To develope an intelligent automatic control system (ACS) based on machine vision and fuzzy logic techniques to control the performance of rice whitener. The developed ACS consisted of two main parts, namely hardware (including sampling unit, kernel singulation unit, image capturing unit, processor (computer), discharge pressure control unit and data acquisition unit), and software (including image processing, fuzzy inference and central control units).
Two important qualitative indices, degree of milling and percentage of broken kernels, were considered as input variables and the level of pressure on the discharge section of the whitening machine was selected as the output variable in the fuzzy inference unit. Results of the evaluations indicated that the developed ACS had 89.2% accuracy in determining the desired working conditions for the whitening machine. The total time of each monitoring round was, on the average, equal to 14.73 s, of which 6.4 s was devoted to kernel sampling and transporting the samples into the imaging chamber, 7.33 s for taking three images from the kernels, processing the captured images and executing the fuzzy inference process, and the remaining 1.5 s for making the adjustments in the level of pressure in the mechanism.
Based on this information and in contrast to the corresponding time spent by the human operator to perform a similar process, it was revealed that the performance speed of the ACS was, on average, 31.3% higher than that of the human operator. Evaluation of the samples obtained from the discharge section of the rice whitening machine at different stages of the control process showed that the decisions made by the developed ACS during the control process resulted in a satisfactory improvement in the quality of the output product.
rice destoner with special institutions, making the distance between the conical rotor and stator can be adjusted ,so as to change the stress intensity of work, to adopt to the various demand for corn flour grinding machine line.