2018
Pimenov, Yu. D; Bustillo, A; Mikolajczyk, T
Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth Journal Article
In: Journal of Intelligent Manufacturing, vol. 29, no. 5, pp. 1045–1061, 2018, ISSN: 1572-8145.
Abstract | Links | BibTeX | Tags: Cutting power, Face milling Wear, Processing time, Random forest, surface roughness
@article{Pimenov2018,
title = {Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth},
author = {Yu. D Pimenov and A Bustillo and T Mikolajczyk},
url = {https://doi.org/10.1007/s10845-017-1381-8},
doi = {10.1007/s10845-017-1381-8},
issn = {1572-8145},
year = {2018},
date = {2018-06-01},
journal = {Journal of Intelligent Manufacturing},
volume = {29},
number = {5},
pages = {1045–1061},
abstract = {Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, $$V_B$$ V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.},
keywords = {Cutting power, Face milling Wear, Processing time, Random forest, surface roughness},
pubstate = {published},
tppubtype = {article}
}
Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, $$V_B$$ V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.