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Development and Modeling of a Slope Insensitive Combine Cleaning Shoe

出版	Iowa State University, 2005
URL	http://books.google.com.hk/books?id=Y8fOPgAACAAJ&hl=&source=gbs_api

註釋A major weakness of the cleaning shoe in today's combines is its performance on sloping terrain. This is due in part to the fact that the basic action of a combine harvester's cleaning system has not changed over the last hundred years. A combine's grain cleaning system is still a flat perforated oscillating chaffer and sieve element coupled with a well distributed air blast to create a fluidized bed that allows separation of grain from MOG (Materials Other than Grain). Sloped terrain allows material to concentrate on the downhill side of the shoe, resulting in a breakdown of the fluidized bed and loss of grain. The first step in this study was to develop a Simulink model that simulated the travel of a single corn kernel on a combine chaffer with three dimensional shake and variable shake speed, in order to evaluate its effects on the kernel's travel path while on a simulated slope. The model showed that on slopes, the shake may be adjusted to replicate level land travel. A test stand that incorporated three dimensional shake, variable fan speed, and variable shake speed was constructed based on the results of the analytical model. The slope insensitive test stand was capable of simulating any combination of transverse and longitudinal slopes up to 17° at feedrates up to 50 tons per hour. Two central composite designs of experiments and one modified central composite design were carried out and analyzed to develop a second order response surface that characterized the grain loss of the test stand in response to variations in environmental and control factors. The six variable parameters were feedrate, longitudinal slope, fan speed, shake speed, transverse slope, and side input. The test stand proved capable of providing level land performance on sloped terrain, with loss levels at or near baseline values for a production combine. The response model derived from the statistical analysis followed the measured results of the test stand well, and was capable of accurately predicting grain loss based on values of the six variable parameters.