The Polycrystalline Cubic Boron Nitride (PCBN) cutting tools has have been developed for high speed machining in modern automation manufacture. The machining surface roughness is regarded as an important criterion to assess PCBN cutting tools performance. There are too many problems in conventional detection method. In order to solve that problem, we present a new way that is based on image analysis of machining surface texture to assess surface roughness. The new method is consisted of three steps. It captures surface texture image when machining is finished or pauses. Firstly, RGB histogram is adopted to analyze image pixel information. This means takes advantage of histogram technique and provides more pixel distribution information than gray histogram. Secondly, unsupervised texture segmentation is used based on resonance algorithm. Thirdly, a new estimation parameter E that is the density of surface contour peak is put forward to estimate machining surface roughness.
The detecting instrument for surface quality of steel ball bases on embedded control system and image detection technique, and is applied to detect surface defect region of steel ball in bearing. Its control system requires excellent real-time character and control accuracy. This paper puts forward a new design for controller of detecting instrument. We adopted TMS320LF2407A which produced by company TI as main processor, and integrated CPLD to develop an embedded controller. We used the Ziegler–Nichols tuning methods to get PID control and designed hardware circuit. We realized the function of correlative logic elements through programming, and constructed an embedded multitask operating system based on the transplant of μC/OS-II on TMS320LF2407A. It solved problems about intricate structure and bad real-time character existed in traditional control module. The result of simulation and experiment indicates that this control system satisfied excellently the requirement of high speed and real-time image detection.
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