CN107907445B - Intelligent cigarette soft point and hard point detection method - Google Patents
Intelligent cigarette soft point and hard point detection method Download PDFInfo
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- CN107907445B CN107907445B CN201711085914.2A CN201711085914A CN107907445B CN 107907445 B CN107907445 B CN 107907445B CN 201711085914 A CN201711085914 A CN 201711085914A CN 107907445 B CN107907445 B CN 107907445B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
- G01N9/36—Analysing materials by measuring the density or specific gravity, e.g. determining quantity of moisture
Abstract
The invention relates to cigarette production quality control and detection links, in particular to an intelligent cigarette soft point and hard point detection method. The method comprises the steps of sampling density value data of each cigarette and carrying out sectional processing; and combining the grouped batch sampling data to form an average density data matrix, setting upper and lower limits of average density control according to process requirements, and judging soft point and hard point. By the detection method, the detection precision of the cigarette soft and hard points in the cigarette production process is improved, the consumption is reduced, and the product quality is improved.
Description
Technical Field
The invention relates to cigarette production quality control and detection links, in particular to an intelligent cigarette soft point and hard point detection method.
Background
The uniformity of the density distribution of the cut tobacco in the cigarette is an important factor influencing the stability of physical indexes (resistance to draw, hardness and the like), chemical indexes of smoke (tar, nicotine, carbon monoxide and the like) and sensory quality (permeability, agglomeration and the like) of the cigarette. In the cigarette manufacturing process, the phenomenon that certain section of tobacco shreds in the cigarette have too high or too low density, namely hard spots and soft spots, can be caused by feeding factors such as tobacco shred uniformity (such as tobacco winding, wet mass, stem sticks and the like) and equipment factors such as negative pressure fluctuation of an air chamber.
The common detection method of cigarette equipment in the current industry for soft-point and hard-point cigarettes comprises the following steps: and (3) eliminating the soft and hard point cigarettes exceeding the absolute limit percentage of hard points and soft points in the production process by artificially setting the integral average density of each cigarette and manually setting the absolute limit percentage of the hard points and the soft points under the standard density. However, in the actual production process, the detection method has high false picking and missing picking rates and poor practicability, and is mainly expressed in the following two points:
1) the detection benchmark is inaccurate, the increment of the dense ends of cigarettes under different configuration of the planisher specifications (the groove length and the depth of the planisher) is different in size, if the soft points and the hard points are detected by taking a density standard baseline percentage line as the benchmark, the tobacco shreds in the cigarettes are distributed in a bowl shape with large density at two ends and small density at the middle section, and the equipment judges whether the cigarette is a hard point cigarette or a soft point cigarette according to the absolute percentage exceeding the theoretical average density, so that a large number of cigarettes can be mistakenly picked or missed picked, for example, wet mass tobacco shreds with the same size can be easily picked at two ends of the cigarettes if the tobacco shreds are distributed, and the tobacco shreds are difficult to be picked if the tobacco shreds are distributed.
2) The limit adjustment is unreasonable, after the mechanical structure of equipment such as the leveler specification is determined, the increment of the cigarette dense end is determined by process parameters such as raw materials and equipment, in the production process, the process parameters have fluctuation and can not meet the requirement of the accuracy of the real-time parameter adjustment only by artificial adjustment, and a large number of cigarettes can be mistakenly picked or missed.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent cigarette soft point and hard point detection method, and by the detection method, the detection precision of cigarette soft points and hard points in the cigarette production process is further improved, the consumption is reduced, and the product quality is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent cigarette soft point and hard point detection method comprises the steps of sampling density value data of each cigarette and performing sectional processing; combining grouped batch sampling data to form an average density data matrix, setting upper and lower limits of average density control according to process requirements, and judging hard and soft points, wherein the method specifically comprises the following steps:
1) periodically sampling the density value of each cigarette at a high speed to obtain sampling data, wherein k density sampling data can be obtained for each cigarette;
2) dividing the k sample data of each cigarette into 1 … … n sections for processing, wherein k can be divided by n, and calculating the average density of each section by using arithmetic mean to obtain the average density array Di (i =1, …, n) of each section after segmentation;
3) performing grouping accumulation preprocessing on the segment average density arrays Di, setting the accumulated cigarette count of each group as m, and obtaining an average density data matrix X of m cigarettes in a segmentation mode, wherein X = mxn;
4) setting the total rejection percentage requirements of soft points and hard points, pre-calculating the segment average density of each segment and the upper and lower control limit set values UCLi%, (i =1, …, n) and LCLi%, (i =1, …, n);
5) comparing the average density of each section with the upper and lower control limits of the section, and if the data of the section j (j is more than or equal to 1 and less than or equal to n) exceeds the control, representing that the cigarette in the section is unqualified with the abnormal soft points and hard points.
As a further improvement, the density matrix X can be re-extracted every fixed time by the density matrix and upper and lower limit calculation method, the average density of the section and the upper and lower limits are re-calculated, and the automatic parameter checking at regular intervals is realized.
By adopting the technical scheme, the invention can remove the cigarettes with the soft-point and hard-point defects more accurately in the cigarette production process, and simultaneously can avoid the unnecessary waste caused by mistaken removal, thereby leading the cigarette manufacturing to be in the direction of intelligent development.
Drawings
FIG. 1 is a schematic flow chart of an intelligent cigarette soft point and hard point detection method.
FIG. 2 is a sectional distribution diagram of the internal curve of the cigarette.
Detailed Description
For the purpose of promoting an understanding of the subject matter sought to be protected, its operation, and many advantages will be understood from the accompanying drawings and the following description.
As shown in fig. 1 and 2, the intelligent cigarette soft point and hard point detection method comprises the following steps:
1) in the cigarette production process, high-speed sampling is periodically carried out on the density value of each cigarette to obtain sampling data, and k density sampling data d can be obtained for each cigarette1~dkCalculating d1~dkThe arithmetic mean value of (a), which is the density value of the whole smoke.
2) Dividing the k sampling data of each cigarette into 1 … … n sections for processing (wherein n is selected as: k may be divided by n) and the average density of each segment is calculated by arithmetic mean to obtain the average density array Di (i =1, …, n) of each segment after segmentation.
3) And performing grouping accumulation preprocessing on the segment average density arrays Di, setting the accumulated cigarette count of each group as m cigarettes, and obtaining an average density data matrix x (m multiplied by n) of the m cigarettes in the segmentation mode.
4) According to the technological requirements and basic principle of quality management, the total rejection percentage of soft points and hard points is required to be y%, and the segment average density of each segment and the control upper and lower limit set values LCL are pre-calculated through the actual production of cigarettes in batchesi% (i =1, …, n) and UCLi% (i =1, …, n), i.e. above the upper control limit UCL represents the presence of hard spot anomalies in the segment, and below the lower control limit LCLiRepresenting the presence of a soft spot anomaly for the segment.
5) And when the method is applied on line, acquiring k density sampling data of each cigarette in real time, calculating to obtain the average density of the section of n sections, comparing the average density of the section of each section with the upper and lower control limits of the section, and if the data of the section j (j is more than or equal to 1 and less than or equal to n) exceeds the control limit, representing that the section of cigarette is unqualified with abnormal soft points and hard points.
6) And re-extracting the density matrix X at fixed time intervals T, and re-calculating the average density of the section and the upper and lower control limits to realize regular automatic parameter checking.
In one embodiment, the method comprises the steps of:
1) in the cigarette production process, a microwave sensor which is widely used at present is selected to be used as an online periodic density measuring device instead of a nuclear sensor (which is used more conventionally), and 125 density measurement data d1 ~ d125 can be obtained for each cigarette.
2) The cigarette is set to be processed by 5 sections, each section comprises 125/5=25 continuous density measurement data, namely the average density Di of each section is: d1= D1+ … … + D25, D2= D26+ … … + D50, D3= D51+ … … + D75, D4= D76+ … … + D100, D5= D101+ … … + D125.
3) Grouping pretreatment is carried out on the continuous cigarettes, 1024 cigarettes/group is adopted as the grouping rule, and then the average density of 5 sections of every 1024 cigarettes, D1 ', D2 ', D3 ', D4 ' and D5 ' can be obtained.
4) According to the process requirements, the soft-point and hard-point rejection ratio is set to be 0.5%, 10 ten thousand cigarettes are continuously pre-produced, and it is found that when the upper control limit of each section is respectively UCL1=22%, UCL2=23%, UCL3=21%, UCL4=24%, UCL5=21% and LCL1=21%, LCL2=24%, LCL3=26%, LCL4=23%, LCL5=22%, namely when D1 > D1 ', and (D1-D1 ')/D1 ' > 22%, the section is judged to be abnormal; when D1 < D1 ' and (D1 ' -D1)/D1 ' >21%, it represents that the segment is a soft-spot anomaly.
5) As described above, if 5X2=10 types of abnormal smoke exceed the upper and lower limits, a single number parameter may be set, that is, when several types of abnormal smoke occur, the abnormal smoke represents that the section is a hard-spot smoke or a soft-spot smoke. Here, we set 3 segments, that is, when 3 of the 10 types exceed the upper and lower limits, that is, the whole cigarette is determined to be abnormal.
6) And (3) accumulating density data of 10 ten thousand cigarettes to form a density matrix again at T =30 minutes of system setting parameters, namely every 30 minutes, namely recalculating the upper and lower control limits described in the point 4 in real time, so that the rejection precision is always kept at a higher level.
The invention has the advantages that: the sectional detection is realized, the number of sections is adjustable, the section length is adjustable, the threshold value can be adjusted in real time, and the contrast base number is adjustable. The method can improve the detection precision of the cigarette hard points and the cigarette soft points in the production process of the cigarettes, reduce consumption, improve product quality and enable the cigarette manufacture to be more intelligent.
Claims (2)
1. An intelligent cigarette soft point and hard point detection method is characterized in that each cigarette is subjected to sectional treatment by sampling density value data of each cigarette; combining grouped batch sampling data to form an average density data matrix, setting upper and lower limits of average density control according to process requirements, and judging soft point and hard point, wherein the method comprises the following steps:
1) periodically sampling the density value of each cigarette at a high speed to obtain sampling data, wherein each cigarette obtains k density sampling data;
2) dividing the k sample data of each cigarette into 1 … … n sections for processing, wherein k can be divided by n, calculating the average density of each section by using arithmetic mean, and obtaining the average density array Di of each section after segmentation, wherein i is 1, …, n;
3) performing grouping accumulation preprocessing on the segment average density arrays Di, setting the accumulated cigarette count of each group as m cigarettes, and obtaining an average density data matrix X of the m cigarettes in a segmentation mode, wherein X is mxn;
4) setting the total rejection percentage requirements of soft points and hard points, pre-calculating the segment average density of each segment and controlling upper and lower limit set values UCLi%, i is 1, …, n and LCLi%, i is 1, …, n;
5) comparing the average density of each section with the upper and lower control limits of the section, if j exceeds the control limit and j is more than or equal to 1 and less than or equal to n as long as j section data exceeds the control limit, representing that the cigarette in the section is unqualified with soft point and hard point abnormity.
2. The method for detecting the soft point and the hard point of the intelligent cigarette as claimed in claim 1, wherein the density matrix X and the upper and lower limits are calculated by re-extracting the density matrix X at regular intervals, re-calculating the average density of the segment and controlling the upper and lower limits, and realizing the regular automatic parameter check.
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| CN1108910A (en) * | 1993-12-03 | 1995-09-27 | 吉第联合股份公司 | Method and device for determining the density of a stream of fibrous material on a cigarette manufacturing machine |
| US5690127A (en) * | 1994-07-28 | 1997-11-25 | Lorillard Tobacco Company | Hollow cigarette |
| JP4751189B2 (en) * | 2004-12-10 | 2011-08-17 | ソシエテ ナショナル デクスプロワタシオン アンドストリエル デ タバック エ アリュメット ソシエテ アノニム | Tobacco packing density graphical method and apparatus |
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