In recent decades, condition-based maintenance has evolved from visual inspection methods to automated inspection methods. Automated based methods include use of advanced signal processing techniques and Machine Learning (ML) techniques. Automated based methods collect sensitive information from machines or tools , , whereas human inspections are sometimes prone to error. The sensitive information regarding health of machines or tools can assist in determining root cause of failure and reducing machine down time. However, real-time tool monitoring systems need consistent data and can be subject to issues of sparse or missing data, or imbalanced data. These issues need to be catered for otherwise, automation will fail . This point is particularly important when dealing with large streams of information.
Every year, global automotive production exceeds more than 60 million vehicles. Each vehicle has hundreds of sheet metal components. Even a small improvement in maintenance strategy of sheet metal stamping process can increase the cost efficiency for the automotive industry. To manufacture light weight vehicles, there is also an increase in trend of using advanced high strength steels (AHSS) and ultra-high strength steels (UHSS). This has resulted in increased forming forces and galling wear on stamping tools causing premature failure of stamping tool. Visual inspection of stamping tools at regular intervals is both time consuming and impractical. Considering the huge cost involved in stamping tools, condition based maintenance is very much required for automotive industries to reduce machine down time and increase cost efficiency . Therefore, attempts have been made in the literature to understand wear on the stamping tool using different sensors.
In the literature, sensors have been used for in-situ monitoring of the stamping tools or to distinguish wear profiles of the stamping part. In situ sensing applied to stamping tools/parts typically involves strain gauges to read either strain directly or different axial loads. Xu et al.  investigated strain experienced during the stamping process. Two strain gauges were used to obtain more uniform results. Daubechies Wavelets (Db 4 and Db 5) were used to provide time and frequency-based information to differentiate different states of process anomalies. Hidden Markov Models and probability density functions were used to predict anomalies. Hidden Markov Models and probability density functions have disadvantages in that they cannot express dependencies between hidden states which suggests they are poor for transparency and visualisation which is an important consideration for the work presented here. Bassiuny et al. and Ge et al. ,  also carried out further investigations into using strain gauges for monitoring stamping processes. Bassiuny et al.  used frequency information to distinguish from normal state where higher frequencies are experienced to both mis-feed and the too thick state of workpiece. In addition, Hilbert Marginal Spectrum features obtained from analysing strain waveform was used as an input to the Learning Vector Quantisation Neural Network. Using this technique, it was possible to distinguish anomalies in the stamping process. Learning Vector Quantisation however usually require a pre-processing layer similar to a Self-organising map or k-means algorithm and therefore considered too complex when considering visualisation and transparency. Ge et al.  used Support Vector Machines (SVM) to distinguish features from strain waveform data. The SVM technique was preferred because it performs well when presented with low data sets. SVMs however are more complex and verbose when considering multiple outputs  which is why they are not appropriate for the work presented here. The use of a strain gauge however lacks resolution and is often difficult to distinguish between one anomaly from another. Hence the requirement for other sensing technologies especially those combined as a multispectral approach. Garcia  used another technique based on the use of digital camera and applied optimised wavelet to distinguish wrinkles and surface roughness by extracting 2D images. In summary, machining learning techniques have been applied to sensor data, such as strain data obtained from strain gauges, in sheet metal stamping. Also other machine learning techniques have been applied to distinguish the evolution of scratch forming with ball-on-disk sliding based on input parameters . The machine learning technique used fuzzy clustering and quantum-behaved particle swarm optimisation to provide accurate and efficient predictions. It is clear there is research to predict scratch formations however not for the onset of galling where galling is a very minuet process and standard sensory quantities such a strain gauges will not have the resolution to see such effects and changing characteristics. Therefore, there is a need for machining learning to be applied to a more sensitive material measurement process.
Within the above mentioned works the sensors used to provide damage mechanisms lack in information accuracy, precision and resolution where there is a need for using more sensitive measurement technologies to provide more information and allow preventative maintenance as opposed to failure reactive maintenance. Having such capabilities help to provide extension of live where material strengthening or damage recovery can be carried out as part of maintenance. With total or near total failure, the part is simply swapped out. Such ideas for preventative maintenance fit in with our need to reduce energy and carbon emissions. One sensor that provides more information and has been used before in stamping tests is acoustic emission. To the knowledge of the authors there has not been any work in measuring acoustic emission from stamping tests and applying it machining learning techniques to gain visualisations, classifications and predictions of damage mechanisms such as the onset, and established wear. Furthermore there has been no attempts to use machine learning techniques to provide automation in detection and preventative maintenance for the stamping process using acoustic emission measurements. The reason behind this can be down to the fact that real time detection of tool wear in a slow mechanical process is very challenging, especially when using AE with wideband sensors where changes are very small in nature compared with the total amount of data extracted.
A large number of researchers have reported the application of Neural Network (NN) models for the tool condition monitoring data to classify tool wear in turning , . Turning however has a lot of similarities with sheet metal stamping where scratches or galling can occur if the conditions are right. NNs are very good for low data sets as well as good and accurate visual output tools. Not to mention their prediction and classification capabilities which score fairly high when ranked against similar supervised methods.
In terms of AE being used in tensile tests (slow varying mechanical change), Godin et al.  looked at using k-means and self-organising maps in segregating different mechanisms of material failure through different AE waveform fingerprints. Such AE is reduced in n-dimensionality to give the rise time, peaks and counts . By using these reduced AE parameters, only three values are required as opposed to a whole AE signature. These reduction techniques coupled with the Short Time Fourier Transform (STFT) of the AE signature has been seen in the AE literature before and specifically, applied to stamping tests , however the use of ML techniques have not been used to date. In comparing the two machine learning techniques, self-organising maps are less prone to local optima than k-means as k-means can suffer from premature convergence. That said, other unsupervised techniques such as fuzzy clustering take information of all the surrounding clusters to calculate the best distance for the point of interest .
More recently studies have focussed on wear mechanisms experienced in micro milling to quantify how AE can be used to distinguish such microscopic phenomena . Ren et al.  also looks at other precision machining processes where fuzzy identification can accurately measure material removal rates using extended subtractive cluster analysis and adaptive filtering techniques, which when tuned, gives the process more accuracy against unwanted noise , . This is another reason why fuzzy clustering is considered a better visualiser/classifier when compared with self-organising maps and k-means.
The tooling insert wear mechanism has a defined cutting edge and can partially represent the mechanics of single grit cutting seen in scratch tests replicating wear mechanisms as seen in tribology studies. Venkatesh et al.  predicts insert wear through NN models using the input of time, velocity, feed and cutting force. In other studies  ML techniques were applied to the AE data recorded from the scratch tests and is directly applicable to the work proposed in this paper. Moreover, the precision of AE technologies applied to wear can also be directly related to material removal mechanisms achieved during stamping. Based on the success of ML application for the AE data, in this work, ML techniques are used to classify AE data related to galling wear in sheet metal stamping process.
AE features have shown interesting tendency to identify galling wear at the very initial stages, that is, much prior to wear that is visually visible , . Further work using AE identified the various stages of galling wear and focused on sensitivities of AE features that would indicate both the initiation of wear and severe wear on stamping tools , . By applying ML techniques to AE features and understand the transition from non-galling to galling wear seems to afford many new insights not offered by previous means. This paper’s work was inspired from previous works where AE sensors have been used to study wear mechanisms and source location without complex signal processing and data orientated algorithms , , , , , –. Time-frequency techniques to study acoustic emission waveforms for the fault diagnosis of machining processes and the in-service operation of bearings has been previously investigated [9–16], –. Condition monitoring of stamping processes using acoustic emissions discussed in ,  is applied with the work presented in [17, 18] ,  where Hilbert Huang Transform (HHT) provides a method to track the state from both stationary and non-stationary data. In displaying such information, it is possible to show a better understanding of the onset, transition and severe galling wear condition. This application of classifying non-galling and galling wear through using ML techniques applied to AE data is the identified knowledge gap in the literature and needs further research.
In this study, data obtained from AE sensors in previous work  is used to segregate unworn and worn stamped parts using ML techniques. AE waveforms were analysed using a number of time-frequency techniques to determine a suitable technique to study the wear behaviour. To ensure the data is more salient for automation of wear initiation; ML techniques were used. With a limited data set of destructive tests (namely depth profile measurements) it was possible to predict the remaining depth profile measurements from just AE signatures and non-tested cases. The non-tested cases are a much larger data set. This holistic approach conforms to a non-destructive testing technique. AE and ML were selected, AE because of the wide bandwidth data and is often difficult to identify the wear features of interests that can correlate from one pattern to another. With ML techniques it is possible to segregate the different conditions of interest useful for maintenance control.
The ML techniques that are used for the classification in this work are Neural Networks (NNs) and Classification and Regression Trees (CART) as they both perform well when presented with small data sets and they are supervised classification techniques. The other technique to obtain good coverage of ML techniques is fuzzy clustering, which also responds fairly well to small data sets and is unsupervised learning in nature. The three techniques not only give a good coverage of ML techniques but also provide a comparison of supervised versus unsupervised learning, which is not common in literature especially when applied to tool wear. Apart from regression trees, both NNs and fuzzy clustering have already been discussed in the introduction where researchers applied this to Tool Condition Monitoring (TCM) and this is a further reason why they have been studied in this paper.
To distinguish different wear mechanisms which are non-linear in nature there is a need to use ML techniques that allow the visualization of such behaviour and this is another reason why these techniques have been chosen over others. To extract out the salient minuet behaviour digital signal processing techniques such as HHT were applied to acoustic emission measurements before being input to the machine learning techniques. Using these transforms as a pre-processing layer is another unique method when introducing data to machine learning or cognitive layer. Finally, load has been introduced as a quantity to compare and contrast sensor technology sensitivities as well a known source to calibrate acoustic emission.
The rest of this paper is organised as follows: Chap. 2 discusses the experimental setup and algorithms, where both AE and ML are discussed in greater depth; Chap. 3 discusses the AE signal to physical data correlation; Chap. 4 discusses the classifier results applied to the signal and physical data.