model has perfect accuracy, precision, and recall for both classes, making it the most accurate model in this comparison. • Naive Bayes: This model has good performance for normal conditions but lower performance for failing conditions, with a higher risk of missing failures. •SVM: This model has a good balance between precision and recall for both classes but has slightly higher error rates compared to the Decision Tree. •Efficient Linear: This model has lower performance across all metrics compared to the other models, indicating potential challenges in accurately classifying both normal and failing conditions. •Kernel: This model has the lowest performance in all metrics, making it unsuitable for this application. •Ensemble and Neural Network: These models have similar performance, with slightly lower accuracy and F1-score than the Decision Tree but still offering good overall results. Figure14 indicate that, J48 Decision tree algorithms performed extremely well on the condition monitoring task. The model was perfectly classified all of the data points in each of the five classes and attained flawless ratings across all criteria employed to assess its performance. 6.2 Receiver operating characteristics (ROC) curve of decision tree The diagram depicted in Figure15 illustrates the ROC curve pertaining to a decision tree model utilized in monitoring the condition of a CNC hobbing cutter. This graphical representation aids in assessing the effectiveness of a binary classification model by plotting the true positive rate (TPR) against the false positive rate (FPR). Each of the five distinct conditions—chipping, cratering, gouging, healthy, and tip and flank wear—is represented by its own ROC curve, distinguished by various colours on the graph. The area under each ROC curve (AUC) serves as a metric for the model’s overall performance, with an AUC of one indicating flawless classification and an AUC of 0.5 indicating random guessing. As depicted in Figure15, all ROC curves exhibit an AUC of 1, signifying the decision tree model’s impeccable ability to classify all five hobbing tool conditions accurately. 6.3 Risk of overfitting The suggested research tackles the issue of overfitting by meticulously weighing the trade-off between model intricacy and its ability to generalize to unfamiliar data. Overfitting arises when a model becomes overly complex, capturing noise from the training data and consequently performing poorly when applied to unseen data. In the context of the study, the decision tree model achieved 100% accuracy, precision, and recall for both classes, making it the most accurate model in the comparison. However, achieving 100% accuracy raises concerns about potential overfitting. To address the risk of overfitting, the study emphasizes the importance of parameter tuning and model evaluation. It explores how the level of intricacy in models affects overfitting, noting that augmenting factors like the maximum depth of decision trees or the quantity of layers in neural networks might enhance accuracy but concurrently raise the overfitting probability. Additionally, it discusses employing Frontiers in Materials 18 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 FIGURE 15 ROC curve of Decision Tree. TABLE 6 Contrast with studies conducted on Tool Condition Monitoring. Sr. No. Algorithm Accuracy (%) Domain Author and year 1 J48 DT Classifier 100 Time Proposed model 2 Random Forest 92 Time Patange (2022) 3 Calibration 90 Time series Liu (2020) 4 Gaussian Support Vector 86 Time-frequency Zhou (2019) 5 Support Vector machine 85 Time-frequency Aghazadeh (2018) 6 J48 DT 82 Wavelet Ravikumar (2018) 7 J48 DT 77 Time Elangovan (2011) 8 Bayes Net 86 Time Elangovan (2010) regularization parameters, like the strength of regularization in linear models or the dropout rate in neural networks, to mitigate overfitting by discouraging excessively complex models. Furthermore, the study employs a robust evaluation process, including the use of a separate test set that was not part of the training data to estimate the model’s generalization ability. This evaluation process helps gauge the model’s effectiveness in predicting classes for new, unseen data points and assesses its performance in real-world scenarios. Additionally, the study discusses the use of Bayesian optimization to efficiently explore the parameter space and find the optimal settings for a given problem, which can help mitigate the risk of overfitting by fine-tuning the model’s parameters. The proposed work addresses the risk of overfitting by carefully considering model complexity, employing regularization Frontiers in Materials 19 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 parameters, and conducting thorough model evaluation using separate test sets. These measures help ensure that the model can generalize well to unseen data and effectively predict the condition of hobbing cutters without being overly influenced by noise in the training data. Based on Machine Learning analysis and classifier output, only J48 Decision Tree model achieved perfect accuracy (100% correctly classified instances) in the given dataset for CNC hobbing cutter condition monitoring. This suggests that only Decision Tree model can effectively identify the different types of cutter wear (healthy, crater, chipping, T & F wear, and gouging) in this specific scenario. The J48 Decision Tree algorithm is a decision tree method employed for tasks related to classification. It is highly preferred in machine learning because of its simplicity and ease of understanding. Therefore, the methodology for categorizing faults in hobbing tools is deemed fitting and suitable. This condition monitoring system is applicable only for hobbing cutter specified in this research work. The effectiveness of the suggested framework is assessed in comparison to contributions from other sources (literature) are presented in Table6. 7 Challenges and future directions Despite its promising potential, ML-based hobbing cutter condition monitoring still faces challenges. These include: •Data Acquisition and Pre-processing: Collecting and preprocessing high-quality sensor data was crucial for accurate ML model development. •Model Training and Validation: Building robust and generalizable ML models requires access to diverse and labelled datasets, which was challenging to acquire. •Real-Time Implementation: Integrating ML models seamlessly into existing CNC systems and ensuring robust performance in real-time settings requires careful consideration. Further research and development efforts are necessary to address these challenges and fully unlock the capabilities of machine learning in monitoring the condition of hobbing cutters. However, one may develop the generalized condition monitoring system for different specifications of hobbing cutter. This includes exploring advanced data acquisition and preprocessing techniques, developing robust and efficient ML algorithms, and facilitating seamless integration with CNC systems. Moreover, investigating the application of ML for real-time process control and optimization holds immense promise for further enhancing efficiency and quality in gear manufacturing. 8 Conclusion The research work in Machine learning (ML) offers a revolutionary approach to hobbing cutter condition monitoring, significantly enhancing efficiency, productivity, and quality in gear manufacturing. By enabling real-time assessment of cutter health, manufacturers will proactively optimize performance and minimize downtime, leading to superior product quality and reduced production costs. This research work proposes a novel ML-based condition monitoring system successfully implemented using artificially induced faults on a hobbing cutter. FMEA was carried out to decide which types of cutter faults need to be considered for developing a condition monitoring system. The vibration signals obtained using a commercial high-capacity and high-frequency range DAQ were examined for changes in speed, feed, and depth of cut. The signals were processed using MATLAB to extract statistical features and underwent training using seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network). Amongst these algorithms, J48 Decision Tree has achieved perfect accuracy (100% correctly classified instances) for given dataset. Based on this analysis, it is recommended to use J48 Decision Tree Classifier to monitor the condition of a CNC hobbing cutter. These algorithms are accurate and fast to build, making them wellsuited for this task. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. In further research, this system will trigger alerts upon detecting worn or damaged cutters, allowing for timely replacement and preventing potential issues. By implementing this ML-based system, manufacturers will ensure optimal hobbing cutter performance, leading to significant advancements in gear manufacturing. This condition monitoring system is applicable only for hobbing cutter specified in this research work (Omoleetal., 2023). Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. Author contributions SS: Conceptualization, Project administration, Resources, Supervision, Writing–review and editing. NT: Formal Analysis, Investigation, Methodology, Project administration, Resources, Software, Writing–original draft, Writing–review and editing. BD: Formal Analysis, Investigation, Project administration, Software, Supervision, Validation, Writing–original draft, Writing–review and editing. SP: Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. RC: Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing–review and editing. EN: Funding acquisition, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing–original draft. HM: Conceptualization, Project administration, Supervision, Visualization, Writing–original draft, Writing–review and editing. Frontiers in Materials 20 frontiersin.org Tambake etal. 10.3389/fmats.2024.1377941 Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors present their appreciation to King Saud University for funding this research through Researchers Supporting Program number (RSPD 2024R1006), King Saud University, Riyadh, Saudi Arabia. Acknowledgments Authors thank the college administration for supporting this work. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Alabdulwahab, S., and Moon, B. K. (2020). 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