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This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser‐induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre‐trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting‐edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84–99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real‐life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A 3D crack reconstruction method is proposed. • The algorithm was tested using synthetic data and confirmed by real cracking data. • Our approach combines acoustic emission with independent component analysis. • Fiber Bragg grating is used as acoustic sensors. Abstract Cracks are one of the main causes of solid bodies weakening and are a hidden threat for mechanical systems. Acoustic emission is an indicator of cracks initiation and propagation and is widely exploited for the localization of cracks in non- or weakly-transparent environments. The present study enriches the functionality of multiple existing algorithms in crack localization using acoustics. The novelty is in the reconstruction of the complex geometry of the crack path and tracking its propagation in time, whereas existing methods focus only on the localization of the crack without any information about its geometry. The algorithm uses sparse acoustic signal representations as relative energies of the narrow frequency bands, extracted with the M- band wavelet transform. Non-linear independent component analysis is applied to de-mix the recorded acoustic signals into a number of separate acoustic patterns. Furthermore, a triangulation, using the time delay arrivals, is applied for each of such patterns separately, thus extracting multiple individual emittance sources. The algorithm was tested using synthetic data that replayed various scenarios of crack propagation together with different detector arrays configurations and its behavior was analyzed. Additional verification with real-time data was carried out by analyzing signals from crack propagation in glass along a known programmed path. The acoustic data was recorded with four fiber Bragg gratings. In both cases, the algorithmic framework showed a high efficiency in recovering the geometrical configuration of the crack. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is an effective method to monitor and control the quality in different technical processes, including tribology and fracture mechanics. However, in highly dynamic processes, the processing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been considered as a new and powerful tool to overcome both: the complexity of the large data processing with a reasonable computational time. In this contribution, we will present two examples from completely different process where AE techniques are combined with AI in order to make a significant step forward in process monitoring and quality control. To start with, in additive manufacturing, which has been at the center of attention in recent years, we will demonstrate that the combination of AE with AI makes possible to detect several types of defects, including pores.. With the second example, we will prove that the combination of AE with AI is a very promising approach for in situ and real-time monitoring of electrical discharge inside natural rocks. [ABSTRACT FROM AUTHOR]

Laser processing is an important technology in industrial manufacture. Today, a standard quality monitoring of the processed workpieces mainly relies on costly X-ray or time consuming post mortem methods. The commercially available in situ and real-time monitoring units are also accessible and rely on optical measurements or high resolution imaging of the process zone. Unfortunately, their performance is greatly affected by the plume, formed during the overheating of the material. This limits the detection of defects (e.g. pores) formed inside the workpiece. To bypass the aforementioned limits, we focus on acoustic measurements that are the derivatives of the shockwaves, generated inside the workpiece directly during processing. The measurements are conducted using a high sensitive piezo sensor. The acoustic signals are analysed further by state-of-the-art signal processing. This included a compartment of wavelets and Fourier decompositions, followed by machine learning. The developed methodology is realized in a hardware unit that operates in pseudo real-time. The unit was tested on real welds with various materials. The welding experiments were performed using spot weld, Stepless High-speed Accurate and Discrete One-pulse Weld (SHADOW). The tests were carried out at different laser powers that are closely related to the weld quality. The in situ quality control unit demonstrated the capability to classify the weld quality with a confidence level ranged between 82% and 95%. Due to the similar light-matter interaction phenomena, the presented approach and apparatus can also be applied for monitoring other laser-processing technologies, namely: ablation, cutting, drilling and additive manufacturing. [ABSTRACT FROM AUTHOR]

Scuffing is one of the most problematic failure mechanisms in lubricated mechanical components. It is a sudden and almost not predictable failure that often leads to extensive cost in terms of damages and/or delay in production lines. This study presents a promising solution that can prevent scuffing for the machinery industry in the future. To achieve this goal, a signal processing approach by means of an acoustic emission is introduced for the prediction of scuffing. An acoustic dataset was collected from metallic surfaces reciprocating under a constant load (typical conditions for semi journal bearings). The coefficient of friction values were measured during the entire experiments and were referred to as the ground truth of the momentary surface state. Based on the friction behavior, three friction regimes were defined that are running-in, steady-state, and scuffing. The present approach is based on tracking the changes in acoustic emission by means of three sets of wavelet-derived features. Those features include: 1) energy, 2) entropy, and 3) statistical information about the content of acoustic emission and the response of each feature to the different friction regimes was individually investigated. The applicability of machine learning classification and regression was studied for scuffing prediction. Both approaches were applied separately but can be unified together to increase the prediction time interval of surface failure. For classification, an extra friction regime was introduced designating as pre-scuffing and is defined as a time span of 3 min before the real surface failure. Random forest classifier was used to differentiate the features from the different friction regime. The best performance in classification of features from pre-scuffing regime reached a confidence level as high as 84%. In regression approach, the extracted features sequences were used together with random forest regressor. Our strategy allowed predicting scuffing up to 5 min preceding its real occurrence. [ABSTRACT FROM PUBLISHER]

The excessive energy consumption from the mining industry are currently receiving international attention. A promising method able to enhance significantly the comminution process efficiency worldwide is by using electric pulse fragmentation treatment. However, to insure a minimum energy consumption in real scale operation, an online process monitoring is of utmost importance. This work presents an in situ and real-time monitoring method by combining acoustic emission sensor and advanced machine learning algorithms. The proposed method was developed on a gold-copper ore in well-controlled single stone experiments and in semi-continuous process, reproducing a real industrial environment. In single stone experiment, the pulse energies was varied from 200 to 750 J leading to three weakening behaviours; no discharge , surface discharge and fragmentation. Acoustic signals for these categories have been decomposed with wavelet packets, and sub-band energies have been chosen as features. Then, only the most informative features were selected via standard linear principal component analysis. Finally, the classification was performed via a traditional support vector machine. In the semi-continuous experiments, an unsupervised learning method was used for classification task based on Laplacian support vector machine. Results for single stone tests showed accuracy above 90% for the three categories. For semi-continuous tests, we demonstrated that the unsupervised classification can be applied efficiently to estimate the amount of weakening of the passed through ore. We are very confident that the proposed method can be easily industrialised to monitor in situ and in real-time the electric discharge process within a comminution operation. We investigated the use of machine learning and acoustic emission for in-situ monitoring for gold-copper ore weakening by electric pulse. The method is based on analysis of features from acoustic emission that are extracted using wavelets packet transform. The experiments were conducted on gold-copper ore samples. Based on the promising results obtained, we are confident that recording of the AE during the electric discharge process and classification of the signals with ML is a promising solution to monitor in situ and in real-time the weakening process and to insure that the correct amount of weakening is made by the electric pulses. Image 1 • An In situ process monitoring for a gold-copper ore weakening by electric pulse is presented. • Our approach combines acoustic emission with machine learning. • acoustic hydrophone sensor is used. • Support vector machine are used for classification in terms of weight loss. • The classification accuracy of the events ranged between 63 and 99%. [ABSTRACT FROM AUTHOR]

The efficiency of processes involving frictional contacts between surfaces is often characterized by wear rates or friction coefficients. However, the classification and forecasting of wear rates in friction related processes is a real industrial challenge that is unsolved today. Hence, an on-line monitoring system able to classify wear rate can be crucial for many industries as it could help in preventing catastrophic failures. Applications include lifetime assessment of various industrial components where a range of wear failures occur such as scuffing (a typical sudden failure mechanism). These tribological processes can now be sensorized, and the corresponding sensor signatures can be modelled and monitored using state-of-the-art Machine learning (ML) algorithms. In this study, we use an Acoustic Emission (AE) sensor and ML frameworks to classify different wear categories simulated with a customized pin-on-disc tribometer. A real-time investigation of the wear track is necessary to find out the origins of the wear scar visible at the surface. To achieve this objective, the experiments were conducted on a pin-on-disc tribometer equipped with a Digital Holographic Microscope (DHM). Experiments were carried out using alumina and steel balls against steel discs at room temperature. Real-time DHM images of the wear track surface were recorded for each lap at the same position. An acoustic emission sensor recorded the AE signals during the complete duration of experiments. The AE signatures, in combination with the real-time DHM images, were correlated as input and ground truth labels for the ML algorithm. Several ML frameworks were compared; they are support vector machine, logistic regression, XGBoost, random forest, neural networks, k -Nearest Neighbor, quadratic discriminant analysis and Naive Bayes. The classifier was trained to differentiate the acoustic emission features of the different wear rates. Most ML algorithms had an average classification accuracy above 80%, and the highest was obtained with support vector machine (84.7%). The classification accuracy can be improved by combining two neighboring categories with limited differences in terms of wear rate. Hence, the proposed method has a significant industrial potential for in-situ and real-time quality monitoring of wear processes since it requires minimum modifications of commercially available industrial machines. • Real-time visualization of the wear scar surface using Digital Holographic Microscope (DHM). • Correlation of Acoustic Emission (AE) signatures with the friction and wear data. • Visualization of feature space distribution during evolution of the wear scar. • Comparison of various Machine Learning (ML) frameworks for classification of wear rates. • In situ and real-time classification of wear rates in abrasive wear combining AE and ML. [ABSTRACT FROM AUTHOR]