US 11,761,930 B2 · Prediction method of part surface roughness and tool wear based on multi-task learning

Abstract

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

A prediction method of part surface roughness and tool wear condition based on multi-task learning, comprising the following steps:

collecting vibration signal during machining by fixing tri-axial acceleration sensor at a position of a spindle, wherein the tri-axial acceleration sensor closest to tool holder to collect a tri-vibration signal of the spindle during a machining process of a CNC machine tool, and the vibration signal of a machine tool load is intercepted from a tri-axial vibration signal;
using measurement equipment to measure a part surface roughness and tool wear;
when measuring the part surface roughness, dividing a surface of the part into equal intervals according to a sampling length, and measuring a roughness value in each interval, wherein whether surface quality of the part is qualified or not is divided according to the roughness;
tool wear is measured every fixed cutting distance, and the tool is categorized into three conditions being normal, worn and damaged according to whether there is obvious wear and damage of the tool;
finally, dividing vibration data collected in the machining process by a length of vibration data corresponding to each sampling length, wherein a surface roughness label and a tool wear condition label are respectively corresponding to the vibration data after division;
according to equation (1), adding M degree of Gaussian white noise to the collected vibration signal;
V
i
s
=
(
k
2
⁢
i
+
k
)
⁢
σ
M
⁢
V
st
s
(
1
)
where:
Vsts is an original vibration signal, s is a sample index, k is a different intensity parameter, i=1, 2, . . . , M is used to control a different noise level, σ is a sample standard variance;
after adding noise, each sample is expanded to M+1 group sample {Vsts, V1s, . . . , VMs}, the sample expansion is as follows:
Sa=Vsts+Vis (2)
normalizing the variance, root mean square, kurtosis, impulse factor and skewness features extracted from equation (3)˜(7);
Va
=
1
N
-
1
⁢
∑
t
=
1
N
⁢
(
V
i
s
⁡
(
t
)
-
V
¯
)
2
(
3
)
RMS
=
(
1
N
⁢
∑
t
=
1
N
⁢
V
i
s
⁡
(
t
)
2
)
1
/
2
(
4
)
KUR
=
[
1
N
⁢
∑
t
=
1
N
⁢
(
V
i
s
⁡
(
t
)
-
V
¯
)
4
]
/
[
1
N
⁢
∑
t
=
1
N
⁢
(
V
i
s
⁡
(
t
)
-
V
¯
)
2
]
2
(
5
)
IF
=
[
V
i
s
⁡
(
t
)
max
-
V
i
s
⁡
(
t
)
min
]
/
[
1
N
⁢
∑
t
=
1
N
⁢

V
i
s
⁡
(
t
)

]
(
6
)
Ske
=
[
1
N
⁢
∑
t
=
1
N
⁢
(
V
i
s
⁡
(
t
)
-
V
¯
)
3
]
/
[
1
N
⁢
∑
t
=
1
N
⁢
(
V
i
s
⁡
(
t
)
-
V
¯
)
2
]
3
/
2
(
7
)
where:
Vis(t) is the vibration data of a sample, t is an index of vibration data, N is a number of data contained in each sample, and V is an average value of each sample;
constructing a deep neural network A based on deep belief networks, wherein the deep neural network A is composed of three layers of restricted Boltzmann machine and a BP network;
among them, the restricted Boltzmann machine is an energy-based model, and its joint probability distribution is specified by an energy function, and a definition of the energy function is shown in equation (8),
E
⁡
(
v
,
h
⁢
;
⁢
⁢
θ
)
=
-
∑
α
=
1
V
⁢
∑
β
=
1
H
⁢
w
α
⁢
β
⁢
v
α
⁢
h
β
-
(
∑
α
=
1
V
⁢
b
α
⁢
v
α
+
∑
β
=
1
H
⁢
a
β
⁢
h
β
)
(
8
)
where vα and hβ are the binary states of visible and hidden units, θ={w,b,a} are the parameters of the model;
wαβ are the weight between visible units α and hidden units β, bα and aβ are offsets of visible units α and hidden units β, respectively, V and H is the number of visible and hidden units;
the energy of hidden neurons is as follows:
E
β
=
∑
α
=
1
V
⁢
w
α
⁢
β
⁢
v
α
+
b
α
(
9
)
similarly, the energy of visible neurons is as follows:
E
α
=
∑
β
=
1
H
⁢
w
α
⁢
β
⁢
h
β
+
a
β
(
10
)
marking the input layer to the first layer restricted Boltzmann machine as B1, and from the second layer restricted Boltzmann machine to the output layer as B2, copying B2 and marking it as B3, wherein B3 is connected with the first layer restricted Boltzmann machine of deep neural network A, at this time, arranging the network structure B2 and B3 side by side and connecting with B1;
according to the task requirements, setting the number of output layer nodes of B2 and B3 to complete the improvement of deep belief network, wherein the improved deep belief network B is composed of network structure B1, B2 and B3, which can realize the function of multi-task learning;
using a greedy algorithm to pre-train the restricted Boltzmann machine of each layer in the deep neural network A, and assigning the weight of the trained deep neural network A to the improved deep belief networks B, wherein based on the back-propagation algorithm, the supervised fine-tuning of the improved deep belief network B is carried out, and the loss function as equation (11) is adjusted to the optimal value to obtain multi-task predicting model;
L
⁡
(
θ
)
=
-
∑
j
=
1
W
⁢
λ
j
m
[
∑
k
=
1
m
⁢
y
^
k
⁢
⁢
log
⁢
⁢
y
k
]
(
11
)
where, W is the total number of tasks in the multi-task model, λj is the weight of each task, m is the total number of fine-tuning samples, ŷk is the actual label, yk is the prediction label;
in the actual machining process, pre-processing the vibration data collected and inputting the pre-processed vibration data into the multi-task prediction model after training;
the multi-task prediction model outputs the part surface roughness and tool wear condition;
as task 1, outputting 1 when the part surface roughness is qualified, and outputting 0 when the part surface roughness is not qualified;
and as task 2, outputting 0 when the tool is not worn, outputting 1 when it is worn, and outputting 2 when it is damaged.

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Prediction method of part surface roughness and tool wear based on multi-task learning

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Prediction method of part surface roughness and tool wear based on multi-task learning

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Abstract

Claims1