The purpose of this utility is to overcome and solve the existing mixer rubber mixing process optimization test deposit
It takes a lot of consumption in the human, material and time, and process specification parameters with various optimum conditions
The production parameters and change the shortcomings and problems, research to design a collection production conditions in everyday life
Parameters related to the production process can be automated mixing process optimization work, even if there are changes in the production conditions which lead
To change the optimum conditions can be optimized parameters of a new optimum conditions to automatically analyze, modify, process planning,
Make mixing process according to the modified procedure for the production of new technology as a mixer rubber mixing process automatic optimization
Of the Monitor.
...
The utility model is structured by the following technical solutions to achieve: the mixer rubber mixing process automatically
Monitor schematic structural view of optimization as 1, which is a signal input apparatus 1, computer control devices 2,
Signal output drive means 5, the printer 4, electrically connected to form the chassis 6 together, their mutual position and connecting related
Department are: signal input device 1 is located within the housing 6 left, the computer control unit casing 6 is located within 2,3 L
At the edge portions, the lower portion of the signal input device 1, the signal output drive means 5 is located at the right side of the intermediate chassis 6
Parts inside the printer 4 in the bottom chassis 6; 6 back in the chassis mounting bracket Terminal Block, the major
Parts fitted cable connector plug, and with the bracket mounted terminal blocks corresponding patch, wherein: the signal input device
Respectively, through the terminal box 1, D-type socket and main mixer motor voltage, current, internal mixer switch,
Analog signal line and mixer phase electrical connection, the computer control unit to the next bit machine 2 through ADT
200 templates and terminal blocks, signal input line and the signal input device 1 phase electrical connection, the host computer 3-way
Over templates, terminal blocks, signal output line and the signal output drive means 5-phase electrical connection, the host computer IBM
3-PC industrial computer via the serial interface COM1 and communication cables and the next computer CM-85
Industrial Computer 2-phase electrical connection, computer-controlled apparatus Host Computer IBM-PC industrial meter
Computer data signal lines 3 through the print, and the printer cable connector with electrical plugs and sockets lotus pick; their structure and composition box
Schematic diagram shown in Figure 2, in which the signal input device 1 includes digital input isolation circuit, analog input isolation
Away from the circuit, instantaneous power acquisition circuit, under computer control device includes bit computer CM-85 Industrial meter
Computer 2, the host computer IBM-PC industrial computer 3, the signal output drive means 5 comprises a switch
Output driver circuit, analog output driver circuit connected to each other the relationship: the signal input device 1
Switch input isolation circuit via a binary input signal line and the mixer phase electrical connection; analog input isolation
Circuit through the analog input signal line and the mixer phase electrical connections; instantaneous power acquisition circuit voltage and current through
Signal line and the mixer main motor phase electrical connection; computer control unit to the next bit machine CM-85
Industrial Computer 2 via a signal input device 1 switch output signal line, analog output signal lines, momentary
Power output signal line and the signal input means electrically connected to one phase; signal output driving means 5 in the output of the switch
A driving circuit through the switch drive input signal line and the lower two-phase electrical machine is connected via switch
Drive output signal line and a driving mechanism mixer with electrical connections, the analog output driver circuit via an analog
Drive input signal line and the next two-phase electrical machine is connected via the analog output signal line drive banburying
Machine actuators with electrical connections; printer 4 by printing signal input line with the host computer IBM-
PC Industrial Computer three-phase electrical connection. Its principle is: the switch, analog input isolation circuit will close
Mill work status signal (digital, analog) after isolation input to the CM-85 Industrial Computer 2
Switch, analog input, while the instantaneous power of main motor acquisition circuit mixer mixing process will consume
Converted to the corresponding power of the instantaneous power signal transmitted to the CM-85 industrial computer 2. CM-85
Industrial Computer 2 input signals on the basis of these mixer mixing process state judge made preliminary calculations,
Finishing, and finishing data sent through the serial port to the IBM-PC Industrial Computer 3. IBM
-PC Industrial Computer 3 has a strong computing power, it will be mixing process data for each data point
Analysis, statistics, map out the mixing process of the instantaneous power curve, and according to the process set value formula to calculate the control
Offset signals, and then through the serial port to transmit the generated control offset to the CM-85 Industrial Computer
2. CM-85 Industrial offset according to the control computer 2 generates a corresponding switch control signal and the analog
Control signals and transmitted through the output port to the signal output drive means 5, the signal output of the switch drive means 5,
Analog control signal optical isolation amplifier, the output drive capability of the switch with a certain amount of signal and analog signals
Used to control the actuator in order to control mixer mixing process automatic optimization purposes.
...
Monitor mixing process automatically optimize the electrical circuit shown in Figure 3 and Figure 4. Figure 3 is a signal in which
Input device circuit schematics and CM-85 industrial computer parts, Figure 4 shows the electrical signal output drive
Road schematics and IBM-PC industrial computer parts. The signal input device 1 instantaneous power acquisition circuit,
16 the same switch input isolation circuit, 16 the same analog input isolation circuit electrically connected in parallel
Lin then constituted. In which the instantaneous power acquisition circuit diagram shown in Figure 5, which consists of power operator W, by the infrared
Light-emitting diodes D1, the infrared light receiving optical coupling consisting of tubes T1 TLP1, transistor T2
Schmitt trigger IC1, monostable shaping circuit IC2, resistors R1 ~ R4, capacitor C1 common string
Electrically connected in parallel form; certain way switch input isolation circuit diagram shown in Figure 6, which is issued by the infrared
Light emitting diode D2, the infrared light receiving optical coupling consisting of tubes T3 TLP2, transistor T4,
Resistor R5 ~ R7 together form series and parallel electrical connections; certain analog input isolation circuit shown in Figure 7,
It consists of Zener diode DW1 ~ DW4, operational amplifier IC3 ~ IC4, optocoupler pieces
TLP3 ~ TLP4, transistor T5, T6, resistors R8 ~ R15, capacitors C2 ~ C5,
Potentiometer RW1 together and electrically connected in series form. The unit circuit is simple works as follows; Figure 5
5 shows the instantaneous power acquisition circuit and its role is the main motor voltage and current signals converted by calculation instantaneous
When the power pulse signals output by the isolation after shaping. The principle is derived from the main motor voltage and current by the power
W operator generates electrical pulse signal, the infrared optical coupling TLP1 there corresponding light emitting diode D1
Pulse current flowing through the infrared receiving tube collector of T1, the corresponding high-low transition, which led to three
T2 collector diode produces a corresponding change in high and low, and through the shaping of IC1 monostable circuit to drive
IC2, IC2 outputs a pulse width signal. Figure 6 shows the switch input isolation circuit its principle
Industrial production site when switch is turned on, the coupler member TLP2 there infrared light diode D2
An electric current flowing through the collector of the infrared receiving tube T3 level will be less than 0.7V, the low level so that the transistor
T4 off, outputs a high level signal. Figure 7 shows the analog input isolation circuit is a circuit using the principle
Two optical coupling TLP3, TLP4 push-pull mode, the two input and output characteristics to be combined
Cheng, amplified by the inverting amplifier IC3 change transistors T5, T6 the emitter current, and constant current
Drive TLP3, TLP4, the TLP3, TLP4 output current of the structure through the amplifier IC4
A current - voltage conversion circuit, the output an analog voltage signal; signal from the output drive means 16
The same switch output driver circuit, 16 identical analog output drive circuitry electrically connected in parallel configuration
Into its switch output driver circuit and the signal input device switch input isolation circuit identical, their
Analog output drive circuitry and signal input device analog input isolation circuit exactly the same, they work
Operating principles are identical, differing only in their input for the computer output of the switch drive signal and analog drive
Dynamic signal, the output is used to drive the actuator.
...
The utility model application for mixing process automatic optimization method works as follows: (1) first determine the optimal inspection
Indicators, the use of automatic optimization of the mixing process monitoring instrument automatically detects the indexes for the optimization; (2) to determine which optimization
These factors, factors that take different values, the level should be able to automatically divide; (3) based on orthogonal experimental design theory, selection and due
Number of factors and levels corresponding orthogonal table, have been identified to optimize the indexes for the main effects and interactions meter
Count (main effect factors on the indicators of the impact against head size, often the response value to represent the amount of change; interaction table
With experimental factors are shown on the impact of each, thereby enhancing or weak base the indicators of each individual experimental factors
Effects), in order to identify the factors required to optimize the indexes on the degree of influence; (4) obtained under orthogonal
Data, the use of mathematical statistics for processing of the experimental data analysis of variance of the detected data side
Analysis of variance to determine the optimal test of various factors on poor indicators of the degree of influence (this quantitative analysis concluded, can be compared
Experience to determine precise, specific). The rubber mixing process automatic optimization theory lies in addition to using the instantaneous power, energy
Deplete, kneading time, mix temperature factors, the average power can be applied to segments, segment energy consumption, such as
Cast raw rubber and other segments, investment cast padding expect oil segment, cast padding expect nesting period, investment and other oilseeds into nesting period and over the whole
Cheng average power factor optimization. Level except for a defined value, the interval can be defined as the horizontal,
Tuning for statistical calculations. If two levels, take the average for the sector, the level 1 ≥ average level of 2 <average
And three levels, taking an average of the center of a symmetrical interval of a horizontal, greater than or less than the interval of about interval,
The other two levels, respectively, as the level of 1 ≤ (mean - standard deviation), (mean - standard deviation) <Level 2
<(Mean + standard deviation), the level 3 ≥ (mean + standard deviation). Automatic optimization of the rubber mixing process
The program flow chart shown in Figure 8. The specific procedures and automatic optimization of mixing process the following steps: (1) use of
Mixer rubber mixing process monitoring detected automatically optimize data collection. From the top bolt off on the top bolt lifted
Cast from raw rubber, the material, powder or reclaimed rubber and carbon black or carbon black fillers cast pressurized oil pressure after cast, sweeping
Powder or put pressure on the top bolt somersault after the last bolt under the roof open, lift the ram, the discharge end of the record each inspection
Measuring point of the time, the compound temperature, instantaneous power, the cumulative energy consumption, as well as the pressure on the top bolt of raw rubber, fillers,
Fuel, and other practical feeding amount of time, every time you start mixing and discharge the cooling water temperature and other relevant data; (2) does
Given test batches (generally 50 times or 100 times as a group, at least not less than 20 times as a group) by
Operator input mixing process automatically optimized Monitor; (3) design tests to determine the optimal index, typically begins
Improve yield optimization, followed by shortening time, save energy optimization (sometimes improve the passing rate, the need
To extend the time or increase energy consumption); ⑷ optimization index based on the identified needs to determine the impact of optimization refers to the
The underlying factors and the different experimental levels of each factor divided on two levels of testing, usually averaged for the community, for the
Three Yongping trial taking an average of the center of a symmetric interval is a level, sometimes less area than the area
Interval between the left and right values were another two levels; (5) using mathematical statistics for processing of the experimental data
The analysis of variance analysis of variance and significance testing to determine the impact of factors on the degree of optimization index. Use
Orthogonal analysis of variance method to determine the optimal factor level combination; (6) Suitability analysis of variance test include:
Test data normality test, independence test equal variance test. If the appropriate test to pass, that the differential side
Analysis of the basic assumptions hold, then the results of analysis of variance is acceptable, complete mixing process automatically optimized. The mixing
Technology automatically optimizes the specific implementation steps and processes program flow block diagram shown in Figure 9.
...
The utility model compared with the prior art has the following advantages and beneficial effects: (1) The utility model uses two
Computer division of responsibilities, the industrial field signal acquisition, signal control, use control ability, good industrial performance
The CM-85 industrial computer. And a large number of data storage, calculation, modeling, etc., using operation can
Strong, large capacity, poor ability to control industrial computer IBM-PC bus. Monitor the entire count
Count, the analysis is carried out in an offline state, avoiding the single machine system crashes caused by overloaded operators and calculations
Error, making the monitoring device with high reliability and good working interface; (2) CM-85 Industrial
Computer address, data, control three buses are optically isolated, so that in the harsh environment can withstand all kinds of
Normal, common-mode interference; IBM-PC industrial computer, using a common PC bus, passive motherboard and
Corresponding function templates, making the computer a simple structure, easy to install, sealed, maintenance; (3) two computers
Using serial asynchronous communication, sharing of resources makes more reliable, and can be installed according to the site conditions will be two sets of machines
Placed in different locations, but also for ultra-long distance communication; (4) Monitor all signals of the input and output are used light
Electrical isolation, making all kinds of interference is impossible industrial site through the signal lines to influence the normal operation of the computer; (5)
Monitor may waive the application of the prior art for the rubber process optimization work to be carried out a lot of experimental work;
(6) Since the traditional process in the production of the finished plastic and then test its quality, and the application of the monitoring instrument using mixing process over
Optimization of process parameters for the index, and timely monitoring, making the product is not out of the situation before and know the quality, and can be timely
Automatically adjusted to prevent mass incidents in the first place, it is more direct and effective; (7) the application of the monitoring device can keep abreast of the real
Whether the process of order when the optimum conditions, and real-time automatic process specification maintained at the optimum conditions like
State; (3) the application of the monitoring instrument control the production process, allows mixing the output of each compound are qualified products, can be as large
Greatly improve production efficiency and product qualification rate, there are significant economic and social benefits.
...
Embodiment of the utility model is as follows: (1) in Figure 3 Figure 4 shows a printed circuit board of the circuit draws, then
After filtering components for installation and simple debugging, you can made the signal input and signal output device drives box
Box. In this embodiment: TLP1 optional optocoupler 6N136 type member, TLP2 optional TTL
117-type coupler piece, TLP3 ~ TLP4 optional type optocouplers TLP521 pieces, IC1
Optional 74LS132, IC2 optional 74LS221, IC3, IC4 optional LF356N;
(2) and then press the Figure 1, Figure 2 and described in the specification above, the connection between the corresponding electrical connections, you can
Optimize the mixing process to achieve automatic monitoring instrument; then refer to Figure 8, Figure 9 shows a process flow chart, and press the
Described in the specification of the concrete surface mixing process automatic optimization of processes and procedures, will be able to achieve the mixer rubber mixing
Automatic optimization process monitoring technology. Instead, the automatic optimization of the mixing process monitoring technology several specific embodiments:
...
Embodiment of the utility model is as follows: (1) in Figure 3 Figure 4 shows a printed circuit board of the circuit draws, then
After filtering components for installation and simple debugging, you can made the signal input and signal output device drives box
Box. In this embodiment: TLP1 optional optocoupler 6N136 type member, TLP2 optional TTL
117-type coupler piece, TLP3 ~ TLP4 optional type optocouplers TLP521 pieces, IC1
Optional 74LS132, IC2 optional 74LS221, IC3, IC4 optional LF356N;
(2) and then press the Figure 1, Figure 2 and described in the specification above, the connection between the corresponding electrical connections, you can
Optimize the mixing process to achieve automatic monitoring instrument; then refer to Figure 8, Figure 9 shows a process flow chart, and press the
Described in the specification of the concrete surface mixing process automatic optimization of processes and procedures, will be able to achieve the mixer rubber mixing
Automatic optimization process monitoring technology. Instead, the automatic optimization of the mixing process monitoring technology several specific embodiments:
...
| The number of observations | Plasticity variable residual | Average residual | Standard error | T values | P-value> | T | value |
| 96 batches |
Y1-0.20
|
-0.014
|
0.028
|
-5.634
|
0.0001
|
Table 1 standard error value of the variable that is malleable sub-sample standard deviation of the ratio of the average sub-sample, the general computing public
The formula: standard error
T value is optimized to test the plastic
Whether the effect of the passing rate improved significantly the use of a test statistic. The formula is:
P-value is to determine whether the amount of T-test statistic significant at 95% confidence level distribution of the test statistic T obey,
Its value is greater than | T | probability threshold is based on the probability distribution table T Richard's; entire batch data plasticity
Average of 0.183, significantly higher than qualified small index 0.20, and thus the bulk of the total energy consumption is also mixing
May be too small; (4) the optimization of the discharge point to the discharge of the total energy consumed when the factor A, plasticity pass rate PY1
As the goal, the level of divided as follows: Level 1 ≤ 15.04KH,
Level 2> 15.04KH, side
Analysis of variance shown in Table 2:
| Source of variance | Freedom | Squares | Mean square | F value | P-value> F |
|
A
|
1
|
0.8041
|
0.8041
|
4.40
|
0.0387
|
| Error |
94
|
17.1959
|
0.1829
| | |
| Total sum of squares |
95
|
18.0000
| | | |
Table 2 is plasticity at different levels (range) when the mixing energy consumption analysis of variance table cleared. Where, F
Value is to test different levels of impact on energy consumption is a significant test statistic, which is calculated as:
. (For the significance level α = 0.05, the factor A on Py1 have
Significant impact on the level α is significant to determine the reliability of test results, are determined according to the actual needs a
Allow the probability of a wrong judgment, generally in the production of 5%), the level of Py1 listed in the following Table 3:
| Level | The number of observations |
PY1
| Standard deviation |
|
1
|
73
|
0.6986
|
0.4620
|
|
2
|
23
|
0.9130
|
0.2881
|
Table 3 shows a high level of energy consumption
plasticity yield Py1 2 has improved significantly. When the observed values nesting
The total energy consumed in Table 4 below:
| Number of observations | Least | Maximum | Average | Standard error |
| 23 D |
15.15
|
17.89
|
15.93
|
0.78
|
Seen from Table 4, it is desirable when the average 15.93, nesting optimum; (5) cast filler points optimized to cast filler
The energy consumption factors point A, the target plasticity Y1, horizontal, divided as follows in Table 5:
| Level |
1
|
2
|
3
|
| Value |
＜2
|
[2,3.587]
|
＞3.587
|
Analysis of variance shown in Table 6:
| Source of variance | Freedom | Squares | Mean square | F value | P-value> F |
|
A
|
2
|
0.0080
|
0.0040
|
5.66
|
0.0049
|
| Error |
86
|
0.0611
|
0.0007
| | |
| Total sum of squares |
88
|
0.0691
| | | |
From Table 5, Table 6, the energy consumption of the filler cast to have a significant impact on the value of Y1. If the value of each level of Y1
Table 7:
| Level | The number of observations | Average Y1 | Standard deviation |
|
1
|
24
|
0.170
|
0.022
|
|
2
|
44
|
0.192
|
0.028
|
|
3
|
21
|
0.180
|
0.028
|
Seen from Table 7, the
level 2, the average value of Y1 in the closest co-Szeged 0.20 (median value of all test data
In the middle of a qualifying test data).
Level 2 energy points of the vote as filler Table 8:
| The number of observations | Least | Maximum | Average | Standard error |
|
44
|
2.169
|
3.586
|
3.073
|
0.373
|
Table 8 shows the average energy consumption of 3.073 when the optimum. (6) conduct a comprehensive analysis: the entire batch of data points and nesting
Cast filler point energy is the optimal range of observed values of Y1 and PY1, summarized in Table 9:
| The number of observations |
Y1
|
PY1
|
|
14
|
0.198
|
0.929
|
Seen from Table 9, Y1 and qualified in the value (0.20) is very close, so the overall pass rate has large PY1
Large increase; (7) for checking the suitability, because it has passed, the above result is acceptable; (8) To further improve the
Pass rate, the available results of the optimization of production to a certain volume (more than 20 times) and then be optimized to further
Improve yield.
Example 2: To improve efficiency and save energy. (1) Use the rubber mixing process of the mixer automatically optimizes monitoring
Instrument mix batches collected data about 166; (2) use three factors two level factorial experiment. Factors A, B,
C represent the cast filler point, cast oil discharge point and the point of time, the level of division is as follows 10:
| Factor |
A
|
B
|
C
|
| Level |
1
|
2
|
1
|
2
|
1
|
2
|
| The value (s) |
≤16
|
＞16
|
≤84
|
＞84
|
≤166
|
＞166
|
(3) Statistical analysis of the energy consumption in Table 11 to Table 14 below:
Table 11
| Source of variance | Freedom | Squares | Mean square | F value | P-value> F |
| Model
Error
Total sum of squares |
7
158
165
|
8.7411
51.3715
60.1126
|
1.2487
0.3251
|
3.84
|
0.0007
|
Table 12
| Source of variance | Freedom | Type 1SS | Mean square | F value | P-value> F |
|
A
B
A*B
C
A*B
B*C
A*B*C
|
1
1
1
1
1
1
1
|
0.5531
2.0493
5.3969
0.0047
0.0021
0.0000
0.7349
|
0.5531
2.0493
5.3969
0.0047
0.0021
0.0000
0.7349
|
1.70
6.30
16.60
0.01
0.01
2.26
2.26
|
0.1940
0.0131
0.0001
0.9048
0.9935
0.1347
0.1347
|
Table 13
| Source of variance | Freedom | Type 3SS | Mean square | F value | P-value> F |
|
A
B
A*B
C
A*C
B*C
A*B*C
|
1
1
1
1
1
1
1
|
0.9756
1.9367
2.1780
0.0642
0.1142
0.0531
0.7349
|
0.9756
1.9367
2.1780
0.0642
0.1142
0.0531
0.7349
|
3.00
5.96
6.70
0.14
0.35
0.16
2.26
|
0.0852
0.0158
0.0105
0.7068
0.5543
0.6866
0.1347
|
Table 14
| A factor | B factor | C factor | The number of observations | The average energy consumption | Standard deviation |
|
1
1
1
1
2
2
2
2
|
1
1
2
2
1
1
2
2
|
1
2
1
2
1
2
1
2
|
96
8
6
9
11
11
2
23
|
22.46
22.60
21.87
21.67
22.47
22.33
22.21
22.64
|
0.49
0.57
0.62
0.49
0.93
0.25
0.16
0.77
|
Table 11 shows that the impact of factors on the indicators is significant. Listing 13, Table 14, the selected significant
Level α is 0.05, the factor B and A can be considered the interaction factors are significant, where the type
1SS and type 3SS denote the first and third categories estimable function corresponds to the sum of squares, and each level of group
Bonded energy situation as shown in Table 14. From the energy point of view A factor level 1, B and C
factors factor level 2 level
A factor level combination and A factor level 1, B and C
factors factor level 2
level 2 level combination of factors
Compare energy, respectively 21.87KWH and 21.67KWH; (4) on the time-consuming statistical analysis is as follows
Table 15 to Table 18:
Table 15
| Source of variance | Freedom | Squares | Mean square | F value | P-value> F |
| Model
Error
Total sum of squares |
7
158
165
|
2483.98
933.46
3417.45
|
354.85
5.91
|
60.06
|
0.0001
|
Table 16
| Source of variance | Freedom | Type 1SS | Mean square | F value | P-value> F |
|
A
B
A*B
C
A*B
B*C
A*B*C
|
1
1
1
1
1
1
1
|
1078.80
733.44
342.44
220.98
24.29
69.42
24.60
|
1078.80
733.44
342.44
220.98
14.29
69.42
24.60
|
182.60
124.14
57.96
37.40
2.42
11.75
4.16
|
0.0001
0.0001
0.0001
0.0001
0.1218
0.0008
0.1430
|
Table 17
| Source of variance | Freedom | Type 3SS | Mean square | F value | P-value> F |
|
A
B
A*B
C
A*B
B*C
A*B*C
|
1
1
1
1
1
1
1
|
72.90
114.42
67.84
324.86
38.34
87.84
24.60
|
72.90
114.42
67.84
324.86
38.34
87.84
|
12.34
19.37
11.48
54.99
6.49
14.87
4.16
|
0.0006
0.0001
0.0009
0.0001
0.0118
0.0002
0.0430
|
Table 18
| A factor | B factor | C factor | The number of observations | Average time | Standard deviation |
|
1
1
1
1
2
2
2
2
|
1
1
2
2
1
1
2
2
|
1
2
1
2
1
2
1
2
|
96
8
6
9
11
11
2
23
|
174.52
176.50
174.00
178.33
174.27
176.91
175.50
185.78
|
1.90
1.07
0.00
1.66
4.73
1.14
2.12
3.80
|
From Table 15 to Table 17 shows A, B, C three factors and their interactions Ministry is very significant, and from Table 18
Each level combination of time-consuming case is the shortest time-consuming visible level combination 1-2-1, which is the second smallest of energy
That level combination; (5) In order to select the optimal combination of levels, should be handled as follows (ie in some combination of levels
Operation).
First, the energy consumption are shown below in Table 19
| Handle | The number of observations | Average | Standard deviation | Standard error | Variance | T values | P-value> | T | |
|
1-2-1
|
6
|
21.87
|
0.62
|
0.25
| Equal |
0.6641
|
0.5230
|
|
1-2-2
|
9
|
21.67
|
0.49
|
0.16
| Not equal |
0.6964
|
0.4985
|
Ho: variances are equal, F '= 1.56, degrees of freedom = (5,8) (P value> F') = 0.5477
Table 19 shows the results of the comparison, whether or not two equal variances processing, no significant difference in the mean,
And testified, the variance of two treatments had no significant difference.
Second, the time-consuming compared as follows in Table 20
| Handle | The number of observations | Average | Standard deviation | Standard error | Variance | T values | P-value> | T | |
|
1-2-1
|
6
|
174.00
|
0.00
|
0.00
| Equal |
-7.84
|
0.0001
|
|
1-2-2
|
9
|
178.33
|
1.66
|
0.55
| Not equal |
-6.32
|
0.0000
|
From Table 20 Comparative seen, whether or not equal to the variance of two treatments, there were significant differences in the mean; (6)
In summary, we can see 1-2-1 for the best deal on the handle 1-2-1 6 observations cast filler
Points, cast oil discharge point and point time averages as follows 21:
| | Least | Maximum | Average |
| Cast filler point (s)
Oil cast point (s)
Discharge point (s) |
15
87
164
|
15
93
165
|
15
90
164.8
|
Resulting in optimal time control value: 15 seconds cast filler and 90 seconds to hit oil, 165 seconds nesting.
Click here to optimize the order for the production will be comparable to the original traditional process (average total energy consumption 22.15KWH, total
Time 176.5 seconds) production, each batch of mix energy savings 0.28KWH, Festival 11.5 seconds; (7)
Check the suitability of the model adopted above optimization results can be accepted.
Example 3: Two indicators (plasticity, dispersion) optimization. (1) Use the rubber mixing process automatic mixer
Monitor optimization in the GK-270-type mixer, the collection process, the tread rubber mixing temperature, time, instantaneous
When parameters such as power and energy consumption more than 50 batches; (2) the detection of plasticity and dispersion of the results of the monitoring input
Instrument; (3) through correlation analysis to find right mix quality plasticity and dispersion have a significant impact mixing process parameters:
The Monitor will analyze the mixing process total time (T), the mix discharge temperature (K), instantaneous discharge
Power (P) and the total energy consumption (N) column plasticity significantly affected. The four factors of 50 cars glue
Statistics, basic statistics are as follows Table 22:
...
| | Example 3: Two indicators (plasticity, dispersion) optimization. (1) Use the rubber mixing process automatic mixer
Monitor optimization in the GK-270-type mixer, the collection process, the tread rubber mixing temperature, time, instantaneous
When parameters such as power and energy consumption more than 50 batches; (2) the detection of plasticity and dispersion of the results of the monitoring input
Instrument; (3) through correlation analysis to find right mix quality plasticity and dispersion have a significant impact mixing process parameters:
The Monitor will analyze the mixing process total time (T), the mix discharge temperature (K), instantaneous discharge
Power (P) and the total energy consumption (N) column plasticity significantly affected. The four factors of 50 cars glue
Statistics, basic statistics are as follows Table 22:
... | Maximum | Mean | Standard deviation |
| T (sec)
K (℃)
P (KWH)
N (KWH) |
144
147
0.505
17.81
|
201
176
0.637
23.14
|
171
167
0.630
21.09
|
5.84
5.11
0.0165
0.8390
|
Based on each of the four factors, the mean and standard deviation factor is divided into three intervals, A1 ≤ (mean -
Standard deviation), (mean - standard deviation) <A2 <(mean + standard deviation), A3 ≥ (mean + standard deviation)
Interval defined by these three factors, three levels of the following table 23:
| Factor level |
A1
|
A2
|
A3
|
| T (sec)
K (℃)
P (KWH)
N (KWH) |
T1≤165
K1≤162
P1≤0.6134
N1≤20.5
|
165＜T2＜177
162＜K2＜172
0.6134＜P2＜0.6466
20.5＜N2＜21.9
|
T3≥177
K3≥172
P3≥0.6466
N3≥21.9
|
The four-factor ANOVA and significant test results are in Table 24:
| Factor | Source of variance | Freedom | Squares | Mean square | F value | Threshold | Significance |
|
0.05
|
0.01
|
|
T
| Handle
Error
Total sum of squares |
2
97
99
|
0.00053
0.01583
0.01638
|
0.00027
0.00016
|
1.62
|
3.09
|
4.82
| |
|
K
| Handle
Error
Total sum of squares |
2
97
99
|
0.00076
0.01562
0.01638
|
0.00038
0.00016
|
2.37
|
3.09
|
4.82
| |
|
P
| Handle
Error
Total sum of squares |
2
97
99
|
0.01177
0.00443
0.01620
|
0.005887
0.000046
|
129.8
|
3.09
|
4.82
|
**
|
|
N
| Handle
Error
Total sum of squares |
2
97
99
|
0.00231
0.01407
0.01683
|
0.00115
0.00015
|
7.96
|
3.09
|
4.82
|
**
|
Table 24 shows the time and temperature on the plasticity of the total energy consumption and without discharging the instantaneous power
Significantly, that is to control the mix plasticity control the mixing process lies in the total energy consumption
And nesting when instantaneous power; (b) the mixing process monitoring instrument will analyze the total time (T), when nesting mix
Temperature (K), when nesting instantaneous power (P), the total energy consumption (N) on the rubber compound dispersion effects were
Significant: four factors as basic statistics Table 25:
| Factor | Min | Maximum | Mean | Standard deviation |
| T (sec)
K (℃)
P (KWH)
N (KWH) |
156
161
0.617
19.07
|
180
173
0.662
22.43
|
170
168
0.633
21.14
|
5
3
0.0124
0.7130
|
These three interval is defined as the level of the three factors, they are as follows Table 26:
Based on each element of the mean and standard deviation is divided into three intervals, A1 ≤ (mean - standard deviation) <A2
<(Mean + standard deviation), A3 ≥ (mean + standard deviation) of the four factors analysis of variance and significant
Test results are in Table 27:
| Factor | Source of variance | Freedom | Squares | Mean square | F value | Threshold | Significance |
|
0.05
|
0.01
|
|
T
| Handle
Error
Total sum of squares |
2
33
35
|
6.7649
23.7351
30.5
|
3.3824
0.7193
|
4.70
|
3.28
|
5.31
|
*
|
|
K
| Handle
Error
Total sum of squares |
2
33
35
|
2.9087
27.5913
30.5
|
1.4544
0.8361
|
1.74
|
3.28
|
5.30
| |
|
P
| Handle
Error
Total sum of squares |
2
33
35
|
5.5337
24.9164
30.5
|
2.7669
0.7566
|
3.66
|
3.28
|
5.30
|
**
|
|
N
| Handle
Error
Total sum of squares |
2
33
35
|
8.2130
22.2871
30.5
|
4.1065
0.6754
|
6.08
|
3.28
|
5.30
|
*
|
From Table 27 shows that the total energy consumption of the impact on the dispersion of the most significant, followed by the total time and instantaneous discharge
Also significantly affect the power, and the most influential mix temperature is not significant, so to control the dispersion, the most important is
Control the total energy, followed by controlling the discharge point of the instantaneous power and total time.
(4) when the discharge parameters (time, instantaneous power, to the) optimization: (a) to mix plasticity index
Optimized, taking into account the main factors affecting the plasticity of the instantaneous discharge power and total energy consumption, and time
The impact was not significant, so the first two factors obtained optimal values of the premise, take the minimum time; instantaneous discharge
Power (P) on the plasticity of optimization; the instantaneous power of three levels of plasticity and time-consuming, energy and other parameters
Numerical calculation order listed in the following table 28:
...
| From Table 27 shows that the total energy consumption of the impact on the dispersion of the most significant, followed by the total time and instantaneous discharge
Also significantly affect the power, and the most influential mix temperature is not significant, so to control the dispersion, the most important is
Control the total energy, followed by controlling the discharge point of the instantaneous power and total time.
(4) when the discharge parameters (time, instantaneous power, to the) optimization: (a) to mix plasticity index
Optimized, taking into account the main factors affecting the plasticity of the instantaneous discharge power and total energy consumption, and time
The impact was not significant, so the first two factors obtained optimal values of the premise, take the minimum time; instantaneous discharge
Power (P) on the plasticity of optimization; the instantaneous power of three levels of plasticity and time-consuming, energy and other parameters
Numerical calculation order listed in the following table 28:
... | Collection batches | P Mean | Mean plasticity | Plasticity standard deviation | N Mean | T Mean |
|
A1
A2
A3
|
17
69
14
|
0.599
0.630
0.660
|
0.35
0.33
0.31
|
0.0062
0.0065
0.0084
|
21.39
21.13
20.54
|
173
171
168
|
Because this glue qualified plasticity range from 0.28 to 0.36 with a mean of 0.32, the resulting level of P
A3, the instantaneous power 0.66KWH, the plasticity close to the mean, and the minimum energy consumption, the shortest time-consuming;
Total energy consumption (N) to optimize the plasticity: the same three levels of N plasticity and time consuming, consumption
Order value and other parameters listed in the table 29:
| The level of N | Collection batches | N Mean | Mean plasticity | Plasticity standard deviation | T Mean |
|
A1
A2
A3
|
16
70
14
|
19.86
21.09
22.53
|
0.32
0.34
0.34
|
0.0114
0.0119
0.0127
|
172
170
173
|
Seen from Table 29, the total energy N Take A1 level, plasticity mean exactly coincide with the mean qualified. Integrated P and
N optimization results for plasticity following Table 30:
| Factor | Level | P Mean | N Mean | T Mean | Mean plasticity |
|
P
N
|
A3
A1
|
0.66
0.64
|
20.15
19.86
|
168
172
|
0.31
0.32
|
| Consolidated Results |
0.65kWH
|
20KWH
| 170 seconds | |
(b) with respect to the optimal mix index dispersion, the factors which affect the dispersibility of the time when the discharge
(T) and the instantaneous power (P), the total energy consumption (N), so to optimize these three factors: the time
T between the optimization of three levels by the time T of dispersion energy consuming to organize in the following table 31:
| The level of T | Collection batches | The mean T | Mean dispersion | Dispersion standard deviation | N Mean |
|
A1
A2
A3
|
6
28
2
|
162
170
175
|
5
4
3.5
|
0.8576
0.8450
0.8839
|
20.73
21.21
21.39
|
Seen from Table 31, total time on the horizontal A1, the dispersion of the largest and least energy-consuming, and time is the shortest; pairs nesting
The instantaneous power (P) optimization: P on the level of dispersion of three time-consuming, energy-consuming if carried out in order
The following Table 32:
| The level of P | Collection batches | P Mean | Mean dispersion | Dispersion standard deviation | T Mean | N Mean |
|
A1
A2
A3
|
6
26
4
|
0.6138
0.6330
0.6537
|
4.25
3.75
5
|
0.6892
0.8724
0.0897
|
166
171
166
|
21.045
21.333
20.125
|
Be seen from Table 32, the instantaneous power (P), the level of mean 0.6537KWH A3, the dispersion of the
Large and time-consuming, energy-consuming most provinces; on the total energy (N) optimization: Press N to three levels on dispersion consuming,
Energy to sort out in the following Table 33:
| The level of N | Collection batches | N Mean | Mean dispersion | Dispersion standard deviation | The mean T |
|
A1
A2
A3
|
5
25
6
|
19.784
21.193
22.141
|
5.25
4.00
3.75
|
1.0062
0.8369
0.5342
|
168
70
170
|
Seen from Table 33, the total energy consumption is minimized (mean 19.784KWH), the dispersion of the mean maximum
And time-consuming too short. T, P, N columns dispersion optimization results are in Table 34;
| Factor | Level | Mean | Mean dispersion | N Mean | T Mean | P Mean |
|
T
P
N
|
A1
A3
A1
|
162
0.6537
19.748
|
5
5
5.25
|
20.73
20.125
19.748
|
162
166
168
|
0 632
0.6537
0.656
|
| Consolidated Results | |
20.2
|
165
|
0.65
|
(c) based on the plasticity and dispersion as an indicator of the optimal results are summarized in the following table 35:
| Optimization index | When nesting instantaneous power | Discharge time | Total energy consumption |
| Dispersion
Plasticity |
0.65KWH
0.65KWH
| 165 seconds
170 seconds |
20.2KWH
20.0KWH
|
Be seen from Table 35, respectively, different indicators, were optimized for the same result.
