Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

The general trend towards lightweight components and stronger but difficult to machine materials leads to a higher probability of vibrations in machining systems. Amongst them, chatter vibrations are an old enemy for machinists with the most dramatic cases resulting in machine-tool failure, accelerated tool wear and tool breakage or part rejection due to unacceptable surface finish. To avoid vibrations, process designers tend to command conservative parameters limiting productivity. Among the different machining processes, turning is responsible of a great amount of the chip volume removed worldwide. This paper reports some of the main efforts from the scientific literature to predict stability and to avoid chatter with special emphasis on turning systems. There are different techniques and approaches to reduce and to avoid chatter effects. The objective of the paper is to summarize the current state of research in this hot topic, particularly (1) the mechanistic, analytical, and numerical methods for stability prediction in turning; (2) the available techniques for chatter detection and control; (3) the main active and passive techniques.Thanks are addressed to Basque country university excellence group IT1337-19. The authors wish to acknowledge also the financial support received from HAZITEK program, from the Department of Economic Development and Infrastructures of the Basque Government and from FEDER funds. This research was funded by Tecnologico de Monterrey through the Research Group of Nanotechnology for Devices Design, and by the Consejo Nacional de Ciencia y Tecnologia (CONACYT), Project Numbers 242269, 255837, 296176, and the National Lab in Additive Manufacturing, 3D Digitizing and Computed Tomography (MADiT) LN299129

Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model

Firstly, this paper computed the static and dynamic characteristics of common boring bars and constrained damping boring bars respectively, and the correctness of the computational model in time-frequency domain was also validated by experiments. Modal frequencies of constrained damping boring bars were obviously more than those of common boring bars, which could effectively avoid structural resonance in low frequency and had an obvious advantage in improving anti-vibration performance of boring bars. The absolute value of the maximum vibration acceleration of common boring bars was 13.1 m/s2, while the absolute value of the maximum vibration acceleration of constrained damping boring bars was 9.1 m/s2. The maximum vibration acceleration decreased by 30.5 %. The maximum vibration displacement of common boring bars was 5.2 mm and corresponding frequency was 201 Hz. The maximum vibration displacement of constrained damping boring bars was 2.3 mm and corresponding frequency was 235 Hz. When the analyzed frequency was lower than the frequency with the maximum vibration displacement, the displacement spectrum of common boring bars had more peak values. Thus, it was clear that constrained damping boring bars had an obvious advantage in improving vibration characteristics. The impact of cutting speed, feed rate and back cutting depth on vibration characteristics was studied respectively. Results showed that the vibration of constrained damping boring bars gradually decreased with the increase of cutting speed and gradually increased with the increase of feed rate and back cutting depth. In addition, the amplitude and frequency of vibration displacement spectrum of boring bars were basically unchanged no matter how cutting parameters changed. In order to quickly predict the vibration characteristic, BP neural network and PSO-BP neural network were respectively used to predict the cutting process of boring bars. When the iteration number of BP neural network was 300, iterative error was 0.00015 which was far more than the set target error. When the iteration number of PSO-BP neural network was 215, iterative error was converged to the set target error. Therefore, PSO-BP neural network had an obvious advantage in predicting the cutting process of boring bars. In addition, the predicted result of PSO-BP neural network was consistent with the experimental result, which showed that the neural network model in this paper was effective

Alternative experimental methods for machine tool dynamics identification: A review

An accurate machine dynamic characterization is essential to properly describe the dynamic response of the machine or predict its cutting stability. However, it has been demonstrated that current conventional dynamic characterization methods are often not reliable enough to be used as valuable input data. For this reason, alternative experimental methods to conventional dynamic characterization methods have been developed to increase the quality of the obtained data. These methods consider additional effects which influence the dynamic behavior of the machine and cannot be captured by standard methods. In this work, a review of the different machine tool dynamic identification methods is done, remarking the advantages and drawbacks of each method.The present work has been partially supported by the EU Horizon 2020 InterQ project (958357/H2020-EU.2.1.5.1.) and the CDTI CERVERA programme MIRAGED project (EXP-00,137,312/CER-20191001)

Review of AI‐based methods for chatter detection in machining based on bibliometric analysis

To improve the finish and efficiency of machining processes, researchers set out to develop techniques to detect, suppress, or avoid vibration chatter. This work involves tracing chatter detection techniques, from time–frequency signal processing methods (FFT, HHT, STFT, etc.), decomposition (WPD, EMD, VMD, etc.) to the combination with machine learning or deep learning models. A cartographic analysis was carried out to discover the limits of these different techniques and to propose possible solutions in perspective to detect chattering in the machining processes. The fact that human expert detects chatter using simple spectrograms is confronted with the variety of signal processing methods used in the literature and lead to possible optimal detecting techniques. For this purpose, the bibliometric tool R-Tool was used to facilitate a bibliometric analysis using specific means for quantitative bibliometric research and visualization. Data were collected from the Web of Science (WoS 2022) using particular queries on chatter detection. Most documents collected detect chatter with either transformation or decomposition techniques

IDENTIFICATION OF CHATTER VIBRATIONS AND ACTIVE VIBRATION CONTROL BY USING THE SLIDING MODE CONTROLLER ON DRY TURNING OF TITANIUM ALLOY (TI6AL4V)

In recent years, with the development of sensor technologies, communication platforms, cyber-physical systems, storage technologies, internet applications and controller infrastructures, the way has been opened to produce competitive products with high quality and low cost. In turning, which is one of the important processes of machining, chatter vibrations are among the biggest problems affecting product quality, productivity and cost. There are many techniques proposed to reduce chatter vibrations for which the exact cause cannot be determined. In this study, an active vibration control based on the Sliding Mode Control (SMC) has been implemented in order to reduce and eliminate chatter vibration, which is undesirable for the turning process. In this context, three-axis acceleration data were collected from the cutting tool during the turning of Ti6Al4V. Finite Impulse Response (FIR) filtering, Fast Fourier Transform (FFT) analysis and integral process were carried out in order to use the raw acceleration data collected over the system in control. The system is modeled mathematically and an active control block diagram is created. It is observed that chattering decreased significantly after the application of active vibration control. The surface quality formed by the amplitude of the graph obtained after active control has been compared and verified with the data obtained from the actual manufacturing result

Chatter mitigation in milling process using discrete time sliding mode control with type 2-fuzzy logic system

In order to achieve a high-quality machining process with superior productivity, it is very important to tackle the phenomenon of chatter in an effective manner. The problems like tool wear and improper surface finish affect the milling process and are caused by self-induced vibration termed as chatter. A strategy to control chatter vibration actively in the milling process is presented. The mathematical modeling of the process is carried out initially. In this paper, an innovative technique of discrete time sliding mode control (DSMC) is blended with the type-2 fuzzy logic system. The proposed active controller results in a significantly high mitigation of vibration. The DSMC is linked to the time-varying gain which is an innovative approach to mitigate chattering. The theorem is laid down which validates that the system states are bounded in the case of DSMC-type-2 fuzzy. Stability analysis is carried out using Lyapunov candidate. The nonlinearities linked with the cutting forces and damper friction are handled effectively by using the type-2 fuzzy logic system. The performance of the DSMC-type-2 fuzzy concept is compared with the discrete time PID (D-PID) and discrete time sliding mode control for validating the effectiveness of the controller. The better performance of DSMC-type-2 fuzzy over D-PID and DSMC-T1 fuzzy in the minimization of milling chatter are validated by a numerical analysis approach

Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

Chatter detection has become a prominent subject of interest due to its effect on cutting tool life, surface finish and spindle of machine tool. Most of the existing methods in chatter detection literature are based on signal processing and signal decomposition. In this study, we use topological features of data simulating cutting tool vibrations, combined with four supervised machine learning algorithms to diagnose chatter in the milling process. Persistence diagrams, a method of representing topological features, are not easily used in the context of machine learning, so they must be transformed into a form that is more amenable. Specifically, we will focus on two different methods for featurizing persistence diagrams, Carlsson coordinates and template functions. In this paper, we provide classification results for simulated data from various cutting configurations, including upmilling and downmilling, in addition to the same data with some added noise. Our results show that Carlsson Coordinates and Template Functions yield accuracies as high as 96% and 95%, respectively. We also provide evidence that these topological methods are noise robust descriptors for chatter detection

A TWO-DIAMETER HELICAL ENDMILL BEAM MODEL FOR TOOL TIP DYNAMICS PREDICTION WITH APPLICATION TO MILLING

The aim of this dissertation is to describe the dynamic response of helical endmill geometries to enable the use of receptance coupling substructure analysis (RCSA) to predict the tool tip vibration response of arbitrary tool-holder-spindle-machine combinations. The tool tip vibration response, or receptance, is a key input for milling stability prediction. Currently, a measurement is required to determine the tool tip receptance for each tool-holder-spindle-machine combination, which may not be possible in production environments. In the RCSA approach, the spindle receptances are measured once and archived, while the tool and holder are modeled. Tool tip receptances are predicted by analytically coupling the tool and holder models, described as equivalent diameter Timoshenko beams, to the archived spindle receptances. In this study, a two-diameter Timoshenko beam model approach is derived and applied to predict the dynamic response of the fluted portion of the endmill geometry