By Jesъs Arбmburo and Antonio Ramнrez Treviсo
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Extra info for Advances in Robotics Automation and Control
When the workpiece reaches this point, it has high aggregated value, which makes a possible rejection very expensive. Monitoring of machining processes is mandatory for their optimization and control. Acoustic emission (AE) has become an increasingly popular monitoring technique. , 2002). Electrical power signals have also been largely used in grinding researches. The signal can be monitored either by the electric current of the electric motor or by the product between voltage and current signals, which gives the electrical power consumed by the electric motor.
An 40x40 8-bit pixel colour image was used as input image. 232 (R2006a) has been used but in order to improve the speed of the proposed algorithm a prototype of C++ code might be used. 2 illustrates the sequence of the proposed algorithm’s steps. After the character’s image is scanned in the system the JPEG approximation MATLAB code will produce a vector. This vector is assumed to uniquely represent input image since it carries the important details of that image. 3 shows a sample for letter Sheen and letter Ain.
The quantification of Predicting Surface Roughness in Grinding using Neural Networks 41 errors was done point by point through the computation of the absolute value of the difference between the measured values of surface roughness and the values given by the neural network. That was done for each of the 15 regions along the workpiece and for each workpiece. Figure 8 shows the average values of square errors for all 15 regions along the workpiece length and 3 repetitions of each grinding condition as well as the variation of the error in each of those conditions.
Advances in Robotics Automation and Control by Jesъs Arбmburo and Antonio Ramнrez Treviсo