JPH039008B2 - - Google Patents

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention is applicable to inspections of sealing defects caused by poor sealing of the opening of flexible containers for sealing and filling of bagged foods, etc. The present invention relates to an inspection method and apparatus that can be used to inspect and control various problems related to bag-like containers, such as gas generation or heat control such as heat sterilization of contents.

[Prior art and problems to be solved] Flexible packaging for solid foods such as boiled beans, pickles, sausages, and various smoked foods made of flexible plastic film or lamination of aluminum foil and plastic film. Bagged foods, etc. that are filled into a bag-like container and sealed in a deaerated state using the vacuum packaging method, may suffer from poor sealing at the mouth, pinholes, gas generation due to deterioration of the contents, or during filling. It is necessary to detect incomplete degassing and reject defective products.

Until now, there have been very few automated systems that have inspected this type of packaged food, and it detects the slight amount of food trapped in the sealed area based on differences in thermal conductivity. The only known testing method was to do so.

However, this inspection method also has problems with responsiveness and reliability, and cannot be put into practical use because it can only detect biting foreign objects, and the various detections mentioned above are still performed by human visual judgment. That was the actual situation.

Although this visual discrimination can effectively detect defects such as dirt that are difficult to detect with mechanical equipment, there are many detection errors due to individual differences in the discrimination criteria among inspection workers and worker fatigue, resulting in poor product quality. There was a problem with quality assurance. Therefore, there has been a desire for an automatic inspection method that can perform reliable discrimination without variation in discrimination criteria and that can perform non-contact detection similar to visual discrimination, and an apparatus used to carry out the inspection method.

On the other hand, when observing the appearance of sealed bag-shaped containers containing bagged foods, etc., as shown in Figures 1 and 2, good products that have been sufficiently degassed are In contrast, a defective product with insufficient degassing has a void inside the bag-like container. , there is less close contact between the contents and the film, and the container surface becomes an enveloping surface of the protruding tip of the contents, with fewer minute irregularities.

These surface conditions can be sufficiently sensed by the tactile sense of the fingers, and although it is possible to automate this, it is difficult to implement this method due to problems with the responsiveness of the contacts.

In addition, good products have minute light and dark spots on the container surface with strong contrast, while defective products have fewer minute spots on the container surface and are smooth and have weak contrast. This corresponds to the above-mentioned tactile discrimination of fingers. Therefore, it is considered possible to determine whether a bag-shaped container is good or bad by determining the degree of fineness.

The present invention has been made in view of the above points, and
A sealing inspection method for flexible containers that can automatically perform highly responsive and reliable inspections under the same conditions as visual inspection by utilizing the differences in appearance between the container surfaces of non-defective and defective containers, and the method thereof. The purpose is to provide equipment.

[Means for Solving the Problems] In order to achieve the above object, the seal inspection method of the present invention illuminates the surface of a flexible container vacuum-filled with contents so as to create a shadow, and Detects light and dark signals along any line on the surface,
Next, this bright/dark signal is computationally analyzed into a spatial frequency spectrum, and the sum of the intensities of each spectrum in this spatial frequency spectrum in a range where features indicating a good product exist and a range where features indicating a defective product exist are calculated. The above-mentioned flexible container is determined by determining the sum of the intensities of the respective spectra in The seal inspection device of the present invention for carrying out this method inspects the surface of a flexible container vacuum-filled with the contents.
Illumination means for illuminating to create shadows, a camera for detecting brightness signals along arbitrary lines on the surface of the flexible container, and Fourier transformation means for Fourier transforming the brightness and darkness signals to obtain a spatial frequency spectrum. and an addition means for calculating the sum of the intensities of each spectrum in the range where features of the spatial frequency spectrum are present that are good products, and means for calculating the sum of the intensities of each spectrum in the range where features that are defective products of the spatial frequency spectrum are present. By comparing the addition means for calculating the sum, the division means for calculating the ratio of these sums, and whether the calculated ratio is larger or smaller than a preset discrimination level, it is possible to determine whether the flexible container is good or not. The configuration includes comparison means for determining defective products.

[Function] As a result, the present invention can detect defective products caused by poor sealing at the mouth, leakage due to pinholes, insufficient deaeration, gas generation due to deterioration, etc., in the same state as when the above-mentioned visual discrimination is automated. This enables optical and non-contact inspection.

[Example] Hereinafter, an example of the present invention will be described based on the drawings.

First, an example of an apparatus used to carry out the sealing inspection method will be explained with reference to FIG.

Reference numeral 101 is a bag-like flexible container filled with, for example, coffee beans, and reference numeral 102 is a floodlight that illuminates the surface of the flexible container 101 so as to create a shadow. 103 is a linear sweep camera that photographs the surface of the flexible container 101 illuminated by the floodlight 102 along an arbitrary line, detects brightness and darkness signals obtained according to the unevenness of the surface, and responds to this brightness and darkness signal. A video signal 104 is output. When photographing, the surface of the flexible container 101 is exposed to the camera 10.
Adjust so that it covers the entire field of view in step 3, and
Photograph the area indicated by the arrow. This avoids photographing the top seal or bottom part, which maintains a constant shape and surface condition regardless of whether it is a good or defective product, and instead photographs only the parts that have many irregularities due to the contents, allowing for sufficiently high accuracy. This is to obtain a video signal 104.

Note that this photographing range A is appropriately selected in accordance with the size of the flexible container 101, the mass of the contents, etc., so that good discrimination can be performed. For example, the flexible container 101 filled with coffee beans of this embodiment is 15 cm long.
Assuming that the height is , the photographing range A is preferably about 8 to 12 cm. Therefore, the following explanation will be based on the case where the photographing range A is 10 cm.

Reference numeral 105 denotes a linear light receiver that is built into the camera 103 and is made up of 1024 semiconductor photovoltaic elements. The resolution of the camera 103 in this case is 0.0977 mm since the photographing range A is 10 cm, which is sufficiently smaller than the size of the unevenness on the surface of the flexible container 101 caused by a single coffee bean. Precision can be maintained.

Reference numeral 106 denotes a sweep clock pulse generator, which uses a clock pulse to read out brightness and darkness signals detected by the light receivers 105 in the camera 103 in the order in which the light receivers 105 are arranged, and outputs them as a video signal 104. If the period of the clock pulse at this time is 1 μS, the dimensional length of the flexible container surface is 0.0977 mm.
to a temporal length of 1 μS, and a dimensional length of 10 cm.
Converted to a time length of 1024 μS. Therefore,
For example, an average of approximately 1 cm of light and dark repetition is approximately 100 μS on average.
video signal 1 with a repetition period of 10KHz, i.e.
It becomes 04.

Note that as the light receiver 105, one having various numbers of elements can be appropriately selected depending on the required resolution.

107 is an A-D conversion circuit, which converts the video signal 104
is converted from an analog signal to a digital signal and sent to a digital Fourier transform analysis circuit 108.
The analysis circuit 108 computationally analyzes the video signal 104 converted into a digital signal into a spatial frequency spectrum, and stores the result in a built-in storage device 109. Here, the spatial frequency spectrum refers to the Fourier transform of the bright and dark intensity distribution caused by the unevenness of the surface of the container, and is arranged in order of frequency.

110 and 111 are adders, of which the first adder 110 is
The sum P ₁ is calculated by reading out each stored value of the peak range of the characteristic of a non-defective product from the address to the address j, and outputs it to the output line 112. On the other hand, the second adder 111 is
Read each memory value from address k to address l in the range that is characteristic of defective products, calculate the sum P ₃ , and connect it to output line 1.
Output to 13.

114 is a divider which calculates the ratio R ₂ of both outputs P ₁ and P ₃ of output lines 112 and 113, and outputs the output line 1.
Output to 15.

116 is a subtracter that compares the output R ₂ of the divider 114 with the discrimination level value R ₀ inputted in advance by a manually set digital switch 117 to determine whether the product is good or defective. When this occurs, the signal is output to the output line 118.

Here, we will consider the spectral pattern analysis method.

◎Spectral pattern analysis method (Part 1) As the simplest analysis method, the height of the spectral component stored at each address in the storage device is calculated by M _o
Then, take the sum P ₁ of each spectral component from address i to j in the range where the characteristic peak group of a good product exists, and compare this value P with a preset discrimination level P ₀ to determine whether it is a good product or a defective product. Do this.

That is, if P ₁ = _j 〓 ⁿ⁼¹ Mn P ₁ −P ₀ ≧0, it is a good product; if P ₁ −P ₀ <0, it is a defective product.

However, this analysis method has the disadvantage that it is necessary to maintain constant ambient conditions such as the brightness of the illumination and the reflectance of the surface of the flexible container, and if these conditions are not constant, the discrimination accuracy will decrease.

◎Spectral pattern analysis method (Part 2) This analysis method eliminates the drawbacks of the above analysis method (Part ₁ ) by compensating for the brightness of the illumination. The sum of addresses i and j in which the peak group exists is P ₁
The ratio R is determined, and this value R is compared with a preset discrimination level _R0 to determine whether the product is good or defective.

That is, R=P ₁ /P ₂ = _j 〓 ⁿ⁼¹ Mn/ _o 〓 ⁿ⁼⁰ Mn If R−R ₀ ≧0, it is a good product. If R−R ₀ <0, it is a defective product.

However, this analysis method can determine whether the flexible container is good or not.
In other words, since calculations are performed including spectral regions showing almost the same pattern, which are unrelated to good and defective products, there is a drawback that accuracy is reduced accordingly.

◎Spectral pattern analysis method (Part 3) This method eliminates the drawbacks of the above analysis method (Part 2).It eliminates spectral regions that are unrelated to the discrimination between good and defective products, and detects the characteristic peaks of good products. The sum P ₁ of addresses from i to j in the range where the group exists,
The ratio R ₂ of the sum P ₃ of addresses from k to l where the characteristics of a defective product exist is determined, and this value R ₂ is compared with a preset discrimination level R ₀ to determine whether the product is good or defective.

That is, R=P ₁ /P ₃ = _j 〓 ⁿ⁼¹ Mn/ _l 〓 ⁿ⁼⁰ Mn If R ₂ −R ₀ ≧0, it is a good product. If R ₂ −R ₀ <0, it is a defective product.

The above description of this embodiment is based on the case where the spectral pattern analysis method (part 3) is adopted.

In addition, various variations of the spectral pattern analysis method can be considered, but various spectral patterns can be obtained depending on the contents filled in the flexible container. Printed patterns and letters on the surface may also become an obstacle. Therefore, in such cases, the above-mentioned
It is necessary to appropriately select the spectral range used for arithmetic processing, such as by applying methods (1) to (3), and also to select an analysis method according to various conditions.

Note that the above processing unit is a normal TTL
Although an example has been shown in which a hard-wired digital arithmetic circuit based on Transistor-Transistor Logic is used, the processing of the Fourier transform analysis circuit and subsequent circuits can also be performed by a microcomputer or the like.

Further, data input/output to/from the computer, ie, digitized video signals and pass/fail determination signals, are performed via a digital input/output interface.

120 is a trigger timing circuit that controls each arithmetic processing block in the order of each step described above. That is, when the flexible container 101 enters the field of view of the camera 103 and is at a position where a proper video signal 104 can be obtained, the position sensor 121, such as a photoelectric type or an eddy current type, detects and starts the photographing operation. ,
Alternatively, the camera 103 is allowed to operate freely and the first rising edge of the video signal is detected to start the operation of the next circuit. However, in the latter case, it is necessary to keep the back shadow of the flexible container 101 in the dark field.

The method to be selected is determined by the shape of the flexible container 101, the state of the surface of the flexible container due to the shape of the contents, and the surrounding conditions such as the brightness of the back shadow. This is done by connecting the changeover switch 122 to the a terminal or the b terminal.

Based on the defective product discrimination signal 118 of the inspection device configured as described above, defective products are eliminated by various means. Note that 119 is a belt conveyor for transporting the flexible container 101.

Next, an embodiment of a method for testing the seal of a flexible container will be described.

Belt conveyor 1 to be sent to the packaging process etc.
The surface of the flexible container 101 moving on the container 09 is illuminated by the illumination means so as to create a shadow. The linear sweep camera 103 photographs an arbitrary line on the surface of the flexible container 101, for example, over a range of 10 cm in a direction perpendicular to the direction of movement. At this time, when sweep clock pulses are continuously applied to the camera 103 from the sweep clock pulse generator 106, the flexible container 10
1, the total width C of the surface in the direction of movement
Since a large number of data can be obtained by repeating the sweep over a wide range, the accuracy of measurement discrimination can be improved. This is equivalent to using the camera 103 as an industrial television camera that performs a normal surface sweep, and photographing the flexible container 101 while it is stationary or moved at a speed sufficiently slower than the sweep speed.

The irregularities on the surface of the flexible container photographed by the camera 103 in this manner are detected by the light receiver 105 as brightness and darkness signals, and read out in the order of arrangement of the light receivers 105 by the clock pulses from the sweep clock pulse generator 105. It is output as a video signal 104 to the A-D conversion circuit 107. The A/D conversion circuit 107 converts the video signal 104 from an analog signal to a digital signal and outputs it to the digital Fourier transform analysis circuit 108 . The analysis circuit 108 computationally analyzes the video signal 104 into a spatial frequency spectrum and stores it in the storage device 109 .

Note that here, since the space is swept temporally, there is no problem in replacing the temporal frequency as it is with the spatial frequency. Therefore, the following description will be made in terms of temporal frequency.

This memory device 109 consists of a group of 256 word memory elements, and addresses 0 to 255 are assigned in order from the lowest frequency to the lowest frequency. A frequency of 1 Hz and a maximum frequency of 256 KHz is expressed per word, and the maximum resolution on the surface of the flexible container 101 is approximately 0.4 mm. Even in this case, in the case of the coffee beans of this embodiment, each grain has a size of about 1 cm, and the irregularities on the surface of the flexible container are approximately equal to this, so that the irregularities can be sufficiently expressed. Further, even if the resolution multiplier in the Fourier transform analysis circuit 108 is selected to be 1/10 and the maximum frequency is set to 25.6 KHz, the unevenness can be sufficiently expressed.

The characteristics of the spectrum pattern analyzed by the Fourier transform analysis circuit 108 are as shown in FIG. 4, which is a graph of the pattern.
In the case of figure a), a weak spectral peak is seen in the low frequency region below 6KHz, and a strong spectral peak is seen in the medium and high frequency region of 6 to 25KHz. , the spectral peak is strong only in the low frequency region below 10 KHz, and there is almost no spectral peak in the medium and high frequency region above 10 KHz.

Next, the first adder 110
The second adder 111 reads out each stored value of the peak range of the characteristic of the good product from address i to j of the storage device 109 and calculates the sum _P1 . Each stored value, which is the peak range, is read out and the sum P ₃ is determined and outputted to the calculator 114 via the respective output lines 112 and 113.

The divider 114 calculates the ratio R ₂ of the outputs P ₁ and P ₃ ,
It is output to the subtracter 116 via an output line 115.
The subtracter 116 is connected to a digital switch 117 in advance.
Discrimination level value R ₀ and output R ₂ set by
to determine whether the product is good or defective,
That is, it is determined whether the photographed flexible container has good sealing performance or not. As a result of the determination, defective products that are determined to have poor adhesion and insufficient degassing are removed from the belt conveyor 119 by, for example, a predetermined defective product removal device. In this way, the sealing test of the flexible container 101 is performed.

Note that each of the above calculation processes is performed according to instructions from the trigger timing circuit 120.

The present invention is capable of inspecting flexible containers, particularly bag-shaped containers, which are hermetically filled with bagged foods, etc. using a vacuum packaging method, for defects in opening sealing or sealing defects due to pinholes, etc. It is also possible to detect gas generation due to spoilage or incomplete deaeration during filling.

Further, the present invention is useful for controlling heating by detecting expansion caused by internal pressure of a flexible container in the process of heating and sterilizing bagged foods, or for heating with uniform temperature distribution. It is also possible to use In other words, for example, when heating with microwaves, if the microwave irradiation causes partial concentrated heating of the food in the flexible container, if the microwave irradiation is continued, water vapor will be generated. As a result, the pressure within the flexible container may become abnormally high, causing the flexible container to burst.
Therefore, the present invention detects the expansion of the flexible container, interrupts the microwave irradiation, lowers the temperature of the abnormal heating area, and then repeats the microwave irradiation intermittently again to control the heating. At the same time, heating is performed with uniform temperature distribution.

Therefore, in this specification, the term "sealing test" includes tests for expanding the flexible container due to various causes.

[Effects of the Invention] According to the present invention as described above, the number of changes in unevenness per unit length on the surface of a flexible container is photographed with a camera, this is displayed as a spatial frequency spectrum, and the high frequency region and Sealing inspection can be performed depending on the degree of low frequency range, so
Responsive and highly reliable inspection can be performed automatically under the same conditions as visual inspection without touching the flexible container.

[Brief explanation of drawings]

Fig. 1 is a perspective view showing the state of a good product filled with the contents in a flexible container, Fig. 2 is a perspective view showing the state of a defective product filled with the contents in a flexible container, and Fig. 3 is a perspective view showing the state of a defective product filled with the contents in a flexible container. FIG. 4a is a graph of the spectral pattern of a non-defective product, and FIG. 4b is a graph of the spectral pattern of a defective product. 101: Flexible container, 103: Camera, 10
4: Video signal, 105: Photoreceiver, 106: Sweep clock pulse generator, 107: A-D converter,
108: Digital Fourier transform analysis circuit, 1
09: Storage device, 110, 111: Adder, 11
4: Divider, 116: Subtractor, 117: Digital switch, 120: Trigger: Timing circuit.

Claims (1)

[Claims] 1. Illuminating the surface of a flexible container vacuum-filled with contents so as to create a shadow, and detecting brightness and darkness signals along any line on the surface of the flexible container, and then: This bright/dark signal is computationally analyzed into a spatial frequency spectrum, and further, the sum of the intensities of each spectrum in the range where the characteristics of a good product exist, and the sum of the intensities of each spectrum in the range where the characteristics of a defective product exist, are calculated. By calculating the sum of the spectral intensities, calculating the ratio of these sums, and comparing whether this ratio is larger or smaller than a preset discrimination level, the above-mentioned flexible container is determined to be non-defective. , a sealing inspection method for flexible containers characterized by determining defective products. 2. The surface of the flexible container vacuum-filled with the contents,
Illumination means for illuminating to create shadows; a camera for detecting brightness and darkness signals along arbitrary lines on the surface of the flexible container; and Fourier transformation means for Fourier transforming the brightness and darkness signals to obtain a spatial frequency spectrum. and an addition means for calculating the sum of the intensities of each spectrum in the range where the features of the spatial frequency spectrum are present that are good, and the sum of the intensities of each spectrum in the range where the features of the spatial frequency spectrum that are defective are present. By comparing the addition means for calculating the sum, the division means for calculating the ratio of these sums, and whether the calculated ratio is larger or smaller than a preset discrimination level, it is possible to determine whether the flexible container is good or not. A sealing inspection device for flexible containers, comprising a comparison means for determining defective products.