This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding tools, which are widely used in the marine and automotive industries, enabling earlier detection of tool faults before they result in weld defects or component failures.
Abstract
This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The Locally Weighted Learning (LWL) algorithm, a lazy learning method, is used to address this limitation. Vibration signals are collected from a PLC-controlled FSW machine under five tool conditions, statistical features are extracted from the raw data, and a J48 decision tree is applied for feature selection to reduce computational overhead. Classification performance is evaluated using three lazy learning algorithms K-star (K*), LWL, and k-Nearest Neighbour (kNN) with LWL yielding the best result. The previously reported best accuracy for the same FSW setup was 73.16% at 1400 rpm using Random Forest; the proposed LWL-based approach achieves 92% accuracy under identical conditions, enabling earlier detection of tool faults before they result in weld defects or component failures.
Tool Condition Monitoring (TCM) plays a significant role in maintaining machining quality, reducing equipment idle time, and improving TCM often affords near-real-time detection of wear-out phenomena. A new method for vibration-based fault diagnosis of round insert face milling tools based on analysis in the time-domain and machine learning, the vibration signals were obtained using a spindle-integrated piezoelectric accelerometer during milling with controlled conditions. Healthy, flank wear, edge chipping, built-up edge and mixed fault conditions were studied from the statistical features extracted. Feature selection was implemented using Recursive Feature Elimination and Mutual Information ranking. Distributed computing was used to pilot SVM and XGBoost classifiers. The output indicated that the test accuracy of XGBoost was better (92.4% accuracy) than SVM (89.2%), while both had lower errors and shorter estimates as well. The presented approach is a cost-effective and real-time applicable method for intelligent monitoring of the condition of the monitoring tool in CNC machining. This study contributes to the development of data-driven predictive maintenance systems for smart manufacturing.
P. Patil, N. Gautam, Bhuvaneshwar D. Patil· International Journal of Mec...· 0 citations
This research presents an integrated condition monitoring framework for deep groove ball bearings by combining Complex Morlet Wavelet analysis, machine learning techniques, thermographic analysis, and SKF Machine Condition Advisor tools that demonstrates significant potential for predictive maintenance and intelligent condition monitoring applications.
M. Maurya, Chandrabhanu Malla, I. Panigrahi et al.· F1000Research· 0 citations
This study leverages multiple machine learning algorithms for defect detection in friction stir welding (FSW), utilizing force-derived features as model inputs. Furthermore, the underlying relationships between welding forces and defect formation were systematically investigated, alongside an evaluation of the efficacy of force-feature-driven defect detection models. Results indicated that the variations in the averages and waveforms in the traverse force (Fx), lateral force (Fy) and plunge force (Fz) are highly responsible for the defect formation in FSW joints, such that an increase in Fy causes waveform distortions in Fx and Fy. Fyavg is the most important feature for the defect formation for 724 sets of experimental data. Defect detection based on thresholding of Fyavg and Fzavg achieves an accuracy of 80.3%. In contrast, four machine learning algorithms—Decision Tree (DT), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were employed to construct defect detection models using the extracted force features as inputs, yielding accuracies of 93.5%, 97.5%, 95.7% and 94.9%, respectively. These findings further elucidate the underlying mechanics, that is, Fx and Fy primarily originated from the extrusion and shear forces induced by the probe’s rotation and traverse, whereas Fz was predominantly attributed to the compressive action of the shoulder.
Accurate detection of faults within rotary machinery components is vital for assuring the structural reliability of equipment in manufacturing facilities and power generation systems. Automatic fault detection is an increasingly common approach whereby data acquired from sensors are analyzed by machine learning algorithms to distinguish between normal and faulty component states. Vibration signatures recorded from healthy and faulty ball bearings, taken from the Case Western Reserve University (CWRU) 12k Drive End database, were utilized in the present study. These vibration signatures were segmented into windows of 0.1 s duration. Standard statistical time-domain features (root mean square, kurtosis, peak value and crest factor) were extracted from each sample. A random forest (RF) classifier was trained on vibration data from healthy bearings and several types of faulty bearings, partitioned into training (70% of data) and testing (30%) sets. The RF classifier distinguished healthy from faulty bearings with 98.8% accuracy across ball, inner race, outer race and normal categories. Feature importance analysis indicated that RMS (50% importance) and peak value (28% importance) were the most influential contributors, together comprising about 78% of the decision making. The RF model further recorded a recall of 1.00 for the normal baseline category, indicating that healthy bearings were identified with zero false negatives. The results demonstrate that ensemble machine learning, combined with statistically significant time-domain features, provides a highly accurate and physically interpretable solution for industrial condition monitoring, serving as a foundation for more advanced rotor-dynamic fault detection.
Reliable identification of railway wheel defects is important for safety and maintenance. This study develops a machine-learning-based diagnostic framework for multi-class defect identification using passive air-coupled ultrasonic acoustic emission signals. Data were collected from eleven full-scale railway wheelsets representing nine health states. Time- and frequency-domain features were evaluated using Kruskal-Wallis statistical testing and mutual-information analysis to identify the most discriminative indicators. A Random Forest classifier was then trained using the selected features with stratified 5-fold cross-validation. The model achieved a balanced accuracy of approximately 0.66 and a Macro-F1 score of 0.65 across the nine classes. Decay rate, kurtosis, skewness, and envelope low-frequency power emerged as the most influential features, while a compact subset of features retained most of the classification performance. The results demonstrate the feasibility of combining passive ultrasonic sensing, statistical feature selection, and supervised machine learning for non-contact railway wheel defect classification and provide a foundation for future field-deployable inspection systems.
Aashish Shaju, Steve Southward, Mehdi Ahmadian· 0 citations
The findings affirm the efficacy of XGBoost in bearing fault classification and emphasise the diagnostic value of carefully selected time-domain features, as well as suggesting strong potential for deploying such models in real-time condition monitoring and predictive maintenance systems.
A. Bhende· Insight - Non-Destructive Te...· 0 citations
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