Surface roughness prediction using a hybrid scheme of difference analysis and adaptive feedback weights

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.

On-Line surface roughness classification for multiple CNC milling conditions based on transfer learning and neural network

“…Lu et al [12] employed the Gaussian process regression with the square exponential covariance function to predict the surface roughness, and the effects of process parameters on the surface roughness was expressed by the length-scale super hyperparameters of covariance function. Ayomoh et al [13] has presented a modelling technique based on a hybrid scheme of Gauss-Seidel difference analysis model combined with a feedback system linked to a dynamic weight generation, and it can deal with the datasets from simple to complex and give the predictions. Zhuo et al [14] proposed a feed-forward multilayer artificial neural network to predict the roughness based on the Levenberg-Marquardt backpropagation training algorithm, and effects of the cutting parameters on the roughness were analyzed and the feed rate shown a most significant influence on the surface roughness.…”