Mobile detection system and method for thickness of composite plastic film based on infrared spectrum

Abstract

The invention discloses a mobile detection system and method for the thickness of a composite plastic film based on an infrared spectrum. Comprising a spectrograph motion control device and a composite plastic film motion control device, can control the spectrograph and the composite plastic film to move, realizes multi-point scanning of the spectrograph on the composite plastic film, reduces errors caused by non-uniform film thickness, and also can meet production online detection requirements. The invention also provides a thickness movement detection method of the multi-component composite plastic film based on the infrared spectrum, a quantitative model is established by adopting a combined interval partial least squares regression method, and the accurate detection of the thickness of each component of the composite film is realized.

Description

Technical Field

[0001] The present invention relates to the field of plastic film thickness detection, and particularly to a moving detection system and method for the thickness of composite plastic films based on infrared spectroscopy. Background Art

[0002] Composite plastic films are multi-layer films formed by compounding two or more plastic materials with different properties through processes such as co-extrusion, coating, and lamination, and are widely used in fields such as food packaging, medical protection, agricultural covering, electronic packaging, and industrial protection. As a key parameter, its thickness directly affects mechanical properties (such as tensile strength and puncture resistance), barrier properties (such as oxygen and water vapor barrier), optical characteristics, and processing suitability, and is a core index for product design and quality control.

[0003] Due to the influence of equipment accuracy, process parameter fluctuations, and the environment during the production of composite plastic films, a completely uniform film hardly exists. Composite plastic films need to monitor the thickness, uniformity, and interfacial bonding quality of each layer in real time on the production line to avoid defects such as delamination, bubbles, and impurities. Traditional fixed detection cannot meet the requirements of continuous production, and moving detection technology synchronizes with the production line to achieve real-time feedback.

[0004] The thickness measurement methods of plastic films mainly include ultrasonic thickness measurement, ray thickness measurement, eddy current thickness measurement, magnetic thickness measurement, and infrared spectroscopy thickness measurement. The infrared spectroscopy thickness measurement method utilizes the infrared absorption characteristics of specific wavelengths. As long as a model with sufficient accuracy is established, the thickness of each layer of a multi-component composite film can be detected non-destructively. Summary of the Invention

[0005] The purpose of the present invention is to provide a moving detection system and method for the thickness of multi-component composite plastic films based on infrared spectroscopy to meet the need for online detection of the thickness of each layer in a composite plastic film production line. By using a set of stepping motors to drive a lead screw slider to control the movement of a spectrometer, and another set of stepping motors to cooperate with rollers to control the movement of the plastic film, infrared spectra of the composite plastic film are collected during movement. Combining the successive projections algorithm (SPA) method, a relationship between the infrared spectra of the composite plastic film and the thickness of the sample components is established to achieve the moving detection of the thickness of each component of the composite plastic film.

[0006] The moving detection method for the thickness of multi-component composite plastic films based on infrared spectroscopy provided by the present invention includes the following steps:

[0007] Step 1, collect the infrared spectra of composite plastic films with different types and thicknesses;

[0008] Step 2, preprocess the collected infrared spectra;

[0009] Step 3, divide the preprocessed infrared spectra into a training set and a prediction set;

[0010] Step 4: In the full-range spectral range, the spectrum is divided into several sub-ranges, and then combined arithmetic operations are performed on the combined sub-ranges to establish partial least squares regression models for each combined range.

[0011] Step 5: By comparing the model index values calculated by the partial least squares regression models for each combined range for the training set and the prediction set, the best range combination is selected, and the partial least squares regression model of the best range combination is used as the quantitative model.

[0012] Step 6: For the infrared spectral data of the new composite plastic film to be measured, after being processed by the preprocessing method in Step 2, it is input into the quantitative model to perform thickness quantification on the composite plastic film.

[0013] In a preferred example, in Step 4, the full-range spectral range is divided into 10, 15, 20, 25, or 30 sub-ranges, and the width of each sub-range is equal.

[0014] In a preferred example, in Step 4, when performing combined arithmetic operations on the combined sub-ranges, the spectral data of each sub-range is processed independently, and the ways of combined operations include linear combination, non-linear combination, or weighted average.

[0015] In a preferred example, in Step 4, when performing combined arithmetic operations on the combined sub-ranges, the method of gradually increasing the number of sub-ranges is adopted, gradually increasing from a single sub-range to a combination of multiple sub-ranges to evaluate the influence of different combinations on the performance of the partial least squares regression model.

[0016] In a preferred example, in Step 5, the partial least squares regression model index values include the training set correlation coefficient, the prediction set correlation coefficient, the root mean square error of cross-validation of the training set, the root mean square error of the prediction set, and the relative standard deviation of the prediction set.

[0017] In a preferred example, in Step 5, when screening the best range combination, the range combination with a high prediction set correlation coefficient and a low root mean square error of the prediction set is preferably selected.

[0018] In a preferred example, in Step 6, the infrared spectral data of the new composite plastic film to be measured is obtained by a mobile acquisition method, and the mobile method is as follows: a set of stepping motors cooperate with rollers to control the composite plastic film to move vertically at a certain speed, and another set of stepping motors drive a screw slide table to control the interferometer and the detector to move horizontally at a certain speed, and the moving speed can be dynamically adjusted according to the thickness uniformity of the film.

[0019] A thickness mobile detection system for multi-component composite plastic films based on infrared spectra provided by the present invention is used to implement the above method, and includes:

[0020] Spectrometer motion control component, used to control the movement of the spectrometer;

[0021] Composite plastic film motion control component, used to control the movement of the composite plastic film;

[0022] Composite plastic film fixing component, used to fix the composite plastic film;

[0023] Infrared spectrum data acquisition and transmission system, used to acquire the infrared spectrum data of the composite plastic film and transmit the data to the host computer;

[0024] Host computer, used to receive and analyze the infrared spectrum data to detect the thickness of each component of the composite plastic film in real time.

[0025] In a preferred example, the spectrometer motion control component includes a lead screw stage and a stepper motor. The stepper motor drives the lead screw stage to control the movement of the spectrometer, so that the spectrometer can scan the composite plastic film along a preset trajectory, thereby realizing the acquisition of spectrum data at different positions of the film.

[0026] In a preferred example, the composite plastic film motion control component includes a roller and a stepper motor. The stepper motor is connected to the roller through a rigid coupling to control the movement of the composite plastic film, so that the film can move at a certain speed and direction during the detection process, matching the scanning path of the spectrometer.

[0027] In summary, the present invention includes the following beneficial technical effects:

[0028] First, by combining plastic films of different thicknesses and different types as composite plastic film samples, using a standardized method for spectral preprocessing, collecting sample spectra through an infrared spectrum system, and then establishing a combined interval partial least squares method model, a fast and high-precision method for obtaining the thickness of each component of the multi-component composite plastic film based on characteristic bands is obtained.

[0029] Second, in the present invention, the motor cooperates with the optical axis to move the composite plastic film vertically at a certain speed, and the motor drives the lead screw stage to control the interferometer and the detector to move horizontally at a certain speed, so that the spectra at different position points on the plane of the composite plastic film can be collected online, thereby reducing the influence of the thickness non-uniformity of the composite plastic film on thickness detection and meeting the on-line detection requirements for the thickness of the composite film production. Description of the Drawings

[0030] The present invention will be further described below in conjunction with the drawings and embodiments.

[0031] Figure 1 It is a schematic structural diagram of an embodiment of the present application.

[0032] Figure 2It is a schematic structural diagram of the interferometer base in the embodiment of the present application.

[0033] Figure 3 It is a schematic structural diagram of the detector base in the embodiment of the present application.

[0034] Figure 4 It is a schematic structural diagram of the sample holder base in the embodiment of the present application.

[0035] Figure 5 It is a schematic structural diagram of the sample holder in the embodiment of the present application.

[0036] Figure 6 It is a flowchart of the detection method of the thickness movement detection system of the multi-component composite plastic film based on infrared spectroscopy provided in the embodiment of the present application.

[0037] Figure 7 It is a schematic structural diagram of fixing and collecting the spectrum of the composite film in the embodiment of the present application.

[0038] Figure 8 It is a schematic structural diagram of moving and collecting the spectrum of the composite film in the embodiment of the present application. Detailed implementation manners

[0039] The following further describes the present invention in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] Embodiment 1: Refer to Figure 1 As shown, the embodiment of the present application provides a thickness movement detection system for a composite plastic film based on infrared spectroscopy, including a spectrometer motion control component, a composite plastic film motion control component, and a composite film fixing component.

[0041] The spectrometer motion control device includes a lead screw slide 1, on which an interferometer base 4, a detector base 5, and a first stepping motor 8 are installed. An infrared interferometer 6 is installed on the interferometer base 4, and an infrared detector 7 is installed on the detector base 5. The infrared interferometer 6 and the infrared detector 7 are electrically connected.

[0042] The composite plastic film motion control device includes a motor bracket 2, on which two second stepping motors 9 are installed. Rigid couplings 12 are connected to the magnetic poles of the two second stepping motors 9. The other end of the rigid coupling 12 is connected to a roller 11, and the other end of the roller 11 is connected to a bearing seat 10 for fixing the roller 11 installed on a bearing bracket 3. The composite plastic film 13 is adhesively fixed on the two rollers 11, and the rotation of the roller 11 drives the composite plastic film 13 to move up and down.

[0043] The composite plastic film fixing component includes a sample holder base 14 and a sample holder 15 on the base.

[0044] Further, in this embodiment, the motor bracket 2 is composed of three layers, and each layer is a cuboid structure without side walls. Four straight holes are provided in the first-layer base for mounting the motor bracket 2 on the optical platform, and four straight holes are provided in the second and third layers for mounting the second stepping motor 9. The structure of the bearing bracket 3 is similar to that of the motor bracket 2, that is, a cuboid without side walls in three layers. Six straight holes are provided in the first-layer base of the bearing bracket 3 for mounting the bearing bracket 3 on the optical platform, and four straight holes suitable for mounting the bearing seat 10 are provided in the second and third layers, and a round hole is cut off at the rear wall. When the bearing seat 10 is fixed outside the bearing bracket, this round hole facilitates the installation of the bearing seat 10. Each layer of the motor bracket 2 and the bearing bracket 3 has the same height, and the motor shaft bearing seats 10 are at the same height, so that the heights at both ends of the roller are the same, and the two rollers 11 are parallel to the ground.

[0045] Refer to Figure 2 and Figure 3 As shown, the interferometer base 4 includes a base with a certain thickness for supporting the infrared interferometer and a baffle 41 provided on the base for fixing the infrared interferometer 6. Four through holes 42 for connecting the interferometer are provided on the base to further fix the infrared interferometer 6. Two countersunk holes are provided on the interferometer base for connecting the lead screw slide table. The detector base 5 includes a base with a certain thickness for supporting the infrared detector. Two pedestals 51 with the same height are provided on the pedestal. The same grooves 52 are provided on the two bases for fixing the infrared detector 7. Two pedestals with the same height are provided on the detector base 5, so that the light outlet of the infrared interferometer 6 and the receiving port of the infrared detector 7 are at the same height.

[0046] Refer to Figure 4 and Figure 5 As shown, through holes 141 are provided at the bottom of the base 14 for mounting on the lead screw slide table, and a card slot 142 is provided above for fixing the sample holder 15. The bottom of the sample holder 15 includes a frame body 151 and a frame cover 152. Four threaded holes are provided on the frame body 151, and four through holes are provided on the frame cover 152. The composite plastic film 16 is sandwiched between the frame body 151 and the frame cover 152. A round hole 153 is provided in the middle of both the frame body 151 and the frame cover 152, and the infrared light transmits through the sample through the round hole 153.

[0047] Further, the detection system further includes an infrared spectrum data acquisition and transmission system and a host computer. The infrared spectrum data acquisition and transmission system is used to collect the infrared spectrum data of the composite plastic film, send the data to the host computer, and the host computer analyzes the infrared spectrum data of the composite film to detect the thickness of each component of the composite film in real time.

[0048] Refer toFigure 6 As shown in the figure, the method for detecting the thickness shift of a composite plastic film based on infrared spectroscopy provided by the embodiment of the present application includes the following steps:

[0049] S1: Collect the infrared spectra of composite plastic films of different types and different thicknesses

[0050] In this embodiment, five types of films, namely PE, PP, PC, PVC, and PET, are used for combination. As Figure 7 shown, place the composite plastic film sample in the sample holder, connect the body and the cover of the holder with screws and fix them with nuts, so that the composite plastic film is fixed in the sample holder, and the infrared spectrometer scans the sample in transmission. To ensure the spectral resolution accuracy, set the system resolution to 4 cm -1 , scan 40 spectra for each sample, and take the average as the final sample spectrum. Use different types of films with known thicknesses for combination, and finally obtain 1050 infrared spectrum samples of composite films.

[0051] S2: Preprocess the infrared spectra of the samples

[0052] In this embodiment, standardization is used to preprocess the collected infrared spectrum data. The specific implementation method of standardization is as follows:

[0053] Calculate the mean value of the spectral data of each sample: ;

[0054] Calculate the standard deviation of the spectral data of each sample: ;

[0055] Subtract the mean value of the spectral data of each row from each spectral data and then divide by the standard deviation: ;

[0056] Among them, is the k-th spectral data of, and m is the number of wavelength points.

[0057] S3: Divide the preprocessed infrared data into data sets

[0058] Adopt the Kennard-Stone algorithm to divide the preprocessed infrared spectra of plastic films into a training set and a prediction set at a ratio of 4:1. Among them, the training set contains 840 samples, and the prediction set contains 210 samples. Use the training set data to establish a model, and the test set is used to verify the performance of the model.

[0059] S4: Establish a synergy interval partial least squares regression (Synergy interval PLS, SiPLS) model, and obtain a quantitative model based on the model index values.

[0060] The specific steps are as follows:

[0061] S4-1: In the full spectral range, it is divided into 10, 15, 20, 25, and 30 subintervals respectively. Then, when divided into subintervals of the same width, 2, 3, and 4 subintervals are combined for calculation respectively to establish the PLS regression model for each combined interval.

[0062] For example, in the full spectral range of 834 - 5000 cm -1 When divided into 10 subintervals, the subintervals 1 to 10 are 834 - 1251 cm -1 , 1251 - 1667 cm -1 , 1667 - 2084 cm -1 , 2084 - 2500 cm -1 , 2500 - 2917 cm -1 , 2917 - 3333 cm -1 , 3333 - 3750 cm -1 , 3750 - 4167 cm -1 , 4167 - 4583 cm -1 , 4573 - 5000 cm -1 , and the PLS regression model for each combined interval is established by combining 2, 3, and 4 of these subintervals.

[0063] Partial least squares regression (PLS) uses the score matrices of different principal components to establish the relationship between the spectral matrix and the component thickness matrix. The simple principle of the algorithm is as follows:

[0064] First, perform orthogonal decomposition on the spectral matrix A and the component thickness matrix C:

[0065]

[0066]

[0067] T and U are the score matrices of the X and Y matrices respectively, P and Q are the loading matrices of the X and Y matrices respectively, and E X and E Y are the PLS fitting residual matrices. Then, perform linear regression on T and U:

[0068]

[0069]

[0070] During prediction, first calculate the score T un of the spectral matrix A un of the unknown sample according to P, and obtain the predicted value of the component thickness from the following formula:

[0071]

[0072] S4-2: By comparing the model metric values calculated by the PLS models of each combined interval for the training set and the prediction set, the best interval combination is selected, and the partial least squares regression model of the best interval combination is used as the quantitative model. The model metrics are as follows:

[0073] Coefficient of determination of the training set ;

[0074] Coefficient of determination of the prediction set ;

[0075] Root mean square error of cross-validation of the training set ;

[0076] Root mean square error of the prediction set ;

[0077] Relative standard deviation of the prediction set ;

[0078] where n is the number of samples in the training set, is the spectral value of the i-th sample, is the reference value of the i-th sample; m is the number of samples in the prediction set, is the spectral value of the j-th sample, is the reference value of the j-th sample, is the predicted value of the i-th sample, is the predicted value of the j-th sample, is the average value of the predicted values of all samples in the prediction set;

[0079] The closer the coefficients of determination Rc and Rp are to 1, the stronger the model's ability to explain the data. The smaller the values of the root mean square errors RMSECV and RMSEP, the higher the model fitting accuracy.

[0080] Screen the segmentation intervals, select the best interval combination as the interval combination of the final quantitative model, and use the PLS model of the best interval combination as the final SiPLS model, that is, the quantitative model.

[0081] S5: Measure the thickness of the moving detection composite film

[0082] For the new composite plastic film with the thickness to be measured, such as Figure 8As shown, when the infrared spectrometer moves to collect the spectrum of the composite plastic film, the sample holder and the sample holder base are removed from the lead screw slide table, and the composite plastic film is pasted and fixed on the roller. The infrared spectrum mobile detection system transmits and scans the composite plastic film. The moving method is that a group of stepper motors cooperate with the roller to control the composite plastic film to move up and down at a certain speed, and another group of stepper motors drive the lead screw slide table to control the interferometer and the detector to move left and right at a certain speed, so that the infrared spectrometer moves along a specified trajectory to collect the spectrum of the composite plastic film. After the obtained spectral data is processed by the method of S2, it is input into the finally obtained SiPLS model to quantitatively determine the thickness of the composite plastic film.

[0083] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for thickness shift detection of a multi-component composite plastic film based on infrared spectroscopy, characterized in that It includes the following steps: Step 1: Collect the infrared spectra of composite plastic films of different types and thicknesses; Step 2: Preprocess the collected infrared spectra; Step 3: Divide the preprocessed infrared spectra into a training set and a prediction set; Step 4: In the full-range spectral range, divide the spectrum into several sub-ranges, and then perform combined operations on the combined sub-ranges to establish a partial least squares regression model for each combined range; Step 5: By comparing the model index values calculated by the partial least squares regression models of each combined range for the training set and the prediction set, screen the best range combination, and use the partial least squares regression model of the best range combination as the quantitative model; Step 6: For the infrared spectral data of the new composite plastic film to be measured, after being processed by the preprocessing method in Step 2, input it into the quantitative model to perform thickness quantification on the composite plastic film.

2. The thickness shift detection method for the multi-component composite plastic film based on infrared spectroscopy according to claim 1, wherein, In Step 4, the full-range spectral range is divided into 10, 15, 20, 25, or 30 sub-ranges, and the width of each sub-range is equal.

3. The thickness shift detection method for the multi-component composite plastic film based on infrared spectroscopy according to claim 2, wherein In Step 4, when performing combined operations on the combined sub-ranges, the spectral data of each sub-range is independently processed, and the combined operation methods include linear combination, non-linear combination, or weighted average.

4. The thickness shift detection method of the multi-component composite plastic film based on infrared spectrum according to claim 2 or 3, characterized in that, In Step 4, when performing combined operations on the combined sub-ranges, the method of gradually increasing the number of sub-ranges is adopted, gradually increasing from a single sub-range to a combination of multiple sub-ranges to evaluate the influence of different combinations on the performance of the partial least squares regression model.

5. The thickness shift detection method for the multi-component composite plastic film based on infrared spectrum according to claim 1, characterized in that In Step 5, the partial least squares regression model index values include the training set correlation coefficient, the prediction set correlation coefficient, the root mean square error of cross-validation of the training set, the root mean square error of the prediction set, and the relative standard deviation of the prediction set.

6. The thickness shift detection method for the multi-component composite plastic film based on infrared spectroscopy according to claim 5, wherein, In Step 5, when screening the best range combination, preferentially select the range combination with a high prediction set correlation coefficient and a low root mean square error of the prediction set.

7. The thickness shift detection method for the multi-component composite plastic film based on infrared spectroscopy according to claim 1, wherein In Step 6, the infrared spectral data of the new composite plastic film to be measured is obtained by a moving acquisition method. The moving method is as follows: a set of stepping motors cooperate with rollers to control the composite plastic film to move at a certain speed, and another set of stepping motors drive the screw slide to control the interferometer and the detector to move at a certain speed, and the moving speed can be dynamically adjusted according to the thickness uniformity of the film.

8. A thickness shift detection system for a multi-component composite plastic film based on infrared spectroscopy, which is used to implement the method according to any one of claims 1-7, characterized in that, It includes: A spectrometer motion control component for controlling the movement of the spectrometer; A composite plastic film motion control component for controlling the movement of the composite plastic film; A composite plastic film fixing component for fixing the composite plastic film; An infrared spectral data acquisition and transmission system for acquiring the infrared spectral data of the composite plastic film and transmitting the data to the upper computer; An upper computer for receiving and analyzing the infrared spectral data to detect the thickness of each component of the composite plastic film in real time.

9. The thickness shift detection system for multi-component composite plastic films based on infrared spectroscopy according to claim 8, wherein The spectrometer motion control component includes a screw slide and a stepping motor. The stepping motor drives the screw slide to control the movement of the spectrometer, so that the spectrometer can scan the composite plastic film along a preset trajectory, thereby realizing the acquisition of spectral data at different positions of the film.

10. The thickness shift detection system for a multi-component composite plastic film based on infrared spectroscopy according to claim 9, characterized in that, The composite plastic film movement control component includes a roller and a stepper motor. The stepper motor is connected to the roller through a rigid coupling to control the movement of the composite plastic film, so that the film can move at a certain speed and direction during the detection process, matching the scanning path of the spectrometer.

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