SIS identification method of reversible recovery fault-oriented workshop key manufacturing resources
A SIS identification method of reversible recovery fault-oriented workshop key manufacturing resources is that based on SIS model in the infectious disease research theory, the workpieces and manufacturing resources required in a production order are linked together through database technology to establish a discrete production workshop manufacturing network. The SIS model is configured to solve a change of the number of bottlenecks of other manufacturing resources caused by the initial fault manufacturing resources over time. The weighted result of a peak number of bottleneck resources and its time to reach the peak is marked to determine the importance of the initial fault manufacturing resources. Through the sorting of importance, the key manufacturing resource nodes in the discrete workshop are finally selected. The invention sorts out other key manufacturing resources that need to pay attention in production management in the manufacturing workshop to plan in advance and to improve the efficiency.
1. A SIS identification method of reversible recovery fault-oriented workshop key manufacturing resources for establishing a production model, comprising the steps, executed by a computerized device, of:
step 1: based on Internet RFID technology and relational SQL database, establishing an automatic link of workpieces produced in a production cycle with their production plan, production process, and manufacturing resources; according to SIS model in the infectious disease research theory, assuming a total number of the manufacturing resources as a constant as U throughout a production cycle of a manufacturing workshop, and configuring an initial “fault has been occurred” manufacturing resource and a “fault has not been occurred” manufacturing resource in discrete workshop manufacturing resources as X(t 0 ) and Y(t 0 ) respectively;
wherein a relationship between X(t 0 ) and Y(t 0 ) is configured as:
X ( t 0 )={ x 1 ( t 0 ), x 2 ( t 0 ), x 3 ( t 0 ), . . . , x j ( t 0 )}
Y ( t 0 )={ y 1 ( t 0 ), y 2 ( t 0 ), y 3 ( t 0 ), . . . , y k ( t 0 )}
wherein:
X j (t 0 ) is the j th initial “fault has been occurred” manufacturing resource at the start time;
Y k (t 0 ) is the k th initial “fault has not been occurred” manufacturing resource at the start time;
step 2: based on Internet RFID technology and relational SQL database, establishing a second automatic link among the workpieces, the production plan, the production process, and the corresponding manufacturing resources within the production cycle in the manufacturing workshop; converting the second automatic link into a plurality of connecting network edges in a workshop manufacturing system network, and finally mapping weighted edges of a processing time and all the manufacturing resources, including machine tools, cutting tools, fixtures, measuring tools and personnel in the production process, to a plurality of network nodes in the workshop manufacturing system network;
step 3: according to a grouping result in the step 1, configuring a probability of eventual failure of the “fault has not been occurred” manufacturing resource caused by the “fault has been occurred” manufacturing resource as β, configuring an effective number in unit of time for the “fault has not been occurred” manufacturing resource to the “fault has been occurred” manufacturing resource as γ, and configuring a ratio of a number of the “fault has been occurred” manufacturing resource that has failed again to a total number of the “fault has been occurred” manufacturing resource as λ;
step 4: configuring a fault propagation rate between the manufacturing resources with a connection relationship thereof as a ratio of the weight of the edge connecting two of the manufacturing resources and the maximum weight in the entire network, wherein β ij is determined as:
β
ij
=
(
w
ij
w
max
)
δ
wherein δ is the contact probability of two of the manufacturing resources, wherein the contact probability is set as 1 when there is a connecting edge between the two manufacturing resources, wherein the contact probability is set as 0 when there is no connecting edge between the two manufacturing resources;
step 5: through the SIS model, determining a change of a number of bottleneck occurring over a time for the “fault has not been occurred” manufacturing resource due to the initial the “fault has been occurred” manufacturing resource;
I
(
t
)
=
N
(
λβ
-
γ
)
λ
β
/
[
(
N
(
λ
β
-
γ
)
I
0
λ
β
-
1
)
e
-
(
λβ
-
γ
)
t
+
1
]
wherein I(t) is the number of bottleneck occurring in the “fault has been occurred” manufacturing resources over the time;
Step 6: marking an importance of the initial “fault has been occurred” manufacturing resource by weighting a peak value of the number of bottleneck and a time length to reach the peak value;
ZYD (i) =k 1 T (i) +k 2 P (i)
wherein ZYD (i) is the importance of the i th group of initial “fault has been occurred” manufacturing resource;
T(i) is the peak time when the number of bottlenecks in the i th group reaches the peak value;
P (i) is the peak value of the number of bottleneck in the i th group;
k 1 , k 2 are the weights of peak time and peak value;
step 7: changing groupings of the initial “fault has been occurred” manufacturing resource and the “fault has not been occurred” manufacturing resource in the workshop manufacturing resources and re-determine the importance according to steps 1 to 6, and repeat it until the importance of all possible groupings is obtained; and
step 8: obtaining key manufacturing resource nodes in the discrete workshop manufacturing system according to an order of all the importance, so as to establish the production model based on the key manufacturing resource nodes.