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Research Papers

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

[+] Author and Article Information
Dazhong Wu

Department of Industrial and
Manufacturing Engineering,
National Science Foundation
Center for e-Design,
Pennsylvania State University,
University Park, PA 16802
e-mail: dxw279@psu.edu

Connor Jennings

Department of Industrial and
Manufacturing Engineering,
National Science Foundation
Center for e-Design,
Pennsylvania State University,
University Park, PA 16802
e-mail: connor@psu.edu

Janis Terpenny

Department of Industrial and
Manufacturing Engineering,
National Science Foundation
Center for e-Design,
Pennsylvania State University,
University Park, PA 16802
e-mail: jpt5311@psu.edu

Robert X. Gao

Department of Mechanical and
Aerospace Engineering,
Case Western Reserve University,
Cleveland, OH 44106
e-mail: robert.gao@case.edu

Soundar Kumara

Department of Industrial and
Manufacturing Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: skumara@psu.edu

1Corresponding author.

Manuscript received October 25, 2016; final manuscript received March 13, 2017; published online April 18, 2017. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 139(7), 071018 (Apr 18, 2017) (9 pages) Paper No: MANU-16-1567; doi: 10.1115/1.4036350 History: Received October 25, 2016; Revised March 13, 2017

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

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Figures

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Fig. 1

Tool wear prediction using a feed-forward back-propagation (FFBP) ANN

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Fig. 2

Tool wear prediction using SVR

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Fig. 3

Tool wear prediction using an RF

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Fig. 4

Binary regression tree growing process

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Fig. 5

Experimental setup

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Fig. 6

Comparison of observed and predicted tool wear using an ANN with 16 neurons in the hidden layer (termination criterion: tolerance is equal to 1.0 × 10−4)

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Fig. 7

Comparison of observed and predicted tool wear using SVR (termination criterion: slack variable or tolerance ξ is equal to 0.001)

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Fig. 8

Comparison of observed and predicted tool wear using RFs (termination criterion: minimum number of samples in each node is equal to 5)

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Fig. 9

Tool wear against time (cut) using RFs

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Fig. 10

Comparison of training times

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Fig. 11

Comparison of MSEs

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Fig. 12

Comparison of R-squared errors

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