Chatter is a self-excited vibration that can occur during machining operations and become a common limitation to productivity and part quality. For this reason, it has been a topic of industrial and academic interest in the manufacturing sector for many years. A great deal of research has been carried out since the late 1950s to solve the chatter problem. Researchers have studied how to detect, identify, avoid, prevent, reduce, control, or suppress chatter. This paper reviews the state of research on the chatter problem and classifies the existing methods developed to ensure stable cutting into those that use the lobbing effect, out-of-process or in-process, and those that, passively or actively, modify the system behaviour.

Chatter in machining processes: A review

This book equips the reader to understand every important aspect of the dynamics of rotating machines. Will the vibration be large? What influences machine stability? How can the vibration be reduced? Which sorts of rotor vibration are the worst? The book develops this understanding initially using extremely simple models for each phenomenon, in which (at most) four equations capture the behavior. More detailed models are then developed based on finite element analysis, to enable the accurate simulation of the relevant phenomena for real machines. Analysis software (in MATLAB) is associated with this book, and novices to rotordynamics can expect to make good predictions of critical speeds and rotating mode shapes within days. The book is structured more as a learning guide than as a reference tome and provides readers with more than 100 worked examples and more than 100 problems and solutions.

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Dynamics of rotating machines

Chatter vibrations are present in almost all cutting operations and they are major obstacles in achieving desired productivity. Regenerative chatter is the most detrimental to any process as it creates excessive vibration between the tool and the workpiece, resulting in a poor surface finish, high-pitch noise and accelerated tool wear which in turn reduces machine tool life, reliability and safety of the machining operation. There are various techniques proposed by several researchers to predict and detect chatter where the objective is to avoid chatter occurrence in the cutting process in order to obtain better surface finish of the product, higher productivity and tool life. In this paper, some of the chatter stability prediction, chatter detection and chatter control techniques for the turning process are reviewed to summarize the status of current research in this field. The objective of this review work is to compare different chatter stability prediction, chatter detection and chatter control techniques to find out most suitable technique/s and to identify a research scope in this area. One scope of research has been identified as establishing a theoretical relationship between chatter vibration and tool wear in order to predict tool wear and tool life in the presence of chatter vibration.

A review of chatter vibration research in turning

Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Application of soft computing techniques in machining performance prediction and optimization: a literature review

Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption

Optimization of electric discharge machining using simulated annealing

Damage detection in vibrating bodies using genetic algorithms

Neural Networks: Algorithms and Applications

This paper proposes a methodology for selecting optimum machining parameters in multi-pass turning using particle swarm intelligence Often, multi-pass turning operations are designed to satisfy several practical cutting constraints in order to achieve the overall objective, such as production cost or machining time Compared with the standard handbook approach, computer-aided optimization procedures provide rapid and accurate solutions in selecting the cutting parameters In this paper, a non-conventional optimization technique known as particle swarm optimization (PSO) is implemented to obtain the set of cutting parameters that minimize unit production cost subject to practical constraints The dynamic objective function approach adopted in the paper resolves a complex, multi-constrained, nonlinear turning model into a single, unconstrained objective problem The best solution in each generation is obtained by comparing the unit production cost and the total non-dimensional constraint violation among all of the particles The methodology is illustrated with examples of bar turning and a component of continuous form

Optimization of multi-pass turning using particle swarm intelligence

Vibration, buckling and bending behavior of functionally graded multi-walled carbon nanotube reinforced polymer composite plates using the layer-wise formulation

J. Srinivas

Papers

Optimization of electric discharge machining using simulated annealing

Damage detection in vibrating bodies using genetic algorithms

Neural Networks: Algorithms and Applications

Optimization of multi-pass turning using particle swarm intelligence

Vibration, buckling and bending behavior of functionally graded multi-walled carbon nanotube reinforced polymer composite plates using the layer-wise formulation