abstract
- © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.Tool condition monitoring systems in High Speed Machining (HSM) are of great importance to maintain the quality of the products and diagnose the useful life of the tools. These systems are highly demanded for the suppliers of molds and dies in the aeronautic and automotive industry. A new methodology to diagnose the tool wear condition by using a Stacked Sparse AutoEncoder(SSAE) neural network is presented. The methodology evaluates different signals obtained from different sensors (accelerometer, dynamometer and acoustic emission), which were recorded during the machining of aluminum workpieces, with different hardness, tools and cutting trajectories. The methodology presents a fairly acceptable performance (99.63%) in the prediction of the tool wear condition especially with the signals of the acoustic emission. SSAE neural network outperforms traditional neural network.