Optimization of wear behavior of straw combine blade through RSM and ANN models

The mechanization in the farming field is grooming in the Indian farming sector with an impressive acceptance rate. The straw combines are a few machines whose demand and acceptability are much better than any other farm machinery. These straw combines are used to recover the leftover crop residues for utilization as animal fodder and ultimately help in reducing crop burning. During operation, the blades of straw combines are under high temperature and a highly abrasive environment, leading to the degradation of the blade surface. In the present work, the blade material was tested, and wear behavior was optimized to obtain the optimal levels of parameters for minimized wear. The response surface methodology (RSM) based Box-Behnken design was used for the generating experimental design. The experimentation was conducted as dry sliding wear testing keeping load, sliding velocity, and time as process parameters at variable levels. The wear loss was obtained as a response parameter. The surface morphology was conducted by using a field emission scanning electron microscope. The artificial neural network (ANN) based model was used to validate the optimal response obtained through RSM. The comparative analysis of the RSM and ANN model shows a good agreement with the wear behavior of straw combine blades.