Automatic production process for zip-top can top cover

Abstract

The invention discloses an automatic production process for a top cover of a zip-top can, and relates to the technical field of zip-top can processing. The method at least comprises the steps that S1, raw material preparation is conducted, specifically, raw material selection and coating preparation are conducted, an aluminum alloy or a steel plate is adopted as the raw material of the pop can top cover, and the thickness ranges from 0.2 mm to 0.3 mm; s2, feeding is controlled by adopting a feeding algorithm; and S3, punch forming is conducted through a punching machine, punch forming comprises top cover disc punching and edge trimming, according to top cover disc punching, a metal plate is punched into a round top cover through a disc die, it is ensured that the size and shape meet the requirements, and according to edge trimming, burrs are removed through a trimming die, and smoothness is ensured. By introducing the advanced automatic production process, the intelligent control feeding method and the coating and quality detection technology, the production efficiency can be effectively improved, the product quality consistency is guaranteed, the production cost is reduced, and the flexibility and the intelligent degree of the production line are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of beverage can processing, and particularly to an automated production process for beverage can lids. Background Art

[0002] The beverage can lid is one of the crucial components in the beverage packaging industry, and its quality and production efficiency directly affect the safety and market competitiveness of products. With the continuous development of industrial automation technology, traditional manual or semi-automated production processes have gradually been replaced by more efficient and precise fully automated production lines. Currently, the process of producing beverage can lids generally includes multiple steps such as stamping, stretching and forming of metal sheets, coating treatment, installation of pull tabs, and quality inspection. Among these steps, especially key processes such as metal forming, coating application, automatic feeding, and quality inspection play important roles in improving production efficiency, reducing costs, and ensuring product consistency.

[0003] With the increasing market demand for high-quality beverage can lids, the traditional production process faces the following technical bottlenecks:

[0004] Bottleneck in production efficiency: Although existing automated production lines have improved production efficiency to a certain extent, there is still significant room for improvement in the automation level and equipment performance when facing large-scale and rapid production requirements, especially in the lack of corresponding designs for algorithms such as feeding control.

[0005] Insufficient coating control: Although the coating process is crucial for improving the corrosion resistance of beverage can lids, existing coating formulations and spraying techniques are still difficult to ensure the uniformity and adhesion of the coating during high-efficiency production.

[0006] Control of stretching and forming force: Although existing stretching and forming equipment can achieve a certain degree of precision control, it has poor adaptability to complex shape changes and dynamic stresses, and is prone to material waste or defective products during production, lacking corresponding control algorithms.

[0007] Therefore, a new solution to the above problems is needed. Summary of the Invention

[0008] The purpose of the present invention is to provide an automated production process for beverage can lids to solve the technical problems raised in the background art.

[0009] To achieve the above purpose, the present invention provides the following technical solution: An automated production process for beverage can lids, which at least includes the following steps:

[0010] S1: Preparation of raw materials, including raw material selection and coating preparation, using aluminum alloy or steel plate as the raw material for beverage can lids, with a thickness between 0.2 mm and 0.3 mm;

[0011] S2: Control the feeding using the feeding algorithm;

[0012] S3: Stamping forming is carried out using a stamping machine. The stamping forming includes top cover disc stamping and edge trimming. The top cover disc stamping is to stamp a metal plate into a circular top cover through a disc mold to ensure that the size and shape meet the requirements. The edge trimming is to remove burrs through a trimming mold to ensure smoothness.

[0013] S4: Drawing forming is controlled by the corresponding drawing control algorithm. The center part of the top cover is drawn to form a concave shape for subsequent opening and sealing functions. A high-precision drawing mold is used and completed through mechanical drawing action. At the same time, depth detection is carried out, and an automated sensor is used to detect the depth and shape of the top cover to ensure compliance with the design standards;

[0014] S5: Flanging and forming. The outer edge of the top cover needs to be flanged multiple times to make its edge tighter and form an annular structure convenient for sealing;

[0015] S6: Coat the processed and formed top cover of the beverage can. During the spraying process, the spraying equipment is controlled by the spraying algorithm, and the coating processing is detected in cooperation with the detection equipment and using the corresponding coating detection algorithm. When the detection is unqualified, the coating is re-sprayed through the re-spraying equipment in cooperation with the subsequent re-spraying control algorithm;

[0016] S7: Install the pull tab. Use automated equipment to fix the pull tab to the center position of the top cover to ensure the tightness and position accuracy of the pull tab;

[0017] S8: Detection and quality control;

[0018] S9: Collect the materials. Convey them to the position of the automatic bagging device through a conveyor belt, and pack and collect the cover bodies.

[0019] Further, the coating preparation in S1 includes preparing the coating material, ratio regulation, and adding solvents;

[0020] The preparation of the coating material includes one of epoxy resin, epoxy ester, or thermosetting paint;

[0021] The ratio regulation is as follows:

[0022] Resin: curing agent = 1:0.8 (by weight).

[0023] The addition of solvents includes one of cyclohexane, alcohol solvents, or ether solvents, and the proportion of the added solvents is adjusted according to the specific required viscosity.

[0024] Further, the feeding algorithm in S2 at least includes the following steps:

[0025] Initialize the system and initialize the components, where the components at least include sensors, motors, and bins;

[0026] Detect the status of the aluminum can, that is, whether there is an aluminum can currently can be represented by a boolean variable:

[0027]

[0028] Detect the bin status. The status of the bin is represented by the number of aluminum can raw materials in the current bin. Set the number of aluminum cans in the current bin to N warelouse ;

[0029] N warehouse = checkwarehouse level()

[0030] Feeding device control condition. Determine whether to start the feeding device according to the current bin status and the aluminum can detection status. Set the thresholds to L theshold and H threshold , which are the low threshold and high threshold of the bin respectively;

[0031] Start the feeding device. If the detected numerical quantity in the bin is lower than the set low threshold L threshold , and no aluminum can is detected currently, that is, is_canpresent = 0, then start the feeding device;

[0032] Feeding device start condition: (is_can_present = 0) ∧ (N warehouse < L tlmeshold )

[0033] Stop the feeding device. If the number of aluminum cans in the bin exceeds the high threshold H threshold , then stop the feeding device;

[0034] Feeding device stop condition: N warehouse ≥ H threslold

[0035] Perform anomaly detection. Anomaly detection is used to detect whether the sensor is working properly. Set the sensor array failure status to E sensor , where it is 1 when there is a failure and 0 when it is normal;

[0036]

[0037] If a sensor failure is detected, that is, E sensor = 1, then trigger an alarm;

[0038] System status monitoring. The action of monitoring the system status is represented by a status variable S system to indicate whether the system is working properly. Assume Ssystem 1 indicates that the system is normal, and 0 indicates that the system is abnormal;

[0039]

[0040] Set the control logic in the main loop:

[0041] If there is no aluminum can currently and the silo is below the low threshold, start the feeding motor;

[0042]

[0043] If the number of aluminum cans in the silo is greater than or equal to the high threshold, stop the feeding motor:

[0044]

[0045] If the sensor fails, trigger an alarm:

[0046]

[0047] System status monitoring: If the system is abnormal, set S system = 0;

[0048]

[0049] And further introduce a delay time Δt (unit: second), so that the system pauses for a period of time after each cycle to avoid excessive CPU occupancy.

[0050] Furthermore, the stretching control algorithm in S4 at least includes the following steps:

[0051] Define parameters: σ: stress of the material (unit: Pa); ε: strain of the material; ε pl : plastic strain; T: temperature (unit: K); strain rate; K: strength coefficient of the material; n: strain hardening index; σ 0 : yield stress of the material; σ t : instantaneous stress during the stretching process;

[0052] Set the material rheological model:

[0053] Use the Hollomon model to represent the stress-strain relationship of the material:

[0054] σ = Kε n

[0055] During the forming process, the rate of change of strain with time, that is, the strain rate, is a key factor. Assume that the strain rate and stress are related, and control the stress through the strain rate:

[0056]

[0057] Among them, A is a constant related to the material properties, and m is the exponent between the strain rate and the stress;

[0058] Considering the influence of temperature on the material properties, the material will be affected by temperature during the tensile process, especially during high-temperature tensile. The influence of temperature is considered through the Arrhenius equation:

[0059]

[0060] A control algorithm is proposed. Among them, Q is the activation energy, R is the gas constant, and T is the temperature. The control algorithm includes strain control and temperature control to ensure that the material will not undergo excessive deformation or rupture during the tensile forming process;

[0061] Calculate the current stress, and calculate the current stress from the strain and strain rate during the tensile process:

[0062] σ = Kε n

[0063] Calculate the correction of the stress by temperature, and correct the stress of the material through the influence of temperature:

[0064]

[0065] Control the strain rate, according to the target strain rate to adjust the tensile speed so that the material is formed within an acceptable range:

[0066]

[0067] Compare the strain with the yield stress to judge whether it exceeds the yield stress. If the current stress σ exceeds the yield stress σ of the material 0 , then the process parameters need to be adjusted. The process parameters at least include temperature and speed to avoid rupture. If σ > σ 0 , then reduce the strain rate or lower the temperature;

[0068] Real-time feedback control

[0069]

[0070] According to the data fed back by the sensor, the data at least includes stress, strain and temperature, and adjust the tensile speed and heating temperature of the tensile machine in real time.

[0071] Furthermore, the spraying algorithm at least includes the following steps:

[0072] Define the parameters: T spray : spraying time (unit: second); ρ spray: Spraying pressure (unit: Pa); V spray : Spraying speed (unit: m / s); D coating : Coating thickness (unit: μm); T curing : Curing temperature (unit: °C); t curing : Curing time (unit: minutes); C adhesion : Coating adhesion (unit: N / mm2)

[0073] For the target control parameters, the control objectives are: the uniformity and thickness D of the coating coating ; the adhesion C of the coating adhesion ; the curing effect of the coating material, i.e., the curing temperature and time of the coating material;

[0074] Coating thickness control: The coating thickness is determined by the spraying time, spraying pressure, and spraying speed. The formula for controlling the coating thickness can be expressed by the following relationship:

[0075]

[0076] Adjustment method:

[0077] where k 1 is a constant related to the spraying equipment and the characteristics of the coating material. To ensure that the coating thickness is within the appropriate range, the goal is to make D coating within the set range. If the actual coating thickness D coating is less than the target value D target , then the spraying time T spray can be increased or the spraying pressure P spray can be increased, or the spraying speed V spray can be decreased;

[0078] If the actual coating thickness D coating is greater than the target value D target , then the spraying time T spray can be decreased or the spraying pressure P spray can be decreased, or the spraying speed V spray can be increased;

[0079] Coating uniformity control: The uniformity of the coating is usually achieved by controlling the state of the spraying equipment and the spraying path. Assuming that the spraying uniformity can be measured by the spraying density of the coating material, it is defined as:

[0080]

[0081] Increasing the spraying pressure P spray or decreasing the spraying speed V spray will increase the coating uniformity;

[0082] Coating adhesion control: The adhesion C of the coatingarhossion Mainly affected by the curing process, during which temperature and time are crucial for the hardness and adhesion of the coating. Curing process control:

[0083] The temperature T of the curing process curing and the time t curing need to be adjusted according to the characteristics of the coating;

[0084] Assuming that the adhesion is related to the curing time and curing temperature, the simplified relationship can be expressed as:

[0085]

[0086] where k 2 is a constant related to the coating type and material properties;

[0087] If the adhesion C of the coating adhesion is lower than the set value, the curing temperature T can be increased curing or the curing time t can be extended curing , but too high a curing temperature will cause over-hardening or blistering of the coating, so it is necessary to ensure that the curing conditions are within a safe range.

[0088] Furthermore, the coating detection algorithm at least includes the following steps: coating thickness detection and coating uniformity detection;

[0089] The coating thickness detection at least includes the following steps:

[0090] Collect coating thickness data, and collect the surface data D of the coating through a laser measurement sensor measured ;

[0091] Calculate the thickness error, compare it with the target coating thickness D target , and calculate the error:

[0092] ΔD = D measured - D target

[0093] Set the threshold: set the maximum allowable error range ΔD max ;

[0094] If |ΔD| ≤ ΔD max , it is considered that the coating thickness is qualified;

[0095] If |ΔD| > ΔD max , it means that there is a problem with the coating, and enter the subsequent supplementary spraying control;

[0096] Coating uniformity detection, obtain coating image data: use a camera or scanning device to obtain the image data I of the coating surface image ;

[0097] Image processing, using image processing algorithms to extract the thickness distribution map I of the coating thickness ;

[0098] Calculate the uniformity index, calculate the standard deviation or coefficient of variation of the image to evaluate the uniformity of the coating:

[0099]

[0100] where σ thickness is the standard deviation of the coating thickness, and μ thickness is the mean value of the coating thickness;

[0101] Set the uniformity threshold. According to the set uniformity standard, judge whether the uniformity of the coating is qualified. If the coefficient of variation CV of the coating exceeds the threshold, it means the coating is non-uniform and re-spraying is required.

[0102] Furthermore, the re-spraying control algorithm at least includes the following steps:

[0103] Determine the triggering conditions for re-spraying:

[0104] Coating thickness deviation, when the coating thickness D measured exceeds the allowable error ΔD target of the target value D max ;

[0105] Coating uniformity does not meet the standard, when the coefficient of variation CV of the coating exceeds the preset threshold;

[0106] Adhesion is insufficient, when the adhesion C adhesion of the coating is lower than the minimum requirement;

[0107] Locate the defective area:

[0108] Use the non-uniform area or the area with insufficient coating thickness obtained by the coating detection algorithm to determine the specific area that needs to be re-sprayed through the vision system or thickness sensor;

[0109] Adjust the spraying parameters:

[0110] Adjust the spraying time, increase the spraying time T spray ;

[0111] Adjust the spraying pressure, appropriately increase the spraying pressure P spray , to improve the adhesion and thickness of the coating;

[0112] Adjust the spraying speed, reduce the spraying speed V spray , increase the residence time of the coating material, so as to form a thicker coating in some local areas;

[0113] Control the re-spraying amount:

[0114] According to the detected coating deviation or defect, control the amount of paint to be re-sprayed. Assume the coating thickness deviation is ΔD, and the amount of coating to be re-sprayed is ΔV spray It can be calculated by the following formula:

[0115]

[0116] where k 1 is a constant related to the coating spraying process;

[0117] After the re-spraying, conduct detection. After the re-spraying operation is completed, use the coating detection algorithm again to detect the coating thickness and uniformity;

[0118] If the detection result meets the standard, terminate the re-spraying; if it still does not meet the requirements, continue the re-spraying or adjust the spraying parameters.

[0119] Furthermore, the S8 at least includes the following steps:

[0120] Dimension detection, use high-precision laser or image recognition technology to automatically check whether the diameter, depth, and edge shape dimensions of the top cover meet the specifications;

[0121] Appearance inspection, use the vision detection system to automatically check whether there are defects, scratches, or depressions on the surface of the top cover;

[0122] Tensile test, test the tensile force between the pull tab and the top cover to ensure that it will not break or slip off during opening.

[0123] Compared with the prior art, the beneficial effects of the present invention are:

[0124] By introducing advanced automated production processes, intelligent control of the feeding method, coating, and quality detection technologies, the present invention can effectively improve production efficiency, ensure product quality consistency, reduce production costs, and enhance the flexibility and intelligence of the production line. Brief Description of the Drawings

[0125] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0126] Figure 1 It is a schematic flow chart of the overall process of the present invention. Detailed Embodiments

[0127] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0128] Please refer to Figure 1 , an automated production process for the top cover of an aluminum can, at least including the following steps:

[0129] S1: Raw material preparation, including raw material selection and coating preparation. Aluminum alloy or steel plate is used as the raw material for the top cover of the aluminum can, with a thickness between 0.2 mm and 0.3 mm;

[0130] S2: Use the loading algorithm to control the loading;

[0131] S3: Stamping and forming, carried out by a stamping machine. Stamping and forming includes stamping the top cover disc and edge trimming. Stamping the top cover disc is to stamp the metal plate into a circular top cover through a disc mold to ensure that the size and shape meet the requirements. Edge trimming is to remove burrs through a trimming mold to ensure smoothness.

[0132] S4: Drawing and forming, controlled by the corresponding drawing control algorithm. The central part of the top cover is drawn to form a concave shape for subsequent opening and sealing functions. A high-precision drawing mold is used and completed through mechanical drawing. At the same time, depth detection is carried out. An automated sensor is used to detect the depth and shape of the top cover to ensure compliance with the design standards;

[0133] S5: Beading and forming. The outer edge of the top cover needs to be beaded multiple times to make its edge tighter and form an annular structure convenient for sealing;

[0134] S6: Coat the processed and formed top cover of the aluminum can. During the spraying process, the spraying equipment is controlled by the spraying algorithm, and the coating processing is detected in cooperation with the detection equipment and the corresponding coating detection algorithm. When the detection is unqualified, the coating is replenished by the replenishing spraying equipment in cooperation with the subsequent replenishing spraying control algorithm;

[0135] S7: Install the pull tab. Use an automated device to fix the pull tab to the center position of the top cover to ensure the tightness and position accuracy of the pull tab;

[0136] S8: Detection and quality control;

[0137] S9: Collect the materials. Transmit them to the position of the automatic bagging device through a conveyor belt, and pack and collect the cover bodies.

[0138] The coating preparation in S1 includes preparing coating materials, regulating the ratio, and adding solvents;

[0139] Preparing the coating materials includes one of epoxy resin, epoxy ester, or thermosetting paint;

[0140] The mixing ratio is adjusted as follows:

[0141] Resin: curing agent = 1:0.8 (by weight).

[0142] Add a solvent including any one of cyclohexane, alcohol solvents or ether solvents, and adjust the proportion of the added solvent according to the specific required viscosity.

[0143] The feeding algorithm in S2 includes at least the following steps:

[0144] Initialize the system and components. The components include at least sensors, motors and bins;

[0145] Detect the status of the cans. Whether there are cans currently can be represented by a boolean variable:

[0146]

[0147] Detect the status of the bin. The status of the bin is represented by the number of can raw materials in the current bin. Set the number of cans in the current bin to N warelouse ;

[0148] N warehouse = checkwarehouse level()

[0149] The control condition of the feeding device is to decide whether to start the feeding device according to the current bin status and the can detection status. Set the thresholds to L theshold and H threshold , which are the low threshold and high threshold of the bin respectively;

[0150] Start the feeding device. If the detected number of cans in the bin is lower than the set low threshold L threshold , and no cans are detected currently, that is, is_canpresent = 0, then start the feeding device;

[0151] Condition for starting the feeding device: (is_can_present = 0) ∧ (N warehouse < L tlmeshold )

[0152] Stop the feeding device. If the number of cans in the bin exceeds the high threshold H threshold , then stop the feeding device;

[0153] Condition for stopping the feeding device: N warehouse ≥ H threslold

[0154] Perform anomaly detection. Anomaly detection is used to detect whether the sensors are working properly. Set the sensor array failure status to Esensor , which is 1 in case of failure and 0 in normal condition;

[0155]

[0156] If a sensor failure is detected, i.e., E sensor = 1, an alarm is triggered;

[0157] System status monitoring. The action of monitoring the system status is represented by a status variable S system to indicate whether the system is working properly. Assume that S system being 1 indicates that the system is normal and 0 indicates that the system is abnormal;

[0158]

[0159] Set the control logic in the main loop:

[0160] If there is no current aluminum can and the silo is below the low threshold, start the feeding motor;

[0161] Start the motor:

[0162] If the number of aluminum cans in the silo is greater than or equal to the high threshold, stop the feeding motor:

[0163]

[0164] If the sensor fails, an alarm is triggered:

[0165]

[0166] System status monitoring: If the system is abnormal, set S system = 0;

[0167]

[0168] And further introduce a delay time Δt (unit: second) to pause for a period of time after each cycle to avoid excessive CPU occupancy.

[0169] The stretching control algorithm in S4 includes at least the following steps:

[0170] Define parameters: σ: stress of the material (unit: Pa); ε: strain of the material; ε pl : plastic strain; T: temperature (unit: K); Strain rate; K: strength coefficient of the material; n: strain hardening index; σ 0 : yield stress of the material; σ t : instantaneous stress during the stretching process;

[0171] Set the material rheological model:

[0172] Use the Hollomon model to represent the stress-strain relationship of the material:

[0173] σ = Kε n

[0174] During the forming process, the rate of change of strain with time, i.e., the strain rate, is a key factor. Assuming that the strain rate and stress are related, control the stress through the strain rate:

[0175]

[0176] where A is a constant related to the material properties and m is the exponent between the strain rate and stress;

[0177] Considering the influence of temperature on the material properties, the material will be affected by temperature during the tensile process, especially during high-temperature tensile. Consider the influence of temperature through the Arrhenius equation:

[0178]

[0179] Propose a control algorithm. Among them, Q is the activation energy, R is the gas constant, T is the temperature. The control algorithm includes strain control and temperature control to ensure that the material will not undergo excessive deformation or rupture during the tensile forming process;

[0180] Calculate the current stress, calculate the current stress from the strain and strain rate during the tensile process:

[0181] σ = Kε n

[0182] Calculate the correction of stress by temperature, correct the stress of the material through the influence of temperature:

[0183]

[0184] Control the strain rate, according to the target strain rate to adjust the tensile speed so that the material is formed within an acceptable range:

[0185]

[0186] Compare the strain with the yield stress to judge whether it exceeds the yield stress. If the current stress σ exceeds the yield stress σ of the material 0 , then it is necessary to adjust the process parameters. The process parameters at least include temperature and speed to avoid rupture. If σ > σ 0 , then reduce the strain rate or lower the temperature;

[0187] Real-time feedback control

[0188]

[0189] According to the data fed back by the sensor, the data includes at least stress, strain and temperature, and the stretching speed and heating temperature of the stretching machine are adjusted in real time.

[0190] The spraying algorithm includes at least the following steps:

[0191] Define parameters: T spray : spraying time (unit: second); ρ spray : spraying pressure (unit: pa); V spray : spraying speed (unit: m / s); D coating : coating thickness (unit: μm); T curing : curing temperature (unit: °C); t curing : curing time (unit: minute); C adhesion : coating adhesion (unit: N / mm2)

[0192] For the target control parameters, the control objectives are: the uniformity and thickness D of the coating coating ; the adhesion C of the coating adhesion ; the curing effect of the coating material, that is, the curing temperature and time of the coating material;

[0193] Coating thickness control, the coating thickness is determined by the spraying time, spraying pressure and spraying speed, and the formula for controlling the coating thickness can be expressed by the following relationship:

[0194]

[0195] Adjustment method:

[0196] Among them, k 1 is a constant related to the spraying equipment and the characteristics of the coating material. In order to ensure that the coating thickness is within a suitable range, the goal is to make D coating within the set range. If the actual coating thickness D coating is less than the target value D target , then the spraying time T spray can be increased or the spraying pressure P spray can be increased, or the spraying speed V spray can be decreased;

[0197] If the actual coating thickness D coating is greater than the target value D target , then the spraying time T spray can be reduced or the spraying pressure P spray can be reduced, or the spraying speed V spray can be increased;

[0198] Coating uniformity control. The uniformity of the coating is usually achieved by controlling the state of the spraying equipment and the spraying path. Assuming that the uniformity of spraying can be measured by the spraying density of the coating material, it is defined as:

[0199]

[0200] Increasing the spraying pressure P spray or decreasing the spraying speed V spray will increase the uniformity of the coating;

[0201] Coating adhesion control. The adhesion C of the coating arhossion is mainly affected by the curing process. During the curing process, temperature and time are crucial for the hardness and adhesion of the coating. Curing process control:

[0202] The temperature T of the curing process curing and time t curing need to be adjusted according to the characteristics of the coating material;

[0203] Assuming that the adhesion is related to the curing time and curing temperature, the simplified relationship can be expressed as:

[0204]

[0205] where k 2 is a constant related to the coating type and material characteristics;

[0206] If the adhesion C of the coating adhesion is lower than the set value, the curing temperature T can be increased curing or the curing time t can be extended curing , but too high a curing temperature will cause over-hardening or blistering of the coating, so it is necessary to ensure that the curing conditions are within a safe range.

[0207] The coating detection algorithm includes at least the following steps: coating thickness detection and coating uniformity detection;

[0208] Coating thickness detection includes at least the following steps:

[0209] Collecting coating thickness data, collecting the coating surface data D through a laser measurement sensor measured ;

[0210] Calculating the thickness error, comparing with the target coating thickness D target and calculating the error:

[0211] ΔD = D measured - D target

[0212] Setting the threshold: setting the maximum allowable error range ΔD max ;

[0213] If |ΔD| ≤ ΔD max , the coating thickness is considered qualified;

[0214] If |ΔD| > ΔD max , it indicates that there is a problem with the coating and enters the subsequent supplementary spraying control;

[0215] Coating uniformity detection, obtaining coating image data: Use a camera or scanning device to obtain the image data I of the coating surface image ;

[0216] Image processing, using image processing algorithms to extract the thickness distribution map I of the coating thickness ;

[0217] Calculating the uniformity index, calculating the standard deviation or coefficient of variation of the image to evaluate the uniformity of the coating:

[0218]

[0219] Among them, σ thickness is the standard deviation of the coating thickness, and μ thickness is the mean value of the coating thickness;

[0220] Setting the uniformity threshold, according to the set uniformity standard, judging whether the uniformity of the coating is qualified. If the coefficient of variation CV of the coating exceeds the threshold, it indicates that the coating is non-uniform and supplementary spraying is carried out.

[0221] The supplementary spraying control algorithm at least includes the following steps:

[0222] Determining the triggering conditions for supplementary spraying:

[0223] Coating thickness deviation, when the coating thickness D measured exceeds the allowable error ΔD target of the target value D max ;

[0224] Coating uniformity not meeting the standard, when the coefficient of variation CV of the coating exceeds the preset threshold;

[0225] Adhesion insufficient, when the adhesion C adhesion of the coating is lower than the minimum requirement;

[0226] Locating the defective area:

[0227] Using the non-uniform area or the area with insufficient coating thickness obtained by the coating detection algorithm, through the vision system or thickness sensor, determine the specific area that needs to be supplemented with spraying;

[0228] Adjusting the spraying parameters:

[0229] Adjusting the spraying time, increasing the spraying time T of the insufficient areaspray ;

[0230] Spraying pressure adjustment, appropriately increase the spraying pressure P spray , to improve the adhesion and thickness of the coating;

[0231] Spraying speed adjustment, reduce the spraying speed V spray , increase the residence time of the coating material, so as to form a thicker coating in some local areas;

[0232] Overspray amount control:

[0233] According to the detected coating deviation or defect, control the amount of coating material for overspray. Assuming the coating thickness deviation is ΔD, the overspray coating amount ΔV spray can be calculated by the following formula:

[0234]

[0235] where k 1 is a constant related to the coating spraying process;

[0236] Overspray detection. After the overspray operation is completed, use the coating detection algorithm again to detect the coating thickness and uniformity;

[0237] If the detection result meets the standard, terminate the overspray; if it still does not meet the requirements, continue the overspray or adjust the spraying parameters.

[0238] S8 includes at least the following steps:

[0239] Dimension detection, using high-precision laser or image recognition technology to automatically check whether the diameter, depth and edge shape dimensions of the top cover meet the specifications;

[0240] Appearance inspection, automatically check whether there are defects, scratches or dents on the surface of the top cover through a visual inspection system;

[0241] Tensile test, test the tensile force between the pull tab and the top cover to ensure that it will not break or slip off when opening the lid.

[0242] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. An automated production process for can top covers, characterized in that: At least the following steps are included: S1: Raw material preparation, including raw material selection and coating preparation, using aluminum alloy or steel plate as the raw material of the can top cover, with a thickness between 0.2mm and 0.3mm; S2: Use feeding algorithm to control feeding; S3: stamping, which is carried out by a stamping machine. The stamping includes top cover disc stamping and edge trimming. The top cover disc stamping is to stamp the metal plate into a circular top cover through a disc die to ensure that the size and shape meet the requirements. The edge trimming is to remove burrs through a trimming die to ensure smoothness. S4: Stretch forming: The stretch forming is controlled by the corresponding stretch control algorithm to stretch the center part of the top cover into a concave shape for subsequent opening and sealing functions. A high-precision stretching die is used to complete the process through mechanical stretching. At the same time, depth detection is performed, and an automated sensor is used to detect the depth and shape of the top cover to ensure that it meets the design standards. S5: Pressing and forming. The outer edge of the top cover needs to be pressed several times to make its edge tighter and form a ring structure that is easy to seal. S6: Spray coating on the processed can top cover. During the spraying process, the spraying algorithm is used to control the spraying equipment, and the coating processing is inspected in conjunction with the inspection equipment and the corresponding coating inspection algorithm. If the inspection fails, the coating is re-sprayed by the re-spraying equipment in conjunction with the subsequent re-spraying control algorithm. S7: Install the pull ring. Use automated equipment to fix the pull ring to the center of the top cover to ensure the tightness and position accuracy of the pull ring. S8: Testing and quality control; S9: Collect the materials, and transfer them to the automatic bagging device through the conveyor belt to pack the cover bodies.

2. The automated production process for can top covers according to claim 1, characterized in that: The coating preparation in S1 includes preparing coating materials, adjusting the proportions and adding solvents; The coating material includes one of epoxy resin, epoxy ester or thermosetting coating; The ratio control is: Resin: curing agent = 1:0.8 (weight ratio); The added solvent includes one of cyclohexane, alcohol solvent or ether solvent, and the proportion of the added solvent is adjusted according to the specific required viscosity.

3. The automated production process for can top covers according to claim 1, characterized in that: The feeding algorithm in S2 at least includes the following steps: Initializing the system, initializing the components, the components at least including sensors, motors and silos; Detecting the state of the can, that is, detecting whether there is a can at the moment, can be represented by a Boolean variable: Check the silo status. The silo status is represented by the number of cans in the current silo. Set the number of cans in the current silo to N. warelouse ; N warehouse =checkwarehouselevel() The control condition of the feeding device determines whether to start the feeding device according to the current state of the silo and the state of the pull-out detection. The threshold is set to L theshold and H threshold , respectively, the low threshold and high threshold of the silo; Start the feeding device. If the quantity in the silo is detected to be lower than the set low threshold L threshold , and no can is detected currently, that is, is_canpresent=0, then the feeding device is started; Conditions for starting the feeding device: (is_can_present=0)∧(N warehouse <L tlmeshold ) Stop the feeding device if the number of cans in the silo exceeds the high threshold H threshold , then stop the feeding device; Stop feeding device condition: N warehouse ≥H threslold Perform abnormality detection. Abnormality detection is used to detect whether the sensor is working properly. Set the sensor fault state to E sensor , where 1 is in case of failure and 0 is in case of normal condition; If a sensor failure is detected, E sensor =1, then the alarm is triggered; System status monitoring, the action of monitoring the system status uses a state variable S system To indicate whether the system is working properly, assuming S system 1 indicates that the system is normal, and 0 indicates that the system is abnormal; Set the control logic in the main loop: If there is no can currently and the silo is below the low threshold, the feeding motor is started; If the number of cans in the silo is greater than or equal to the high threshold, the feeding motor is stopped: If a sensor fails, an alarm is triggered: System status monitoring: If the system is abnormal, set S system =0; A delay time Δt (unit: second) is further introduced to pause for a while after each cycle to avoid excessive CPU usage.

4. The automated production process for can top covers according to claim 1, characterized in that: The stretching control algorithm in S4 at least includes the following steps: Definition parameters: σ: material stress (unit: Pa); ε: material strain; ε pl : plastic strain; T: temperature (unit: K); Strain rate; K: strength coefficient of the material; n: strain hardening exponent; σ0: yield stress of the material; σ t : Instantaneous stress during stretching; Set the material rheology model: The Hollomon model is used to represent the stress-strain relationship of the material: σ=Kε n In the forming process, the rate of change of strain with time, that is, the strain rate, is the key factor. Assuming that the strain rate is related to stress, the stress is controlled by the strain rate: Where A is a constant related to material properties, and m is the exponent between strain rate and stress; Considering the influence of temperature on material properties, the material will be affected by temperature during the stretching process, especially high-temperature stretching. The influence of temperature is considered through the Arrhenius equation: A control algorithm is proposed, wherein Q is activation energy, R is gas constant, and T is temperature, and the control algorithm includes strain control and temperature control to ensure that the material does not deform excessively or break during the stretch forming process; Calculate the current stress from the strain and strain rate during stretching: σ=Kε n Calculates the temperature correction for stresses to correct the material stresses by temperature effects: Control the strain rate, according to the target strain rate To adjust the stretching speed so that the material can be formed within an acceptable range: Compare the strain with the yield stress to determine whether the yield stress is exceeded. If the current stress σ exceeds the yield stress σ0 of the material, the process parameters need to be adjusted. The process parameters include at least temperature and speed to avoid rupture. If σ>σ0, reduce the strain rate or lower the temperature. Real-time feedback control Adjustment parameters: According to the data fed back by the sensor, which at least includes stress, strain and temperature, the stretching speed and heating temperature of the stretching machine are adjusted in real time.

5. The automated production process for can top covers according to claim 1, characterized in that: The spraying algorithm comprises at least the following steps: Definition parameters: T spray : Spraying time (unit: second); ρ spray : Spraying pressure (unit: pa); V spray : Spraying speed (unit: m / s); D coating : coating thickness (unit: μm); T curing : Curing temperature (unit: ℃); t curing : Curing time (unit: minutes); C adhesion :Coating adhesion (unit: N / mm2) For target control parameters, the control targets are: coating uniformity and thickness D coating ; Adhesion of coating C adhesion ; The curing effect of the coating, that is, the curing temperature and time of the coating; Coating thickness control: coating thickness is determined by spraying time, spraying pressure and spraying speed. The formula for controlling coating thickness can be expressed by the following relationship: Adjustment method: Among them, k1 is a constant related to the spraying equipment and coating characteristics. In order to ensure that the coating thickness is within the appropriate range, the goal is to make D coating Within the setting range, if the actual coating thickness D coating Less than target value D target , you can increase the spraying time T spray Or increase the spraying pressure P spray , or reduce the spraying speed V spray ; If the actual coating thickness D coating Greater than target value D target , the spraying time T can be reduced spray Or reduce the spraying pressure P spray , or increase the spraying speed V spray ; Coating uniformity control, the coating uniformity is usually achieved by controlling the state of the spraying equipment and the spraying path. It is assumed that the uniformity of the spraying can be measured by the spraying density of the coating, which is defined as: Increase the spraying pressure P spray Or reduce the spraying speed V spray It will increase the uniformity of the coating; Coating adhesion control, coating adhesion C arhossion Mainly affected by the curing process. During the curing process, temperature and time are crucial to the hardness and adhesion of the coating. The curing process control: The temperature of the curing process is T curing and time t curing Need to be adjusted according to the characteristics of the coating; Assuming that adhesion is related to curing time and curing temperature, the simplified relationship can be expressed as: Where k2 is a constant related to the coating type and material properties; If the coating adhesion C adhesion If it is lower than the set value, the curing temperature T can be increased curing Or extend the curing time t curing Too high a curing temperature can cause excessive hardening or blistering of the coating, so make sure the curing conditions are within a safe range.

6. The automated production process for can top covers according to claim 1, characterized in that: The coating detection algorithm includes at least the following steps: coating thickness detection and coating uniformity detection; The coating thickness detection comprises at least the following steps: Collect coating thickness data and coating surface data through laser measurement sensors measured ; Calculate the thickness error and the target coating thickness D target Compare and calculate the error: ΔD=D measured -D target Set threshold: Set the maximum allowable error range ΔD max ; If |ΔD|≤ΔD max , the coating thickness is considered qualified; If |ΔD|>ΔD max , it means there is a problem with the coating and the subsequent spraying control is started; Coating uniformity detection, obtaining coating image data: Use a camera or scanning device to obtain image data of the coating surface. image ; Image processing, using image processing algorithms to extract the coating thickness distribution map I thickness ; Calculate uniformity metrics and calculate the standard deviation or coefficient of variation of the image to assess the uniformity of the coating: Among them, σ thickness is the standard deviation of coating thickness, μ thickness is the mean coating thickness; Set a uniformity threshold and judge whether the uniformity of the coating is qualified according to the set uniformity standard. If the coefficient of variation CV of the coating exceeds the threshold, it means that the coating is uneven and re-spraying is required.

7. The automated production process for can top covers according to claim 1, characterized in that: The supplementary spraying control algorithm comprises at least the following steps: Determine the triggering conditions for re-spraying: Coating thickness deviation, when coating thickness D measured Exceed target value D target The allowable error ΔD max hour; The coating uniformity does not meet the standard, when the coefficient of variation CV of the coating exceeds the preset threshold; Insufficient adhesion, when the coating adhesion C adhesion When it is below the minimum requirement; Locate defective area: Use the uneven areas or areas with insufficient coating thickness obtained by the coating detection algorithm to determine the specific areas that need to be re-sprayed through the visual system or thickness sensor; Adjust spraying parameters: Adjust the spraying time and increase the spraying time T of the insufficient area spray ; Adjust the spraying pressure and increase the spraying pressure P appropriately. spray , improve the adhesion and thickness of the coating; Adjust the spraying speed, reduce the spraying speed V spray , increasing the paint residence time, thus forming a thicker coating in certain local areas; Supplementary spray volume control: According to the detected coating deviation or defect, the amount of paint sprayed is controlled. Assuming that the coating thickness deviation is ΔD, the amount of coating sprayed is ΔV. spray It can be calculated by the following formula: Among them, k1 is a constant related to the coating spraying process; After the re-spraying operation is completed, the coating thickness and uniformity are tested again using the coating detection algorithm; If the test result meets the standard, stop spraying; if it still does not meet the requirements, continue spraying or adjust the spraying parameters.

8. The automated production process for can top covers according to claim 1, characterized in that: The S8 at least comprises the following steps: Dimension detection, using high-precision laser or image recognition technology to automatically check whether the diameter, depth and edge shape dimensions of the top cover meet the specifications; Appearance inspection, using a visual inspection system to automatically check if there are any flaws, scratches or dents on the top cover surface; Tensile test: Test the tension between the pull tab and the top cover to ensure that they will not break or slip when opening the cover.

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