Prediction of surface roughness in end milling using swarm intelligence

A new technique from EC (evolutionary computation), PSO (particle swarm optimization), is implemented to model the end milling process and predict the resulting surface roughness. Data is collected from CNC cutting experiments using DOE approach. The data is used for model calibration and validation...

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Bibliographic Details
Main Authors: El-Mounayri, H., Dugla, Z., Haiyan Deng
Format: Conference Proceeding
Language:English
Subjects:
Calibration
Computer numerical control
Evolutionary computation
Feeds
Milling
Particle swarm optimization
Predictive models
Rough surfaces
Surface roughness
US Department of Energy
Online Access:Request full text
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Description
Summary:A new technique from EC (evolutionary computation), PSO (particle swarm optimization), is implemented to model the end milling process and predict the resulting surface roughness. Data is collected from CNC cutting experiments using DOE approach. The data is used for model calibration and validation. The inputs to the model consist of feed, speed and depth of cut while the output from the model is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.
DOI:10.1109/SIS.2003.1202272