Research of Surface Roughness Network Data Collector System Based on ARM
|School||Harbin University of Science and Technology|
|Course||Communication and Information System|
|Keywords||Network embedded ARM anti-aliasing filtering TCP/IP|
Nowadays, computer and network have walked into the post PC age, and theembedded system has been widely applied in every walk of life. Under certainconditions, real-time monitoring running status of various kinds of industrialequipment can be realized through the network by combining the general industrialinstrumentation with a network data collector. In this way, all kinds of necessary dataat the scene can be measured on the remote site.Many of traditional industrialinstruments need to be measured at the scene, and the trouble can be solved throughusing the Internet. At the same time, this method can save a lot of cost for enterprises.This thesis is to study this network data acquisition system as the main content.In the course of surface roughness measurement, ARM9chip was chosen todesign a network data collector used for surface roughness measurement. It wasconnected with surface roughness measuring instrument in the common industry viaa serial port and equipped with the function of remote measurement.An importantpart is to build a suitable ARM9platform, select LINUX system as a developmentplatform for embedded development board, and build related hardware circuits, usingA/D converter and Ethernet extension interface for data transfer capabilities, as wellas prepare the appropriate TCP/IP communication protocol. Another important partof the subject after the A/D channel surface roughness of the finished collecting data,to design a digital anti-aliasing filter, collecting information on low-noise processing,Matlab simulation by anti-filtering software can show the before and after aliasingeffect.The aim of the soft data processing is to display data collection, so a appropriatedata processing software is required, then showed the waveform of the surfaceroughness data, so as to get filtering midline. At last, get workpiece surfaceroughness profile and assess the roughness parameters. Experimental analysis shows that the system could finish network data collection of surface roughness.