# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 1 Introduction  
The maintenance and periodic replacement of railway wheelsets represents a significant cost faced by train operating companies. Railway vehicle wheels wear away relatively slowly and could be expected to last for many years (sometimes more than 20 years) based only on wear considerations. However, they are also subject to tread damage caused by wheel slide events, rolling contact fatigue, flange wear, and tread roll over. These events are more likely in the case of poorly maintained railways, where debris is accumulated on highly eroded tracks. Wheels, therefore, require regular re-profiling by machining on a wheel lathe. Massive maintenance service on a wheelset is done one or two times in its entire service life. The reprofiling of the worn wheels and the machining of the brake-disc are the usual main corrective actions. This reprofiling operation is carried out by reducing the diameter of the wheel in accordance with local standards [1; 2]. For train operating companies, an optimum strategy for wheel maintenance and lathe operation is required to achieve two main goals: maintaining wheels within operational safety and efficiency limits, and minimizing the costs. Thus, performing the repair operation as fast as possible while retaining the tolerances of the wheel profile is essential. The problem arises when trying to reshape severely damaged freight train wheelsets, where the high deterioration of the tread profile requires a high depth of cut turning operation. This fact, linked to the usual forged steel material of the wheel and its hardened outer layer, makes machining of the parts very demanding. For thisreason, chatter vibrations that compromise wheel surface finish and machine-tool life expectancy and reliability may arise during turning. It is usual to reprofile a freight wheelset in several cutting passes instead of one to avoid chattering, leading to a significant loss of productivity. The first step in designing a smart system for chatter suppression is the automatic identification of chatter vibrations. Most of the available in-process methods for chatter identification rely on extracting certain features from the acoustic or vibration signals and comparing them against some predefined chatter benchmarks. They can be broadly divided into two groups: categorization of feature matrix extraction (signal spectrum and decompositions) and classifiers (supervised and non-supervised) [3; 4]. In the first group, different methods have been proposed based on analysis of the signal spectrum [5], and decomposition of the signal based on wavelet packet transform [6] or empirical mode decomposition [7]. The general detection of chatter vibrations is a complex problem, and alternative types of classifier have been applied, including the hidden Markov model [8] and neural network classification [9]. Chatter vibrations in turning operations have been extensively studied, and many solutions to overcome the problem have been presented [10]. It is well known that the stability lobe diagram can be used to optimize the cutting capacity by selecting the correct process parameters [11; 12; 13; 14]. The selection of an adequate tool-tool holder geometry based on stability diagrams can also increase the cutting capacity [15; 16]; however, this solution is rarely applicable to structural chattering cases in turning. The process damping effect raises the effective damping by increasing the friction between tool and workpiece [17], and can be applied to stabilize the cutting [18]. The increase of stiffness of the vibrating structure could also be a feasible way to avoid chatter, especially if there is a dominant mode, although massive structural changes are sometimes required to achieve a substantial effect [19; 10]. Passive and active damping solutions, on the other hand, can be used to add damping to the critical mode/s by means of external subassembly devices, leading to a higher stability limit [20; 21; 22; 23; 24].