On Line Detection Technology of the Hardness of Cast Iron Parts Based onMachine Vision

doi:10.3901/JME.2017.01.157

Abstract

Abstract: In order to realize online-detection of casting the Brinell hardness, automatic measurement system for the hardness based on machine vision, is applied to collect the Indentation image, to study the algorithm of image filtering, image contour diameter extraction and diameter coefficient calibration. According to the characteristics of the indentation image, a snake model of indentation contour extraction algorithm based on particle swarm is proposed. The indentation diameter calibration coefficient is introduced to solve the conversion relation between the indentation diameter pixels and indentation diameter in the visual measurement, to achieve the least-squares fitting what can improve the measurement accuracy. The online-detection device of the Brinell hardness is used to test 180-210 HBW standard hardness test block. Test shows that the average error is 0.72%, the measurement precision is between ±2 HBW, the standard deviation is 125 HBW, the device has good repeatability and high precision. So the device can meet the online-detection requirement of the hardness of castings.

Key words: error analysis, image processing, machine vision, Brinell hardness

SHAN Zhongde, ZHANG Fei, REN Yongxin, ZHANG Jing, NIE Jungang. On Line Detection Technology of the Hardness of Cast Iron Parts Based onMachine Vision[J]. Journal of Mechanical Engineering, 2017, 53(1): 157-164.

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