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Cloud-Based Machine Learning for Predictive Analytics: Tool Wear Prediction in Milling

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2016 IEEE International Conference on Big Data (Big Data)
978-1-4673-9005-7/16/$31.00 ©2016 IEEE 2062
Cloud-Based Machine Learning for Predictive Analytics: Tool Wear Prediction in
Milling
Dazhong Wu, Connor Jennings, Janis Terpenny, Soundar Kumara
Department of Industrial and Manufacturing Engineering
The Pennsylvania State University
University Park, PA 16802, USA
{dxw279, connor, jpt5311, skumara}@psu.edu
Abstract—The proliferation of real-time monitoring systems
and the advent of Industrial Internet of Things (IIoT) over the
past few years necessitates the development of scalable and
parallel algorithms that help predict mechanical failures and
remaining useful life of a manufacturing system or system
components. Classical model-based prognostics require an in-
depth physical understanding of the system of interest and
oftentimes assume certain stochastic or random processes. To
overcome the limitations of model-based methods, data-driven
methods such as machine learning have been increasingly
applied to prognostics and health management (PHM). While
machine learning algorithms are able to build accurate
predictive models, large volumes of training data are required.
Consequently, machine learning techniques are not
computationally efficient for data-driven PHM. The objective of
this research is to create a novel approach for machinery
prognostics using a cloud-based parallel machine learning
algorithm. Specifically, one of the most popular machine
learning algorithms (i.e., random forest) is applied to predict
tool wear in dry milling operations. In addition, a parallel
random forest algorithm is developed using the MapReduce
framework and then implemented on the Amazon Elastic
Compute Cloud. Experimental results have shown that the
random forest algorithm can generate very accurate
predictions. Moreover, significant speedup can be achieved by
implementing the parallel random forest algorithm.
Keywords-prognostics and health management; machine
learning; cloud computing; tool wear prediction
I.
I
NTRODUCTION
Almost all engineering systems (e.g., aerospace systems,
nuclear power plants, and machine tools) are subject to
mechanical failures resulting from deterioration with usage
and age or abnormal operating conditions [1-3]. Some of the
abnormal operating conditions include wear, corrosion, high
temperature, high pressure, vibration, buckling, and fatigue.
The degradation and failures of engineering systems or system
components will often incur higher costs and lower
productivity due to unexpected machine down time. In order
to increase manufacturing productivity while reducing
maintenance costs, it is crucial to perform a maintenance
strategy that allows manufacturers to schedule production
shutdowns for repairs, inspection, and maintenance.
Conventional maintenance strategies include reactive,
preventive, and predictive maintenance [4-6]. The most basic
approach to maintenance is reactive, also known as run-to-
failure maintenance planning. In the reactive maintenance
strategy, assets are deliberately allowed to operate until
failures actually occur. The assets are maintained on an as-
needed basis. One of the disadvantages of reactive
maintenance is that it is difficult to anticipate maintenance
resources (e.g., manpower, tools and replacement parts) will
be needed for repairs. In preventive maintenance, systems or
components are replaced based on a conservative schedule to
prevent commonly occurring failures. Although preventive
maintenance allows for more consistent and predictable
maintenance schedules, it is expensive to implement
preventive maintenance because of frequent replacement of
components or parts before their end-of-life. To reduce the
high costs of preventive maintenance, predictive maintenance
is an alternative strategy in which maintenance actions are
scheduled based on equipment performance or conditions
instead of time. The objective of predictive maintenance is to
determine the condition of in-service equipment, and
ultimately to predict the time at which a system or a
component will no longer meet desired functional
requirements.
The discipline that predicts health condition and remaining
useful life based on previous and current operating conditions
is often referred to as PHM. Classical prognostic approaches
fall into two categories: model-based and data-driven
prognostics [7-12]. Model-based prognostics refers to
approaches based on mathematical models of system behavior
derived from physical laws or probability distribution. For
example, conventional model-based prognostics include
methods based on Wiener and Gamma processes [13], hidden
markov models [14], Kalman filter [15], and particle filter
[16]. One of the disadvantages of model-based prognostics is
that an in-depth understanding of the underlying physical
processes that lead to system failures is required. Another
disadvantage is that it is assumed that underlying processes
follow certain probability distribution such as gamma or
normal distributions.
In comparison with model-based prognostics, data-driven
prognostics refers to approaches that build a predictive model
using a learning algorithm and large volumes of historical
data. For example, classical data-driven prognostics include
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approaches based on autoregressive model, multivariate
adaptive regression, fuzzy set theory, and artificial neural
networks (ANNs). The unique benefit of data-driven methods
is that an in-depth understanding of system physical behaviors
is not required. In addition, data-driven methods do not
assume any underlying probability distributions. While a few
machine learning algorithms such as ANNs and decision trees
have been applied in the area of tool wear prediction, little
research has been reported on the parallel implementation of
machine learning algorithms on the cloud in the context of
manufacturing [17]. To address the research gap, we
developed a cloud-based parallel random forest algorithm to
predict tool wear using two experimental data sets. The
performance of the random forest algorithm is measured using
accuracy and training time. The advantages of random forests
[18] are: it is one of the most accurate machine learning
algorithms; it runs efficiently on large datasets; it handles a
large number of input variables (i.e., predictors) without
variable selection; feature importance is estimated during
training; and cross validation is not required because random
forests generate an internal unbiased estimate of the
generalization error as the forest building progresses.
Moreover, because machine condition monitoring systems
generate large volumes of measurement data, it is extremely
challenging to design and implement efficient and scalable
data-driven approaches that are capable of processing large
volumes of historical data or high speed streaming data on a
multi-core processor and/or a cluster. In order to benefit from
multi-core processors and high performance computing
clusters, it is important to parallelize the data-driven
algorithms. To address this research gap, a novel PRF
machine learning algorithm is implemented on a public cloud
based on the MapReduce framework. It should be noted that
the objective of this paper is to investigate the performance of
the random forest algorithm and its parallel implementation
using the MapReduce paradigm. Due to this reason, the
comparison of random forests with other machine learning
algorithms such as ANNs is not conducted.
The main contributions of this paper include:
A parallel random forest (PRF) algorithm is
developed based on the MapReduce framework and
implemented on a single machine with multiple cores
in a high performance computing cloud.
The performance of the PRF algorithm is compared
with that of the random forest algorithm implemented
in serial. The speedup and scalability of the PRF are
evaluated using two training data sets.
The remainder of the paper is organized as follows:
Section 2 reviews the related literature on data-driven
prognostics. Section 3 introduces the theoretical background
of the random forest algorithm and a PRF implementation
based on the MapReduce framework. Section 4 presents the
methodology for data-driven prognostics for tool wear
prediction using the MapReduce-based PRF algorithm.
Section 5 presents an experimental setup, an experimental
data set acquired from different types of sensors on a CNC
milling machine, and experimental results. Section 6 provides
conclusions that include a discussion of research contribution
and future work.
II. D
ATA
-D
RIVEN
P
ROGNOSTICS
Schwabacher and Goebel [19] conducted a review of data-
driven methods for prognostics. The most popular data-driven
approaches to prognostics include ANNs and decision trees in
the context of systems health management. ANNs are a family
of computational models based on biological neural networks
which are used to estimate complex relationships between
inputs and outputs. Chungchoo and Saini [20] developed an
online fuzzy neural network (FNN) algorithm that estimates
the average width of flank wear and maximum depth of crater
wear. A modified least-square backpropagation neural
network was built to estimate flank and crater wear based on
cutting force and acoustic emission signals. Chen and Chen
[21] developed an in-process tool wear prediction system
using ANNs for milling operations. A total of 100
experimental data were used for training the ANN model. The
input variables include feed rate, depth of cut, and average
peak cutting forces. The ANN model can predict tool wear
with an error of 0.037mm on average. Ozel and Karpat [22]
developed a predictive model for tool flank wear and surface
roughness in finish dry and turning operations using
feedforward neural networks and regression. Based on
experimental results, predictive neural network models
provided more accurate predictions than regression models.
Bukkapatnam et al. [23-25] developed effective tool wear
monitoring techniques using ANNs based on features
extracted from the principles of nonlinear dynamics. The
disadvantages of ANNs include (1) the training outcome
depends significantly on the choice of initial parameters such
as number of layers and number of neurons in each layer and
(2) training is too computationally expensive to solve large
problems.
Another data-driven method for prognostics is based on
decision trees, which is a non-parametric supervised learning
method used for classification and regression. The goal of
decision tree learning is to create a model that predicts the
value of a target variable by learning decision rules inferred
from data features. A decision tree is a tree structure in which
each internal node denotes a test on an attribute, each branch
represents the outcome of a test, and each leaf node holds a
class label. Jiaa and Dornfeld [26] proposed a decision tree-
based method for the prediction of tool flank wear in a turning
operation using acoustic emission and cutting force signals.
The features characterizing the AE RMS and cutting force
signals were extracted from both time and frequency domains.
The decision tree approach was demonstrated to be able to
make reliable inferences and decisions on tool wear
classification. Elangovan et al. [27] developed a decision tree-
based algorithm for tool wear prediction using vibration
signals. Ten-fold cross-validation was used to evaluate the
accuracy of the predictive model created by the decision tree
algorithm. The maximum classification accuracy was 87.5%.
While the advantage of decision trees is interpretability,
decision trees can be very sensitive to small variations in
training data.
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III. M
ACHINE
L
EARNING
A. Random Forests
To address the research gap, the random forest algorithm
is introduced to predict tool wear. A comprehensive tutorial
on random forests can be found in Friedman et al. [28]. The
random forest algorithm, developed by Leo Breiman [18,29],
is an ensemble learning method that constructs a forest of
decision trees from bootstrap samples of a training data set.
Each decision tree produces a response, given a set of
predictor values. In a decision tree, each internal node
represents a test on an attribute, each branch represents the
outcome of the test, each leaf node represents a class label for
classification or a response for regression. A decision tree in
which the response is continuous is also referred to as a
regression tree. In the context of tool wear prediction, each
individual decision tree in a random forest is a regression tree
because tool wear describes the gradual failure of cutting
tools. The pseudo code of the random forest algorithm for
regression is shown in Table 1.
Table 1. Pseudo Code of the Random Forest Algorithm
Random Forests for Regression [28]
Input: Training data
Output: Prediction at a new data point
1. for b = 1 to ܤ do % ܤ is the number of trees %
1.1 Draw a bootstrap sample ܼ of size ܰ from the training data
1.2 Grow a random-forest tree ܶ
to the bootstrapped data
(1.2.1) Select ݉ variables at random from ݌ variables
(1.2.2) Pick the best split-point among the ݉ variables
(1.2.3) Split the node into two children nodes
2. Output the ensemble of trees ܶ
3. Make a prediction at a new point ݔ by aggregating the
predictions of the ܤ trees
݂
௥௙
ݔͳ
ܤ෍ܶ
ሺݔሻ
௕ୀଵ
Bootstrap aggregating or bagging: Given a training data
set ܦൌሼሺݔ
ǡݕ
ǡݔ
ǡݕ
ǡǥǡݔ
ǡݕ
ሻሽ,
bootstrap aggregating or bagging generates ܤ new
training data sets ܦ
of size ܰ by sampling from the original
training data set ܦ with replacement. ܦ
is referred to as a
bootstrap sample. By sampling with replacement or
bootstrapping, some observations may be repeated in each ܦ
.
Bagging helps reduce variance and avoid overfitting. The
number of regression trees ܤ is a parameter specified by users.
Typically, a few hundred to several thousand trees are used in
the random forest algorithm.
Choosing variables to split on: For each of the bootstrap
samples, grow an un-pruned regression tree with the following
procedure: At each node, randomly sample ݉ variables and
choose the best split among those variables rather than
choosing the best split among all predictors. This process is
sometimes called “feature bagging”. The reason why a
random subset of the predictors or features is selected is
because the correlation of the trees in an ordinary bootstrap
sample can be reduced. For regression, the default ݉ൌ݌Ȁ͵.
Splitting criterion: Suppose that the training data is
partitioned into ܯ regions ܴ
, ܴ
, …, ܴ
. A regression tree
can be modeled as follows:
݂ݔൌ෍ܿ
ܫሺݔܴ߳
௠ୀଵ
(3.1)
where ܫሺǤ is an indicator function; If its argument is true,
then the indicator function returns 1; otherwise 0; and the
response is modeled as a constant ܿ
in each region. The
splitting criterion at each node is to minimize the sum of
squares. Therefore, the best ܿ
is the average of ݕ
in region
ܴ
: ܿ
ෞൌܽݒ݁
ݕ
ȁݔ
ܴ߳
(3.2)
Consider a splitting variable ݆ and split point ݏ, and define
the pair of half-planes
ܴ
݆ǡݏൌ൛ܺܺ
൑ݏ, ܴ
݆ǡݏൌ൛ܺܺ
൒ݏ. (3.3)
Then we seek the splitting variable ݆ and split point ݏ that
solve

௝ǡ௦
ቂ
௖ଵ
σሺݕ
െܿͳ
אோ
௝ǡ௦

σሺݕ
െܿʹ
אோ
௝ǡ௦
. (3.4)
For any choice ݆ and s, the inner minimization is solved by
ܿ
ෝൌܽݒ݁ݕ
หݔ
ܴ߳
݆ǡݏ
ܿ
ෝൌܽݒ݁
ݕ
ݔ
ܴ߳
݆ǡݏ
(3.5)
Having found the best split, we partition the data into two
resulting regions and repeat the splitting process on each of
the two regions. This splitting process is repeated until a
predefined stopping criterion is satisfied.
Stopping criterion: Tree size is a tuning parameter
governing the complexity of a model. The stopping criterion
is that the splitting process proceeds until the number of
records in ܦ
falls below a threshold. The default threshold is
five (݊
௠௜௡
ൌͷ). Alternatively, the maximum depth to which
a decision tree should be constructed can be specified.
After ܤ such trees ܶ
are constructed, a prediction at a
new point ݔ can be made by averaging the predictions from
all the individual ܤ regression trees on ݔ:
݂
௥௙
ݔͳ
ܤ෍ܶ
ሺݔሻ
௕ୀଵ
(3.6)
B. MapReduce-based Parallel Random Forests
Because the tree growth step of the random forest machine
learning algorithm is parallelizable, the MapReduce
framework is used to parallelize the random forest algorithm.
MapReduce is a programming model for processing large data
sets with a parallel algorithm on a single machine with multi-
core CPUs and a cluster [30].
Fig. 1 illustrates a high-level view of the MapReduce
architecture [31,32]. In step 0, input data (i.e., training data
sets) are fed into an algorithm. In step 1, the algorithm is
executed on a single machine with multiple CPU cores or a
cluster. In step 2, a master is created to split the input data into
multiple pieces. Each piece is assigned to a mapper. In step 3,
a Map function parses the input data and generates a list of
intermediate <key, value> pairs. In step 4, the master collects
the intermediate data from the mappers and sorts the
intermediate data by the keys. All the intermediate data with
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2065
the same key are grouped together. After sorting, a Reduce
function is called. In step 5, the Reduce function aggregates
all the intermediate pairs with the same key generated by the
Map function. Finally, in step 6, the reducer returns the final
results.
Figure 1. MapReduce Framework
IV. M
ETHODOLOGY
This section presents the methodology for data-driven
prognostics for tool wear prediction with the MapReduce-
based PRF algorithm. The input of the PRF is the training data
ܦൌݔ
ǡݕ
where ݔ
denote the cutting forces, vibrations,
and acoustic emissions, ݕ
denotes the magnitude of tool
wear. A random forest is constructed using ܤ ൌ ͳͲǡͲͲͲ
regression trees. Given the labeled training dataset ܦൌ
ݔ
ǡݕ
, we drew a bootstrap sample of size ܰ from the
training dataset (Step 1.1). For each regression tree, we select
݉ൌ͵݉ൌ
ݎ݋ݑ݊݀݁݀݀݋ݓ݊ǡ݌ ൌ ͻሻvariables at random
from the 9 variables (Step 1.2.1). The best variable/split-point
is selected among the 3 variables (Step 1.2.2). A regression
tree progressively splits the training dataset into two child
nodes, left node (with samples < z) and right node (with
samples >= z). A splitting variable and split point are selected
by solving Equations 3.4 and 3.5. The process is applied
recursively on the dataset in each child node. The splitting
process stops if the number of records in a node is less than 5.
An individual regression tree is built by starting at the root
node of the tree, performing a sequence of tests about the
predictors, and organizing the tests in a hierarchical binary
tree structure as illustrated in Fig. 2.
Figure 2. Binary Regression Tree Growing Process
After 10,000 trees are constructed, a prediction at a new
point can be made by averaging the predictions from all the
individual binary regression trees on this point. Because the
random forest algorithm can be decomposed into a large
number of independent computations, also known as perfectly
parallel, the MapReduce framework performs optimally.
V. E
XPERIMENT AND
R
ESULTS
A. Experimental Setup
The dataset used in this paper was obtained from Li et al.
[33]. The details of the experiment are presented in this
section. The experimental setup is shown in Fig. 3.
Figure 3. Binary Regression Tree Growing Process
The experiment was conducted on a three-axis high speed
CNC machine (Röders Tech RFM 760). The workpiece
material used in the dry milling experiment was stainless steel.
The detailed description of the operating conditions in the dry
milling operation can be found in Table 2. The spindle speed
of the cutter was 10,400 RPM. The feed rate was 1,555
mm/min. The Y depth of cut (radial) was 0.125 mm. The Z
depth of cut (axial) was 0.2 mm. The sampling rate was 50
KHz/channel.
Table 2. Operating Conditions
Parameter Value
Spindle Speed 10400 RPM
Feed Rate 1555 mm/min
Y Depth of Cut 0.125 mm
Z Depth of Cut 0.2 mm
Sampling Rate 50 KHz/channel
Material Stainless steel
As shown in Table 3, seven signal channels, including
cutting force, vibration, and acoustic emission data, were
monitored. A stationary dynamometer, mounted on the table
of the CNC machine, was used to measure cutting forces in
three, mutually perpendicular axes (x, y, and z dimensions).
Three piezo accelerometers, mounted on the workpiece, were
Map() Map() Map()
3. Intermediate data
2. Call the Map function
Algorithm
Master
Reducer
5. Reduce
6. Result
0. Data input
Input data
1. Run
4. Ca ll the Reduce function
Output data
False
ܸ
௠௔௫
<0.05
ܨ
௠௔௫
<0.5
True
FT ܣܧ
௠௘௔௡
<0.01
FT
ܨ
௠௘ௗ௜௔௡
<0.3
y=26.7
FT y=111.7
y=5.3
y=14.9 y=50
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2066
used to measure vibration in three, mutually perpendicular
axes (x, y, and z dimensions). An acoustic emission (AE)
sensor, mounted on the workpiece, was used to monitor a high
frequency oscillation that occurs spontaneously within metals
due to crack formation or plastic deformation. Acoustic
emission is caused by the release of strain energy as the micro
structure of the material is rearranged. Three datasets were
generated. Each dataset contains 315 individual data
acquisition files in the csv format. The size of each dataset is
about 2.89 GB.
Table 3. Signal Channel and Data Description
Signal Channel Data Description
Channel 1 Force (N) in X dimension
Channel 2 Force (N) in Y dimension
Channel 3 Force (N) in Z dimension
Channel 4 Vibration (g) in X dimension
Channel 5 Vibration (g) in Y dimension
Channel 6 Vibration (g) in Z dimension
Channel 7 Acoustic Emission (V)
B. Tool Wear Prediction with Random Forests
Feature extraction is an essential preprocessing step in
which raw data collected from various signal channels is
converted into a set of statistical features in a format supported
by machine learning algorithms. The statistical features are
then given as an input to a machine learning algorithm. In the
first experiment, the raw data was collected from (1) cutting
force, (2) vibration, and (3) acoustic emission signal channels.
A set of statistical features (28 features) extracted from these
signals include Maximum, Median, Mean, and Standard
Deviation as listed in Table 4.
Table 4. Extracted Features
Cutting Force Vibration Acoustic Emission
Max Max Max
Median Median Median
Mean Mean Mean
Standard Deviation Standard Deviation Standard Deviation
Figure 4. Tool Wear Prediction
A predictive model was developed using the random forest
algorithm. Two thirds (2/3) of the input data was used for
model development (training). The remainder (1/3) of the
input data was used for model validation (testing). Fig. 4
shows the predicted against observed tool wear values using
the experimental data set.
The performance of the random forest algorithm is
evaluated using accuracy and training time. The accuracy of
the random forest algorithm is measured using the ܴ
statistic,
also referred to as the coefficient of determination, and mean
squared error ( ܯܵܧ ). In statistics, the coefficient of
determination is defined as ܴ
ൌͳെ
ௌௌா
ௌௌ்
where ܵܵܧ is the
sum of the squares of residuals, ܵܵܶ is the total sum of
squares. The coefficient of determination is a measure that
indicates the percentage of the response variable variation that
is explained by a regression model. A higher R-squared
indicates that more variability is explained by the regression
model. For example, an ܴ
of 100% indicates that the
regression model explains all the variability of the response
data around its mean. In general, the higher the R-squared, the
better the regression model fits the data. The MSE of an
estimator measures the average of the squares of the errors.
The ܯܵܧ is defined as ܯܵܧ ൌ
σሺܻ
െܻ
௜ୀଵ
where ܻ
is a
predicted value, ܻ
is an observed value, and ݊ is the sample
size. The random forest algorithm uses between 50% and 90%
of the input data for model development (training) and uses
the remainder for model validation (testing). Table 5 lists the
MSE, ܴ
, and training time.
Table 5. Accuracy and Training Time
Random forest (10,000 Trees)
Training size (%) MSE R
2
Training time (Second)
50 14.242 0.986 20.876
60 11.466 0.989 26.562
70 10.469 0.990 33.230
80 8.195 0.992 38.995
90 8.295 0.992 45.224
C. Performance Evaluation for Parallel Random Forests
This section presents the performance evaluation for the
parallel implementation of the random forest algorithm.
Specifically, the speedup and efficiency of the MapReduce-
based PRF algorithm are discussed. The PRF algorithm was
implemented on one of the most popular public cloud
computing platforms, Amazon Elastic Compute Cloud
(Amazon EC2). A variety of instance types with varying
combinations of CPU, memory, and storage are provided for
evaluating the speedup, efficiency, and scalability of the PRF
algorithm. The cloud computing service on the Amazon EC2
can be accessed by an online user interface, called the AWS
Management Console. A user can configure, launch, stop,
restart, and terminate an instance (i.e., a virtual server in
Amazon EC2) to run application programs in the cloud
computing environment via a web browser. Amazon EC2
provides a variety of instance types which comprise varying
combination of virtual CPU (vCPU), memory, and storage. A
C3 instance on the Amazon EC2 is one of the compute-
optimized instances, featuring the highest performing
processors and the lowest price/compute performance. Table
6 lists the hardware specifications of the C3 instance. The C3
large instance equips with 32 virtual cores, 60 GB memory,
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and two disks of 320 GB storage and runs on the Linux
operating system.
Table 6. Amazon EC2 Infrastructures
Instance Type C3.8×large R3.8×large
Processor Intel Xeon E5-2680 v2 Intel Xeon E5-2670 v2
Number of CPU 32 32
Memory (GB) 60 244
Storage (GB) 640 (2 × 320) 640 (2 × 320)
Fig. 5 shows the time spent training the predictive model
using a C3 instance with varying number of cores and
different sizes of training data sets. The algorithm was
executed twenty times with the percentage of the training data
sets and the number of cores ranging from 50% to 90% and
from 1 to 32 cores, respectively. Execution time is calculated
as an average over the twenty runs. Note that the time to
construct the features from the initial signals is not included.
Figure 5 Runtime vs number of cores using C3 instances
As shown in Fig. 5 and Fig. 6, the PRF algorithm scales
relatively well with the number of cores for different
percentages of the training data sets. Take the run time with
90% of the training data set for example, near linear speedup
is observed when the PRF algorithm runs on the number of
cores ranging from 1 to 16 cores based on the speedup curve.
However, the speedup falls rapidly for 32 cores. It should be
noted that relative speedup is the ratio of the solution time for
a problem with a parallel algorithm executed on a single
processor to the solution time with the same algorithm when
executed on multiple processors. Another metric to measure
the performance of the PRF algorithm is efficiency, defined as
the ratio of relative speedup to the number of processors. As
shown in Fig. 5, the execution time using 1, 2, 4, 8, 16, and 32
cores are 44s, 23s, 12s, 7s, 4s, and 3s. For 1 to 16 cores,
relative speedup is almost linear. Linear speedup in turn
corresponds to efficiency of 1. When the number of cores
continues to increase beyond 16 cores, the PRF cannot
achieve linear speedup. This is because speedup is always
limited by the serial part of the program according to
Amdahl’s law [34] of the theoretical speedup of the execution
of a program.
Figure 6 Speedup using C3 instances (90% of the training data set)
Because high performance computing applications are
often limited by either computing speed or memory, it is
worthwhile to evaluate whether this application is compute
bound or memory bound. The PRF algorithm was executed on
a R3 instance which is optimized for memory-intensive
applications. Similar to the C3 instance, the R3 large instance
equips with 32 virtual cores, two disks of 320 GB storage, and
244 GB memory instead of 60 GB memory. Table 6 lists the
hardware specifications of the R3 large instance. As shown in
Fig. 7, training time using the R3 instance with 244 GB
memory is almost the same as that of the C3 instance with 60
GB memory. The results demonstrate that the PRF algorithm
is compute bound instead of memory bound.
Figure 7 Runtime vs number of cores using R3 instances
VI. C
ONCLUSIONS
In this paper, the prediction of tool wear in milling
operations was performed with the random forest and PRF
algorithms. The PRF algorithm was developed using the
MapReduce framework and then implemented on the Amazon
Elastic Compute Cloud. The effectiveness and efficiency of
the algorithms were demonstrated with two different data sets
collected from two milling experiments under various
operating conditions. Two sets of statistical features were
extracted from cutting forces, vibrations, acoustic emissions
and the electrical current of the spindle motor, vibrations at
the table and spindle, acoustic emissions at the table and
spindle, respectively. Two thirds of the input data were used
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2068
for training. The remainder of the input data was used for
testing. The performance of the random forest algorithm was
evaluated using mean squared error, R-square, and training
time. The experimental results have shown that random
forests can generate very accurate predictions for the first data
set. Due to the limited amount of training data, the random
forest algorithm generated less accurate predictions for the
second experiment. Moreover, the PRF algorithm was
developed to scale up the random forest algorithm. The
experimental results have shown that significant speedup can
be achieved when building a large number of decision trees.
Further, the PRF algorithm has been demonstrated to be
compute bound by comparing the training time using two
Amazon instances.
In the future, it will be worthwhile to predict tool wear
with other machine learning algorithms such as support vector
machines as well as compare the performance of these
algorithms with that of random forests using accuracy and
training time. In addition, our future work will focus on
collecting large volumes of streaming data from a network of
CNC machines and build predictive models for tool wear
estimation with the PRF algorithm and a cluster on the cloud.
A
CKNOWLEDGMENT
The research reported in this paper is partially supported
by NSF under grant number IIP-1238335. Any opinions,
findings, and conclusions or recommendations expressed in
this paper are those of the authors and do not
necessarily reflect the views of the National Science
Foundation.
R
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