In the automotive market, there is a strong interest for the production of sidewinder keys made of Nickel-coated Nickel-Silver alloy. The Nickel coating improves wear resistance and brightness of the key, nevertheless it reduces the machinability and the tool life when milling the key groove is short. In this work, several innovative tool coatings were applied on conventional mills to enhance the machinability of Nickel-coated Nickel-Silver alloy. Tool life and burr formation obtained with the tested tools were investigated and discussed. Some of the coatings proved to be very promising for this application thank to their excellent tool life. Specifically, the PCD tool was the most interesting since the tool life was significantly longer than conventional carbide tool.
Automation of engineering procedures for the development of new manufacturing processes is of great importance in modern competitive conditions. For example, metalworking companies would greatly benefit from the development of methods for automatic generation, testing and optimization of part programs for machining operations. Indeed, the generation of part programs-even by using CAM software-does still require strong human intervention and it is basically a best guess approach with minimum optimization. Moreover, further refinement and correction of the part program on the machine tool is often necessary. Machining operations are generally based on a large number of parameters and therefore optimization strategies should be able to deal with high-dimensional spaces and disjoint domains. In this paper, two swarm intelligence optimization algorithms-particle swarm optimization (PSO) and artificial bee colony (ABC)-have been applied for optimizating the part program of a complex turning part. The optimizers were implemented in a framework for automatic part program generation, realistic simulation, and feasibility analysis. The results evidenced that both approaches were capable of optimizing efficiently the part program, and that the optimization time of the PSO approach on modern computers may be suitable for application in production
Modal analysis and parametric model identification play a fundamental role in many fields, especially for the optimization and vibration control of civil structures and complex mechanical systems. In the last decades these techniques have been frequently applied for the development of innovative cutting tools and CNC machine tools. Although several methodologies are available in literature for parametric model identification, there is still a lack of an effective and robust algorithm. In this paper a new algorithm for automatic identification of a parametric model of a linear dynamic Single Input Single Output system with Multiple Degrees of Freedom is presented. Some recent approaches perform the Wavelet decomposition of the Impulse Response in the time-frequency domain. Here a Wavelet-like decomposition of the Frequency Response in the frequency-damping domain is introduced for vibration modes recognition. Afterwards, advanced statistical approaches are applied for vibration modes selection and model generation. The method was successfully tested on a complicated frequency response characterized by several vibration modes, which was obtained from experimental modal analysis performed on a circular saw blade.
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