CN115471048A - Production line equipment comprehensive efficiency monitoring and collaborative optimization method based on digital twin - Google Patents
Production line equipment comprehensive efficiency monitoring and collaborative optimization method based on digital twin Download PDFInfo
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Abstract
The invention discloses a monitoring and collaborative optimization method of comprehensive efficiency of production line equipment based on digital twin, which comprises the following steps: collecting decision characteristics for calculating the comprehensive efficiency of the equipment; calculating and analyzing by adopting a machine learning algorithm according to the decision characteristics, and dividing the shutdown type; calculating the real-time equipment comprehensive efficiency of the equipment by adopting a standard calculation formula of the equipment comprehensive efficiency and combining the divided shutdown types, and dividing the equipment comprehensive efficiency into a plurality of grades; and performing bill insertion distribution of the operation tasks according to the grade of the comprehensive efficiency of the equipment, and increasing the efficiency of the equipment with low comprehensive efficiency. The monitoring and collaborative optimization method for the comprehensive efficiency of the production line equipment based on the digital twin can solve the problems of data offline statistics, aging lag and the like in the prior art, realize the circular iteration and twin optimization of the comprehensive efficiency and collaborative control of the production line equipment based on the digital twin, and improve the comprehensive efficiency of the production line equipment.
Description
Technical Field
The invention belongs to the technical field of digital twinning, production operation and management and machine learning, and particularly relates to a method for monitoring and cooperatively optimizing the comprehensive efficiency of production line equipment based on digital twinning.
Background
Currently, the global ic industry is entering into a major regulatory revolution, and the development and manufacturing of the ic industry are continuously added in the united states, japan, korea, and some european countries. Accelerating the development of the technology of the new generation of integrated circuit and the construction of the manufacturing capability, developing high and new technologies, and seizing the market high point of the new generation of information technology become one of the important measures for realizing the transformation of the economic structure. The Digital Twin technology (DT) provides a new idea for the collaborative optimization of the workshop.
A Digital Twin Shop (DTS) is a specific application of DT in a Shop, mainly embodied in cooperative iterative optimization of production activity planning and manufacturing process control, and aims to realize the optimal overall process of multiple material flows such as manufacturing, distribution and the like through bidirectional real mapping and real-time interaction of multi-stage overall elements of a physical Shop and a virtual Shop. Taking a sealed production line as an example, the manufacturing process comprises more than ten working procedures, the distribution process comprises more than thirty materials and tools, and the interaction between a physical workshop and a workshop service space is used for meeting the initial resource allocation scheme of production task requirements and distribution constraints; the interaction between the service space and the virtual workshop can realize the operation optimization of the production plan under the distribution disturbance; the interaction of the physical plant with the virtual plant may enable iterative optimization of manufacturing process control. The three processes are iterated repeatedly, synchronous mapping, prediction and regulation of the manufacturing and distribution processes of the physical workshop can be achieved, and real-time optimization of workshop production management and control is achieved.
In an SMT (Surface Mount Technology) production line cluster workshop, a production mode using products as a guide pays more attention to productivity and equipment efficiency, so that the gradual increase of the production energy per unit time is realized by continuously reducing the downtime and reducing the ineffective waste, thereby reducing the cost, increasing the profit, and increasing the speaking right and the competitiveness of enterprises under increasingly competitive international and domestic situations. Improving the Equipment Overall efficiency (OEE) is a powerful means for continuous improvement.
Disclosure of Invention
In view of the above, in order to overcome the defects of the prior art, the present invention aims to provide a monitoring and collaborative optimization method for the comprehensive efficiency of production line equipment based on digital twin.
In order to achieve the purpose, the invention adopts the following technical scheme:
a monitoring and collaborative optimization method for comprehensive efficiency of production line equipment based on digital twin comprises the following steps:
collecting decision characteristics for calculating the comprehensive efficiency of the equipment;
calculating and analyzing by adopting a machine learning algorithm according to the decision characteristics, and dividing the shutdown type;
calculating the real-time equipment comprehensive efficiency of the equipment by adopting a standard calculation formula of the equipment comprehensive efficiency and combining the divided shutdown types, and dividing the equipment comprehensive efficiency into a plurality of grades;
and performing bill insertion distribution of the operation tasks according to the grade of the comprehensive efficiency of the equipment, and increasing the efficiency of the equipment with low comprehensive efficiency.
According to some preferred aspects of the invention, the decision feature comprises a down time partition.
According to some preferred implementation aspects of the invention, the decision characteristic collection manner comprises one or more of embedded collection, PLC-based control information, and monitoring information collection.
According to some preferred aspects of the invention, the decision factors of the decision characteristics include time to enter the apparatus, time to leave the apparatus, number of workpieces carried, duty cycle time.
According to some preferred embodiments of the present invention, taking the automated optical inspection apparatus in the surface mounting technology production line of the integrated circuit as an example, the decision factors of the decision characteristics include the time each fixture enters the apparatus, the time each fixture leaves the apparatus, the number of circuit boards carried on each fixture, the work cycle time, and the number of pins on each fixture detected by the automated optical inspection apparatus.
According to some preferred embodiments of the invention, the shutdown types include normal operation, planned shutdown, unplanned shutdown, and change-line shutdown.
According to some preferred embodiments of the present invention, the machine learning algorithm is preferably a neural network algorithm, and other algorithms such as a support vector machine may be used as the comparison algorithm. Repeated operation is needed for many times, and the usability and the efficiency of the algorithm in classification precision (more than 99 percent) and operation time (ms level) are verified.
According to some preferred embodiments of the invention, the standard calculation formula for the overall efficiency of the plant is as follows:
the standard calculation formula of the comprehensive efficiency of the equipment is as follows:
OEE＝A×P×Q
in the formula, OEE is the comprehensive efficiency of the equipment, A is the availability utilization rate, P is the performance utilization rate, and Q is the yield.
According to some preferred embodiments of the present invention, the availability utilization rate is calculated by the following formula:
A＝(Lt-UPt)/Lt
in the formula, UPt is an unplanned downtime (unplanned shutdowns time) of the equipment, and Lt is a start-up time (loading time) of the equipment.
According to some preferred embodiments of the present invention, the start-up time of the equipment is calculated by the formula Lt = Wt-PSt, wt is the working time of the equipment (working time), and PSt is the planned downtime of the equipment (planning times).
According to some preferred embodiments of the present invention, the performance utilization ratio is calculated by the following formula:
P＝Ct×Qp/(Lt-UPt)
where Ct is the duty cycle time (cycle time) of the apparatus and Qp is the yield (yield product).
According to some preferred embodiments of the present invention, the yield is calculated by the following formula:
Q＝(Qp-NG rejected)/Qp
where Qp is the yield and NG rejected is the number of NGs that have not been used in a single test.
According to some preferred embodiments of the present invention, the grades are divided into three grades according to the comprehensive efficiency of the equipment, namely a first grade of the comprehensive efficiency of the equipment from 50 to 100, a second grade of the comprehensive efficiency of the equipment from 40 to 50 and a third grade of the comprehensive efficiency of the equipment from 0 to 40.
According to some preferred implementation aspects of the present invention, in the distribution of the billing, if the device comprehensive efficiency of a device is in the third gear, the device is preferentially billed, and then the device with the device comprehensive efficiency in the second gear is billed.
According to some preferred aspects of the invention, the billing rate of the device in third gear is greater than the billing rate of the device in second gear. If the order insertion rate of the equipment in the third gear is greater than or equal to 2; and the order insertion rate of the equipment in the second gear is greater than or equal to 5, namely the equipment continuously produces 5 products and can insert 1 product. And calculating the bill insertion rate according to the current real-time efficiency and the target efficiency to be achieved of the equipment. Specifically, the insertion rate = { OEE boundary upper limit }: {60% -OEE boundary upper limit },60% is the desired target equipment integrated efficiency. If the result of the OEE statistic of a third gear is 35%, and the corresponding { OEE boundary takes the upper limit } =40%, the bill insertion rate is 2; if the second OEE statistic is 41%, and the corresponding { OEE boundary upper limit } =50%, the order insertion rate is 5.
Compared with the prior art, the invention has the beneficial effects that: the monitoring and collaborative optimization method for the comprehensive efficiency of the production line equipment based on the digital twin can solve the problems of data offline statistics, aging lag and the like in the prior art, realize the circular iteration and twin optimization of the comprehensive efficiency and collaborative control of the production line equipment based on the digital twin, and improve the comprehensive efficiency of the production line equipment.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a logic diagram of a production line equipment comprehensive efficiency monitoring and collaborative optimization method based on digital twin in a preferred embodiment of the present invention;
fig. 2 is a schematic diagram of the cooperative optimization control operation of the robot arm in the preferred embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not a whole embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the embodiment discloses a method for monitoring and collaborative optimization of comprehensive efficiency of production line equipment based on digital twin, which mainly aims to realize loop iteration and twin optimization of comprehensive efficiency and collaborative control of production line equipment based on digital twin, and specifically comprises the following steps,
step 1, collecting decision characteristics for calculating comprehensive efficiency of equipment
Aiming at the SMT production line cluster, various acquisition modules are additionally arranged through an equipment physical entity model, and decision characteristics for OEE calculation are acquired.
The acquisition module can be divided into embedded acquisition and PLC-based control information and monitoring information acquisition. In the cluster OEE calculation of the SMT production line, the main decision characteristics are shutdown time division and are divided into normal operation, planned shutdown, unplanned shutdown, line changing shutdown and the like, so that data acquisition mainly acquires the decision characteristics for the shutdown time division.
In the SMT production line cluster, the purchase value of the AOI (Automated Optical Inspection) Optical Inspection equipment is the highest, so that the OEE twin monitoring and collaborative optimization is performed by taking the AOI Optical Inspection equipment as an example, and the utilization rate of the equipment with the highest value is optimal, so that the improvement of the productivity and the thinning of the profit are realized.
The decision-making features are the shutdown time division, which is divided into normal operation, planned shutdown, unplanned shutdown, line-changing shutdown and the like. Aiming at the decision characteristics of the AOI optical detection equipment, the decision factors of the AOI optical detection equipment are 5-dimensional factors in total, namely the time of each fixture entering the equipment, the time of each fixture leaving the equipment, the number of FPC boards borne by each fixture, the working cycle time and the number of pins detected by the AOI on each fixture, the five-dimensional factors can be read from PLC control signals of the AOI equipment, RS 485-to-wireless transmission equipment is designed, and acquired signals are stored in a database. All collected information is stored in a database for data analysis and calculation.
Because part of the imported AOI optical detection equipment does not open a PLC port, an embedded acquisition mode can be adopted, such as the equipment entering time or the equipment leaving time, and an RFID embedded acquisition mode can be adopted; as another example of the cycle time, an embedded acquisition mode of the working time of the main shaft can be adopted.
Step 2, dividing the shutdown types
And a machine learning algorithm is adopted, and the shutdown types are accurately classified according to the obtained decision characteristics.
Due to the fact that the SMT shutdown time classification samples have the characteristics of large samples and high frequency (7 ten thousand samples per day, and the triggering frequency is in the minute level or the second level), other algorithms such as a neural network algorithm and a support vector machine can be used as a comparison algorithm. After repeated operation for many times, the usability and the efficiency of the algorithm in classification precision (more than 99 percent) and operation time (ms level) are verified.
The algorithm inputs a feature set which is built by 5-dimensional feature factors such as time of each jig entering equipment, time of leaving the equipment, the number of FPC boards loaded on each jig, working cycle time, the number of pins detected by AOI on each jig and the like, and the classifier adopts a neural network algorithm to divide the shutdown types into 4 types of 'normal operation, planned shutdown, unplanned shutdown and line change shutdown'.
Step 3, calculating the real-time equipment comprehensive efficiency of the equipment and dividing the efficiency grade
The method comprises the steps of calculating real-time OEE by adopting an OEE standard calculation formula and combining shutdown classification obtained based on a machine learning algorithm, dividing the OEE into three grades, namely a red, yellow and green grade, and respectively representing serious idleness (third grade), good efficiency (second grade) and good efficiency (first grade), wherein the corresponding OEE respectively comprises the following steps: red: [0, 40], yellow: (40, 50, 100) the grading standard is based on the statistical efficiency of the existing OEE of the SMT enterprises in China, the distribution interval is mainly [30% -60% ], so 4 nodes are trisected, the collaborative optimization target OEE is 60%. The twin entity is presented in three states at the control layer, and three dynamic behaviors of the equipment are represented for collaborative optimization decision.
OEE standard calculation formula is as follows:
OEE＝A×P×Q
in the formula, OEE is the comprehensive efficiency of the equipment, A is the availability utilization rate, P is the performance utilization rate, and Q is the yield.
Wherein, the availability utilization rate is calculated by the following formula:
A＝(Lt-UPt)/Lt
in the formula, UPt is an unplanned downtime (unplanned shutdowns time) of the equipment, and Lt is a start-up time (loading time) of the equipment.
The starting time of the equipment is calculated by a formula Lt = Wt-PSt, wt is the working time (working time) of the equipment, and PSt is the planned downtime (planning times) of the equipment.
The performance utilization rate is calculated by the following formula:
P＝Ct×Qp/(Lt-UPt)
where Ct is the duty cycle time (cycle time) of the apparatus and Qp is the yield (yield produced).
The above-mentioned parameters relating to the working time or the downtime of the plant are obtained and divided from the above-mentioned steps 1 and 2.
The yield is calculated by the following formula:
Q＝(Qp-NG rejected)/Qp
where Qp is the yield and NG rejected is the number of NGs that have not been used in a single test.
Taking AOI data of a certain production line cluster as an example, the calculation result and the corresponding state of OEE are shown in table 1 below.
TABLE 1 Cluster AOI station OEE State real-time monitoring on certain day of SMT production line
| Production line cluster AOI station | OEE | Twinning space coloration | Twin entity state |
| Production line 1AOI station | 31.4％ | Red wine | Severe idle |
| Production line 2AOI station | 52.9％ | Green | Has good efficiency |
| Production line 3AOI station | 37.7％ | Red wine | Severe idle |
| 4AOI stations of production line | 58.4％ | Green | Has good efficiency |
| Production line 5AOI station | 42.8％ | Yellow colour | The efficiency is better |
| 6AOI stations for production line | 61.3％ | Green | Has good efficiency |
| Production line 7AOI station | 51.2％ | Green | Has good efficiency |
| 8AOI stations of production line | 65.0％ | Green | Has good efficiency |
As can be seen from the results in table 1, the production line 1 and the production line 3 are in a severe idle state, the production line 5 is in a state of high efficiency, and the remaining production lines are in a state of high efficiency.
Step 4, insert the single distribution, raise the comprehensive efficiency of the apparatus
And the operation task list insertion distribution is carried out according to the real-time state of the equipment, so that the efficiency of the equipment with low comprehensive efficiency is improved. And when the bill is distributed, the bill is inserted preferentially for the equipment with the comprehensive efficiency in the third gear, and then the bill is inserted for the equipment with the comprehensive efficiency in the second gear.
The enterprise in this embodiment has 8 SMT production lines in common, and forms a standardized patch production line cluster, the process is similar, and the main difference is the difference of various configuration parameters, which is similar to a parallel machine structure. On the premise that the overall structure of an SMT production line cluster is unchanged, a coordinated operation distribution mechanical arm is added, and the coordinated operation distribution mechanical arm has the functions that under the condition of original production scheduling, red equipment and yellow equipment are given additional same process orders, the efficiency of low OEE equipment is increased in the form of list insertion, the number of red equipment lists is large, the number of yellow equipment lists is small, red equipment is preferentially distributed, and yellow equipment is distributed in a lagging mode. And issuing the decision result to an actuator to act on the physical entity of the equipment. Finally, the twinning concept of reflecting deficiency by real things and controlling excess by deficiency things.
As shown in fig. 2, the AOI station in this embodiment belongs to the sixth station, the previous process is a patch station and a reflow soldering station (SPI is a station node, an OEE process is calculated, and only the subsequent stations of the SPI station are counted), and the main reason why the AOI station OEE is low is material delivery delay of equipment, that is, the previous process cannot deliver in time, which causes redundant waiting for 0-3min of the equipment, and the waiting does not cause a shutdown, but seriously reduces the performance utilization (P) of the equipment, and the difference between the beat of the previous process and the beat of the AOI is the main reason for causing the waiting.
And (3) performing operation task order insertion distribution according to three states of the twin space, namely giving orders of the same process to the red and yellow equipment under the original production scheduling condition, and increasing the efficiency of the low OEE equipment in an order insertion mode. If the results in table 1 correspond, a mobile mechanical arm (or other mobile distribution equipment) is added to the AOI stations of the production line 1, the production line 3 and the production line 5, and during the waiting period of production, the same-process product is inserted, and the source of the same-process product includes a single piece, an NG piece return line, a flexible circuit substrate pre-produced by the production line No. 9 with no AOI station added, and the like.
The single workpiece is inserted preferentially, the connecting machine between the reflow soldering station and the AOI station is clamped, and even if the fifth station flows out, the workpieces are not transferred downwards. And secondly, moving the mechanical arm to feed materials, inserting the sheets into an AOI station in the same process, and releasing the connecting machine after the sheets are processed by the AOI station.
According to the method mode, the efficiency of the low OEE equipment can be effectively improved. And because the comprehensive efficiency of the production line equipment is updated in real time, the problems of data offline statistics, aging lag and the like in the prior art are solved, the cyclic iteration and twin optimization of the comprehensive efficiency and cooperative control of the production line equipment based on digital twin are realized, and the comprehensive efficiency of the production line equipment is improved.
And calculating the bill insertion rate according to the current real-time efficiency and the target efficiency to be achieved of the equipment. If the order insertion rate of the red equipment can be set to be 2; the order insertion rate of the yellow equipment is 5. The instructions of the decision module are issued to the executor mobile robot, and the mobile robot acts on the equipment physical entity. Finally, a twin design of real mapping virtual control real and virtual control real of 'real-time decision data acquisition, real-time OEE calculation and real-time action decision' can be realized.
The above embodiments are merely illustrative of the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the content of the present invention and implement the invention, and not to limit the scope of the invention, and all equivalent changes or modifications made according to the spirit of the present invention should be covered by the scope of the present invention.
Claims (11)
1. A monitoring and collaborative optimization method for comprehensive efficiency of production line equipment based on digital twin is characterized by comprising the following steps:
collecting decision characteristics for calculating the comprehensive efficiency of the equipment;
calculating and analyzing by adopting a machine learning algorithm according to the decision characteristics, and dividing the shutdown types;
calculating the real-time equipment comprehensive efficiency of the equipment by adopting a standard calculation formula of the equipment comprehensive efficiency and combining the divided shutdown types, and dividing the equipment comprehensive efficiency into a plurality of grades;
and performing bill insertion distribution of the operation tasks according to the grade of the comprehensive efficiency of the equipment, and increasing the efficiency of the equipment with low comprehensive efficiency.
2. The monitoring and co-optimization method according to claim 1, wherein the decision feature comprises a down time partition.
3. The monitoring and collaborative optimization method according to claim 1, wherein the collection manner of the decision characteristics includes one or more of embedded collection, PLC-based control information, and monitoring information collection.
4. The monitoring and co-optimization method according to claim 1, wherein the decision factor of the decision feature comprises time to enter the equipment, time to leave the equipment, number of workpieces to be carried, duty cycle time.
5. The monitoring and collaborative optimization method according to claim 4, wherein taking an automated optical inspection device in a surface mount technology production line of an integrated circuit as an example, the decision factors of the decision characteristics include time for each fixture to enter the device, time for leaving the device, number of circuit boards carried on each fixture, duty cycle time, and number of pins on each fixture to be inspected by the automated optical inspection device.
6. The monitoring and co-optimization method according to claim 1, wherein the shutdown types include normal operation, planned shutdown, unplanned shutdown, change-line shutdown.
7. The monitoring and collaborative optimization method according to claim 1, wherein the standard calculation formula for the plant integrated efficiency is as follows:
OEE＝A×P×Q
in the formula, OEE is comprehensive efficiency of equipment, A is availability utilization rate, P is performance utilization rate, and Q is yield;
the availability utilization rate is calculated by the following formula:
A＝(Lt-UPt)/Lt
in the formula, UPt is unplanned shutdown time of the equipment, lt is start time of the equipment, and the start time is calculated by a formula Lt = Wt-PSt, wt is working time of the equipment, and PSt is planned shutdown time of the equipment;
the performance utilization rate is calculated by the following formula:
P＝Ct×Qp/(Lt-UPt)
in the formula, ct is the working cycle time of the equipment, and Qp is the yield;
the yield is calculated by the following formula:
Q＝(Qp-NG rejected)/Qp
where Qp is the yield and NG rejected is the number of NGs that have not been used in a single test.
8. The monitoring and collaborative optimization method according to claim 1, wherein the levels are divided into three levels according to the integrated plant efficiency, namely a first level of the integrated plant efficiency of 50-100, a second level of the integrated plant efficiency of 40-50 and a third level of the integrated plant efficiency of 0-40.
9. The monitoring and collaborative optimization method according to claim 8, wherein during the distribution of the billing, the billing is preferentially performed on the device with the device comprehensive efficiency in the third gear, and then the billing is performed on the device with the device comprehensive efficiency in the second gear.
10. The monitoring and collaborative optimization method according to claim 8, wherein a billing rate of a device in third gear is greater than a billing rate of a device in second gear.
11. The monitoring and collaborative optimization method according to claim 10, wherein the order insertion rate is calculated as follows:
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| CN116362454A (en) * | 2023-06-03 | 2023-06-30 | 宁德时代新能源科技股份有限公司 | Yield analysis system and method, electronic equipment, storage medium and product |
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| CN116362454A (en) * | 2023-06-03 | 2023-06-30 | 宁德时代新能源科技股份有限公司 | Yield analysis system and method, electronic equipment, storage medium and product |
| CN116362454B (en) * | 2023-06-03 | 2023-10-20 | 宁德时代新能源科技股份有限公司 | Yield analysis system and method, electronic equipment, storage medium and product |
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