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To manufacture parts with nano-or microscale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.
Access and use of this website and the material on it are subject to the Terms and Conditions set forth at A fast-response thin film thermocouple to measure rapid surface temperature changes Heichal, Yoav; Chandra, Sanjeev; Bordatchev, Evgueni
NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=21272515&lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=21272515&lang=fr READ THESE TERMS AND CONDITIONS CAREFULLY BEFORE USING THIS WEBSITE.http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca.
Porous metals, typically produced through powder metallurgy, represent a class of relatively new materials with wide industrial applications, lately extending into the microscale domain. Although produced in near-net shapes, most components fabricated from these materials still require some form of secondary machining. Despite the progress made in the field, relatively little is known either on the inherent cutting mechanism or on the behaviour of these materials under micromachining conditions. The present study reviews the main cutting theories proposed in macroscale machining, along with one of the primary parameters used to describe its machinability performances, namely cutting forces. Then, the feasibility of macroscale concepts is discussed in the context of micromachining technology that is characterized by comparable tool and pore sizes. The microslot cutting experiment performed in a porous titanium sample outlined the relative interplay between the magnitude of the cutting force and porosity of the material. Based on this, it was concluded that the impact of structural porosity on cutting forces experienced during micromachining is significant and therefore further in-depth investigations will be required.
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