Abstract:
This research has presented an optimum model for surface roughness prediction in a shop floor machining
operation. The proposed solution is premised on difference analysis enhanced with a feedback control model
capable of generating transient adaptive weights until a converging set point is attained. The surface roughness
results utilized herein were adopted from two prior experiments in the literature. The design of experiment herein
is premised on three cutting parameters in both experimental scenarios viz: feed rate, cutting speed and depth of
cut for experimental dataset one and cutting speed, feed rate and flow rate for experimental dataset two. Three
experimental levels were considered in both scenarios resulting in twenty-seven outcomes each. The simulation
trial anchored on Matlab software was divided into two sub-categories viz: prediction of surface roughness for
cutting combinations with vector points off the edges of the mesh referred to as off-edge cutting combinations
(Off-ECC) and recovery of cutting combinations with positions on the edges of the mesh referred to as on-edge
cutting combinations (On-ECC). The proposed hybrid scheme of difference analysis with feedback control premised
on the use of dynamic weights produced an accurate output in comparison with the abductive, regression
analysis and artificial neural network techniques as earlier utilized in the literature. The novelty of the proposed
hybrid model lies in its high degree of prediction and recovery of existing datasets with an error margin
approximately zero. This predictive efficacy is premised on the use of set points and transient dynamic weights for
feedback iterations. The proposed solution technique in this research is quite consistent with its outputs and
capable of working with very small to complex datasets.
