An Artificial Neural Network Approach to Surface Waviness Prediction in Surface Finishing Process by Chi Ngo ECE/ME 539 Class Project.

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Presentation transcript:

An Artificial Neural Network Approach to Surface Waviness Prediction in Surface Finishing Process by Chi Ngo ECE/ME 539 Class Project

Problem description: In order to control the surface finishing results in automated surface finishing process, it is important to specify the relationship between surface waviness and cutting parameters such as feed rate, cross feed, tool displacement, and spindle speed. In this project, an artificial neural network is used to predict surface waviness (output) from feed rate, cross feed, tool displacement, and spindle speed (inputs).

Outline: The data file consists of six columns. The first four columns represent feed rate, cross feed, tool displacement, and spindle speed, and are used as feature vectors. The last two columns represent depth of cut and surface waviness, and are used as target vectors. I have collected twenty-eight real sets of data performed on actual CNC machine and workpieces. These sets of data will be used to train and test the neural network.

Method: Back propagation perceptron learning would work best for this application. A multi-layer perceptron neural network back propagation algorithm with one hidden layer is implemented in this project. Since this is application problem, bpappro.m is used with some modifications. Multi-layer perceptron Input vector Target vector output

Results: parameters used that produce best results: For depth of cut prediction: -Learning rate (between 0 and 1) = 0.3 -Momentum constant (between 0 and 1) = 0.8 -Hidden neurons: 15 -Maximum number of epochs to run = 800 -Epoch size (# of samples) = 25 Training data set results: -Mean square error = Maximum absolute error =

For waviness prediction: -Learning rate (between 0 and 1) = 0.2 -Momentum constant (between 0 and 1) = 0.8 -Hidden neurons: 24 -Maximum number of epochs to run = 800 -Epoch size (# of samples) = 26 Training data set results: -Mean square error = Maximum absolute error = 3.839

Conclusion/Discussion: Multi-layer perceptron neural network back propagation algorithm seems to work better than time series prediction and other methods that could be used for this project. But still, the mean square error is big. It is very difficult for the program to converge. Other existing mathematical models to predict depth of cut and waviness seem to work much better than my neural network in this project.