Resonance effect in chatter formation in metal cutting

Machine tool chatter is a type of intensive self-excited vibrations of individual components of Machine-Tool-Fixture-Work (MTFW) system. Chatter causes unwanted excessive vibratory motion in between the tool and the work-piece causing adverse effects on the product quality and machine-tool and tool life. In addition to the damage of the work-piece surface due to chatter marks, the occurrence of severe chatter results in many adverse effects, which include poor dimensional accuracy of the work-piece, reduction of tool life, and damage to the machine. There are many sources of vibration in the machine tools, like unbalanced rotating forces in the drives of the machine tools, transmission of vibrating forces from neighbourhood to jobs and tools, fluctuations in the cutting forces, variable chip thickness, chip formation instability etc. causing self-excited vibration. However, it needs to be mentioned that chatter is not the only vibration phenomenon during practical cutting conditions; there are other forms of vibration evident in metal cutting, which include free vibration and forced vibration. These vibrations do not pose any fundamental problems to the machinist because when the sources of the problem have been identified, they can be eliminated, at least in theory if not always practically. This just goes to show that in engineering the gap between theoretical knowledge and practical application is often difficult to bridge (Tobias 1965). Though the application of certain conservative cutting parameters may help to avoid chatter but that would lead to loss of productivity. As high productivity has become important in many applications, the industry has started seeking for less conservative chatter-free cutting parameters not to compromise with the material removal rate. Although chatter stability limits for certain cutting conditions can be found through experiments, they are, however, time-consuming and expensive. As an alternative, a theoretical model considering the dynamics in machining processes can be more costeffective and efficient than the experimental approach. Hence the development of a reliable chatter model can significantly contribute to achieving high productivity in machining operations. Although the phenomenon of chatter has been extensively investigated over the past 100 years but most of the research works were concentrated on the basic theory of chatter, or the role of the structural dynamics of the machine tool on chatter. The phenomenon was not looked at from the perspective of interaction between the inherent instabilities of the chip formation process during metal cutting and the natural vibrations of the elastic system components of the machine tool. Hence there is a need for in-depth understanding of the chatter phenomena, its inherent nature, the causes of its formation, the factors that influence its appearance and help in maintaining it and the possible ways of avoiding its occurrence during metal cutting the methods of chatter suppression. An attempt has been made in this book to present first the basic understandings on the metal cutting process and the methods of investigation of chip and the chip-tool contact processes. The existing classification of the chips has been reviewed and a new classification is presented. The instabilities of chip formation under different cutting conditions has been looked into more carefully with special emphasis on the serrated chips, since most chatter problems in metal cutting are believed to be related to serrated chip formation. The causes and mechanisms of serrated chip formation have also been included. Classification of serrated chips, the frequencies of their formation and methods of determining these frequencies have been discussed. Since the machine-tool-work-fixture is considered to be a closed loop system, any form of vibration appearing during metal cutting would involve vibration of any one or more of the components of this system. Hence, the prominent mode (natural) frequencies of the system are considered to be important and these will be discussed in detail and the behavior of the system under cutting and non cutting conditions will be presented. The main objective of the book will be to present convincingly the interaction between the inherent instabilities of the chip formation process and the natural vibrations of the prominent system components to explain the causes of formation chatter in different cutting speed ranges and in cutting different metals and alloys under wide variety of cutting conditions. This is believed to establish a new understanding on the mechanism of chatter formation based on the proposed ‘resonance’ concepts of chatter formation as opposed to the existing ‘regenerative’ chatter concept

The role of tool geometry in process damped milling

The complex interaction between machining structural systems and the cutting process results in machining instability, so called chatter. In some milling scenarios, process damping is a useful phenomenon that can be exploited to mitigate chatter and hence improve productivity. In the present study, experiments are performed to evaluate the performance of process damped milling considering different tool geometries (edge radius, rake and relief angles and variable helix/pitch). The results clearly indicate that variable helix/pitch angles most significantly increase process damping performance. Additionally, increased cutting edge radius moderately improves process damping performance, while rake and relief angles have a smaller and closely coupled effect

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection

Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

A simulated investigation on the machining instability and dynamic surface generation

In this paper, the authors propose the generic concept of machining instability based on the analysis of all kinds of machining instable behaviors and their features. The investigation covers all aspects of the machining process, including the machine tool structural response, cutting process variables, tooling geometry and workpiece material property in a full dynamic scenario. The paper presents a novel approach for coping with the sophisticated machining instability and enabling better understanding of its effect on the surface generation through a combination of the numerical method with the characteristic equations and using block diagrams/functions to represent implicit equations and nonlinear factors. It therefore avoids the lengthy algebraic manipulations in deriving the outcome and the solution scheme is thus simple, robust and intuitive. Several machining case studies and their simulation results demonstrate the proposed approach is feasible for shop floor CNC machining optimisation in particular. The results also indicate the proposed approach is useful to monitor the machining instability and surface topography and to be potentially applied in adaptive control of the instability in real time