Improvement of surface roughness models for face milling operations through dimensionality reduction

Authors:
Maciej Grzenda;Andres Bustillo;Guillem Quintana;Joaquim Ciurana
Affiliations:
Faculty of Mathematics and Information Science, Warsaw University of Technology, Warszawa, Poland;Department of Civil Engineering, University of Burgos, Burgos, Spain;ASCAMM Technology Centre, Parc Tecnologic del Valles, Av. Universitat Autonoma, Barcelona, Spain;Department of Mechanical Engineering and Industrial Construction, University of Girona, Girona, Spain
Venue:
Integrated Computer-Aided Engineering
Year:
2012

Citing 19
Cited 0

A neural network model for selecting machining parameters in fixture design

Integrated Computer-Aided Engineering
Data Mining Methods and Models

Data Mining Methods and Models
View invariant head recognition by Hybrid PCA based reconstruction

Integrated Computer-Aided Engineering
Support Vector Machines for Visualization and Dimensionality Reduction

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A Bayesian network model for surface roughness prediction in the machining process

International Journal of Systems Science
Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

Expert Systems with Applications: An International Journal
Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process

Expert Systems with Applications: An International Journal
Surface roughness prediction in machining using soft computing

International Journal of Computer Integrated Manufacturing
Nonlinear Dimensionality Reduction

Nonlinear Dimensionality Reduction
A PCA-based technique to detect moving objects

SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding

International Journal of Computer Vision
A multi-criteria optimization framework for industrial shop scheduling using fuzzy set theory

Integrated Computer-Aided Engineering
Enhanced probabilistic neural network with local decision circles: A robust classifier

Integrated Computer-Aided Engineering
Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles

Integrated Computer-Aided Engineering
Prediction-oriented dimensionality reduction of industrial data sets

IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Surface roughness monitoring application based on artificial neural networks for ball-end milling operations

Journal of Intelligent Manufacturing
Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

Journal of Intelligent Manufacturing
A non-linear quality improvement model using SVR for manufacturing TFT-LCDs

Journal of Intelligent Manufacturing
A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Surface roughness generation is influenced by many complex and interrelated factors. Moreover, in real industrial conditions many different milling tools have to be used to create a final product. Hence, the acquisition of experimental data used to set up artificial intelligence models of individual tools is a complicated task. The aim of this paper is to present a new strategy to improve the artificial intelligence models used to predict surface roughness with small datasets. The new strategy proposed in this paper relies on dimensionality reduction to minimise the number of experiments required to train the models. Unlike in most other approaches, the dimensionality reduction is not applied to set an a priori determined dimension. In the proposed approach, the scale of dimensionality reduction is controlled by the quality of roughness prediction models created with the transformed data. This strategy has been tested on high torque milling operations using multilayer perceptrons i.e. the most frequently used artificial intelligence models for this task. Experiments were conducted to obtain data to train the models. Finally, a comparison was made of the models' performance with and without dimensionality reduction based on Principal Component Analysis, which confirmed the merits of the proposed technique.