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

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.

An approach to tackling the environmental and health impacts of municipal solid waste disposal in developing countries.

Indiscriminate disposal of municipal solid waste in developing countries poses severe environmental and health threats. The study proposes a new method for dealing with these problems. The hybrid structural interaction matrix (HSIM) was used to prioritise major identifiable environmental health factors arising from improper solid waste disposal. The simplistic resource allocation model was adopted to ensure optimality in the allocation of resources to prioritised factors. The study indicates that tackling environmental health impacts from the most prioritised negative disposal factors through optimal allocation of resources, will either reduce or eliminate the impacts associated with subsequent less prioritised factors that are direct consequences of the highly prioritised negative factors. The method proposed will aid decision makers in knowing which set of systemic factors are to be given preference and to what extent at given periods in time.