Barwick, Steve

Booker, Vince

Mildenhall, Tom

Nguyen, Khuong An

Still, Laura

Wang, Yan

Wilson, Nishant

English

The work aims to develop an automatic cutting tool life prediction model for die-cuts machine at Parafix. Such model will be able to estimate how long a given tool is likely to last, in order to improve performance and productivity. This work is part of the KTP project between Parafix and University of Brighton

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Proceedings of Machine Learning Research 152:1–3, 2021 Conformal and Probabilistic Prediction and ApplicationsConfidence Machine Learning for Cutting Tool LifePredictionNishant Wilson NWilson@parafix.co.ukSteve Barwick SBarwick@parafix.co.ukVince Booker VBooker@parafix.co.ukTom Mildenhall TMildenhall@parafix.co.ukLaura Still LStill@parafix.co.ukParafix Tapes & Conversions Ltd, Lancing Business Park, Lancing BN15 8UA, United KingdomYan Wang Y.Wang5@brighton.ac.ukKhuong An Nguyen K.A.Nguyen@brighton.ac.ukUniversity of Brighton, Brighton BN2 4GJ, United KingdomEditor: Lars Carlsson, Zhiyuan Luo, Giovanni Cherubin and Khuong An NguyenAbstractThe work aims to develop an automatic cutting tool life prediction model for die-cutsmachine at Parafix. Such model will be able to estimate how long a given tool is likelyto last, in order to improve performance and productivity. This work is part of the KTPproject between Parafix and University of Brighton.Keywords: Tool life prediction, Conformal Clustering, predictive maintenance.1. IntroductionParafix is a long established UK based converter and distributor of self-adhesive tapes,foams, films and other flexible materials, creating complex, bespoke adhesive components fora number of market sectors. One of the challenges for Parafix is understanding maintenancescheduling and machine failure, which have previously occurred mid-job where the cuttingtool broke down and subsequently required jobs to be rescheduled, impacting on deliverytimes.Therefore, this project aims to utilise the input data from the recently installed pressuresensors on the die-cut machines, along with the material types and the product geometries,to develop an automatic tool life prediction model, while addressing the problem of sensornoises.2. Sensor input dataThe training samples come from an industry grade miniature load cell (see Figure 1), de-signed to operate in confined space. This load cell measures the pressure (in PSI unit) beingapplied by the operator on two separate points (i.e., near side and gear side) on both endsof the die-cutting machine, holding the cutting tool in place. The sensor has a sensitivity of2.0 mV/V , a sampling rate of 9,000 Hz, and can withstand up to 2,250 PSI (158 kg/cm2).However, the challenge for using such sensor is the high electrical noise (see Figure 2) atup to 12 PSI (0.8 kg/cm2). Therefore, there is an urgent need to develop a robust MachineLearning model that can handle such uncertainty.© 2021 N. Wilson, S. Barwick, V. Booker, T. Mildenhall, L. Still, Y. Wang & K.A. Nguyen.Confidence Machine Learning for Cutting Tool Life Prediction(a) The clamp and sensor box. (b) The load cell.Figure 1: Data capturing facility on each machine. The miniature load cell was installedinside the sensor box to measure the clamping force.(a) Gear side pressure. (b) Near side pressure.Figure 2: The histogram of the pressure measure distribution on two separate points on thecutting tool, when the machine was at rest.3. Conformal predictive maintenanceHistorically, most traditional Machine Learning algorithms only provide predictions, with-out any associated confidence information. However, when the training samples are noisyor incomplete (e.g., in our case with the pressure sensor), such information will be valuableand more informative than a simple ‘Yes/No’ prediction (i.e., informing the cutting tool tobe replaced, only when the confidence of the prediction is high).The assumption is that the more pressure the operator needs to apply to successfullycut the parts, the more likely that the cutting blade’s sharpness has decreased and mayneed replacing.As more data will be collected in the next 12 months, our work is derived into twostages.In the first stage, we will assess the dataset to find any existing pattern, which is impor-tant for Machine Learning algorithms to work. This process involves training a ConformalPredictor on the entire dataset, to output a prediction region which can be divided intoclusters (Balasubramanian et al., 2014; Cherubin et al., 2015). Each cluster will correspondto a different stage in the tool life cycle (e.g., brand-new, lightly used, well-used, needreplacing, etc.).2Confidence Machine Learning for Cutting Tool Life PredictionIn the second stage, we will build a predictive model to estimate if the tool needs tobe replaced within a given time frame, using the clusters generated in the first stage. Apotential method is based on k-nearest neighbours, as follows (Nouretdinov et al., 2020).Firstly, given a new sensor sample of the cutting tool in an unknown stage, k nearest datapoints will be identified. Secondly, a majority vote will decide which cluster the new sampleshould belong to. For example, out of 10 nearest data points, 8 of them belong to the‘lightly-used’ cluster, and the remaining 2 belong to the ‘well-used’ cluster; then it is likelythat the tool is currently in the lightly-used state. We will verify our model’s results againstground-truth labels obtained manually by the machine operator.ReferencesVineeth Balasubramanian, Shen-Shyang Ho, and Vladimir Vovk. Conformal prediction forreliable machine learning: theory, adaptations and applications. Newnes, 2014.Giovanni Cherubin, Ilia Nouretdinov, Alexander Gammerman, Roberto Jordaney, ZhiWang, Davide Papini, and Lorenzo Cavallaro. Conformal clustering and its applicationto botnet traffic. In International Symposium on Statistical Learning and Data Sciences,pages 313–322. Springer, 2015.Ilia Nouretdinov, James Gammerman, Matteo Fontana, and Daljit Rehal. Multi-level con-formal clustering: A distribution-free technique for clustering and anomaly detection.Neurocomputing, 397:279–291, 2020.3

Confidence Machine Learning for cutting tool life prediction

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Confidence Machine Learning for cutting tool life prediction

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