Download Intelligent Production Machines and Systems - 2nd I*PROMS by Pham D.T., Eldukhri E.E., Soroka A.J. PDF
By Pham D.T., Eldukhri E.E., Soroka A.J.
I*PROMS 2005 is a web web-based convention. It presents a platform for featuring, discussing, and disseminating study effects contributed by way of scientists and commercial practitioners lively within the zone of clever structures and smooth computing suggestions (such as fuzzy good judgment, neural networks, evolutionary algorithms, and knowledge-based structures) and their program in several components of producing. constituted of a hundred peer-reviewed articles, this significant source presents instruments to assist corporations in achieving objectives serious to the way forward for manufacturing.I*PROMS is an eu Union-funded community that contains 30 associate agencies and greater than a hundred thirty researchers from universities, examine organisations, and corporations.- state of the art examine effects- major ecu researchers and business practitioners- accomplished number of listed and peer-reviewed articles in publication structure supported via a simple full-text CD-ROM with seek performance
Read or Download Intelligent Production Machines and Systems - 2nd I*PROMS Virtual International Conference 3-14 July 2006 PDF
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Additional info for Intelligent Production Machines and Systems - 2nd I*PROMS Virtual International Conference 3-14 July 2006
Example text
E. we know that the tool is - for example - in class 4 (Severe wear), when it may have reached class 5 (Breakage), or 6 (Chatters). 53 Class in ANN 8 2 3 3 3 4 3 3 3 3 3 3 3 3 3 possible. 7. Acknowledgement This research was supported through two grants by Ministry of Education of Romania [1, 7]. References [ 1] Balan, G, 2002, The monitoring of a lathe using an artificial neural network, Grant type A hr. , 2003, The monitoring simulation of a lathe, Mathematical & Computational Applications, an International Journal published by the Association for Scientific Research, Vol.
We consider this error to decrease in the future if we take several recordings in tables 4 and 5. 6- Conclusions The algorithm to monitor the tool wear making use of ANN proved efficient, the error range being below 5 percent. In the case of real monitoring, when the cutting is continuous, to avoid "thermal no-compensation" a cooling of the knife should be provided. However, water can cause trouble in the circuits of strain gauges, although they are protected (with Poxipol). Consequently, for this step of the experiment, the strain gauges will be removed and the cutting force components will be measured by averages of a KISLER device (Austria).
Snarled/short/short spiral chip form identification for fixed (vc2) or variable cutting speed (Vcl+Vc2) training sets, containing 210 or 420 training cases respectively, using a single cutting force component (Fc) or the integration of 3 cutting force components (F~+F/+Fp). NN error vs. number of training pairs for: (a) Vce and Fc with SR = 93%; (b) Vc2 and (F~+F/+-Fp)with SR = 100%; (c) (Vcl+Vce) and F,, with SR = 87%; (d) (vcl+Vce) and (F~+F/+Fp) SR = 98%. Furthermore, chip form identification was carried out through sensor data processing of single cutting force components (Fc, FU, or Fp) and sensor data integration of the 3 force components (F~+F/+Fp).