Predictive maintenance and engineered processes in mechatronic industry: an italian case study.

Predictive maintenance and engineered processes in mechatronic industry: an italian case study.


In the proposed paper are discussed the results of a research industry project focused on the optimization of predictive maintenance processes of a machine cutting polyurethane. A company producing cutting machines, has been provided with an online control system able to detect blade status of a machine supplied to a customer producing polyurethane components. A software platform has been developed for the real time monitoring of the blade status and for the prediction of the break up conditions adopting a multi-parametric data analysis approach, based on the simultaneous use of unsupervised and supervised machine learning algorithms. Specifically, the proposed method adopts a k-Means algorithm to classify bidimensional danger maps and a Long Short Term Memory (LSTM) one to predict the alerting levels based on the analysis of the last values for some process variables. The analysed algorithms are applied to an experimental dataset

Data di pubblicazione

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Smart District 4.0 


  • Prof. Ing. Alessandro Massaro
  • Ing. Nicola Magaletti
  • Ing. Gabriele Cosoli
  • Dr. Angelo Leogrande


The technology innovation of mechatronic systems combined with Artificial Intelligence (AI) facilities is an important research topic in industrial engineering. The case study of the proposed paper is addressed on this main topic, focusing the attention on predictive maintenance tools which can be performed mainly to avoid machine breakdowns and product defects. In this scenario, different sensors can be adopted to detect cutting machine data for monitoring blade status. Concerning manufacturing processes, the approach to monitor wear status can be based on acoustic multi-sensors systems as well as on Artificial Neural Networks (ANNs) able to estimate and classify certain wear parameters. Cutting tool wear analysis can be performed also by microscope-based 3D image process too, providing the blade wear profile. Some studies highlight that wear conditions can be analysed by the relationship between temperature and electrical resistance, or defining wear classes applying thermography combined to Convolutional Neural Network (CNN). In particular, AI Elman Adaboost approaches are used to predict wear conditions, by analysing force data, vibration data, acoustic emission signal, and other multi-sensor data [10]. Cutting forces and vibrations are surely important parameters to detect wear [11]. Temperature distribution analysis [12] can be useful to understand physical phenomena such as elongation in metallic components [13],[14]. Machine learning unsupervised and supervised algorithms, such as respectively k- Means [15] and Long Short Term Memory (LSTM) [16], are suitable for predictive maintenance applications, thus suggesting their use for this specific case study. All the variables can be processed simultaneously to find criteria oriented on predictive maintenance of the whole cutting machines, and of each part such as the blade component. With the aim of undertaking an innovative business model based on servitization of its offering, industries producing cutting machines could provide predictive maintenance services by real time monitoring and AI data processing. The pilot company, FEMA. srl, is addressed on these services suitable to predict and reduce failures of the machines cutting polyurethane. At the beginning of the company activity, the maintenance or replacement of a component was activated only after a failure occurred and often when the component has reached the end of its life cycle. An unexpected machine downtime, seriously affects the progress of the production process, resulting in expensive consequences such as: (i) decrease of the Overall Equipment Effectiveness (OEE) of the machine and / or plant; (ii) damages (eg higher expenses for overtime work, lower revenues); (iii) delays in the production plan and in the fulfillment of orders; (iv) long production stops, if there is no availability in the warehouse of the spare parts necessary for the immediate repair of the machinery; (v) end customer dissatisfaction. To avoid these risks, the pilot company producing cutting machines is oriented to provide an advanced predictive maintenance service adopting some of the results achieved with the Smart District 4.0 (SD 4.0) project. SD 4.0 is a project supported by the Italian Ministry of Economic Development (MISE), with the aim of stimulating the widespread digitization processes of Small and Medium-sized Enterprises (SMEs) in some typical sectors as mechatronic.

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Corresponding author: Prof. Ing. Alessandro Massaro can be contacted at:

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