article

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Authors:

D. Yu. Pimenov,

T. MikolajczykAuthors Info & Claims

Journal of Intelligent Manufacturing, Volume 29, Issue 5

Pages 1045 - 1061

https://doi.org/10.1007/s10845-017-1381-8

Published: 01 June 2018 Publication History

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, $$V_{B}$$VB 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.

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Cited By

Wang XYan J(2024)Deep learning based multi-source heterogeneous information fusion framework for online monitoring of surface quality in milling processEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108043133:PAOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108043
Wang PTao HQi JLi P(2024)Machining quality prediction of complex thin-walled parts using multi-task dual domain adaptive deep transfer learningAdvanced Engineering Informatics10.1016/j.aei.2024.10264062:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.aei.2024.102640
Pimenov DBustillo AWojciechowski SSharma VGupta MKuntoğlu M(2022)Artificial intelligence systems for tool condition monitoring in machining: analysis and critical reviewJournal of Intelligent Manufacturing10.1007/s10845-022-01923-234:5(2079-2121)Online publication date: 12-Mar-2022
https://dl.acm.org/doi/10.1007/s10845-022-01923-2
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Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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Journal of Intelligent Manufacturing Volume 29, Issue 5

June 2018

212 pages

ISSN:0956-5515

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Wang XYan J(2024)Deep learning based multi-source heterogeneous information fusion framework for online monitoring of surface quality in milling processEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108043133:PAOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108043
Wang PTao HQi JLi P(2024)Machining quality prediction of complex thin-walled parts using multi-task dual domain adaptive deep transfer learningAdvanced Engineering Informatics10.1016/j.aei.2024.10264062:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.aei.2024.102640
Pimenov DBustillo AWojciechowski SSharma VGupta MKuntoğlu M(2022)Artificial intelligence systems for tool condition monitoring in machining: analysis and critical reviewJournal of Intelligent Manufacturing10.1007/s10845-022-01923-234:5(2079-2121)Online publication date: 12-Mar-2022
https://dl.acm.org/doi/10.1007/s10845-022-01923-2
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Bustillo APimenov DMia MKapłonek W(2021)Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teethJournal of Intelligent Manufacturing10.1007/s10845-020-01645-332:3(895-912)Online publication date: 1-Mar-2021
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