Abstract: Metal matrix composites have been increasingly used
as materials for components in automotive and aerospace industries
because of their improved properties compared with non-reinforced
alloys. During machining the selection of appropriate machining
parameters to produce job for desired surface roughness is of great
concern considering the economy of manufacturing process. In this
study, a surface roughness prediction model using fuzzy logic is
developed for end milling of Al-SiCp metal matrix composite
component using carbide end mill cutter. The surface roughness is
modeled as a function of spindle speed (N), feed rate (f), depth of cut
(d) and the SiCp percentage (S). The predicted values surface
roughness is compared with experimental result. The model predicts
average percentage error as 4.56% and mean square error as 0.0729.
It is observed that surface roughness is most influenced by feed rate,
spindle speed and SiC percentage. Depth of cut has least influence.
Abstract: End milling process is one of the common metal
cutting operations used for machining parts in manufacturing
industry. It is usually performed at the final stage in manufacturing a
product and surface roughness of the produced job plays an
important role. In general, the surface roughness affects wear
resistance, ductility, tensile, fatigue strength, etc., for machined parts
and cannot be neglected in design. In the present work an
experimental investigation of end milling of aluminium alloy with
carbide tool is carried out and the effect of different cutting
parameters on the response are studied with three-dimensional
surface plots. An artificial neural network (ANN) is used to establish
the relationship between the surface roughness and the input cutting
parameters (i.e., spindle speed, feed, and depth of cut). The Matlab
ANN toolbox works on feed forward back propagation algorithm is
used for modeling purpose. 3-12-1 network structure having
minimum average prediction error found as best network architecture
for predicting surface roughness value. The network predicts surface
roughness for unseen data and found that the result/prediction is
better. For desired surface finish of the component to be produced
there are many different combination of cutting parameters are
available. The optimum cutting parameter for obtaining desired
surface finish, to maximize tool life is predicted. The methodology is
demonstrated, number of problems are solved and algorithm is coded
in Matlab®.
Abstract: Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.