Dynamic feeding speed adjusting method self-adaptive to production line takt

Abstract

The invention discloses a dynamic feeding speed adjusting method self-adaptive to production line takt, and relates to the technical field of production line automation control, and the method comprises the following steps: 1, constructing a production line takt sensing system, setting sensing units at all key stations of a production line, and setting a plurality of monitoring points on a material transmission path of the production line; according to the invention, various sensing assemblies are arranged at key stations, various state information of materials in the process from station entering to transmission can be captured in real time in combination with monitoring points on a transmission path, and information reliability is ensured through data screening; therefore, the central processing unit can comprehensively master the actual operation condition of the production line, and firm data support is provided for follow-up adjustment. On the basis of characteristic parameters of multi-dimensional analysis, continuous correction is carried out in combination with historical data, and the defect that a traditional fixed model cannot adapt to beat fluctuation is avoided; in this way, the feeding speed can be adjusted on the basis of the current real production line takt all the time, and timeliness and pertinence of adjustment are ensured.

Description

Technical Field

[0001] This invention relates to the field of production line automation control technology, and in particular to a method for dynamically adjusting the feeding speed of an adaptive production line cycle. Background Technology

[0002] In existing production lines, the feeding speed is usually a pre-set fixed value, or can only be adjusted within a limited range based on a few simple parameters. This approach has obvious drawbacks: when the production line cycle time changes, the feeding speed cannot respond in a timely and accurate manner, which can easily lead to material accumulation or insufficient material supply.

[0003] For example, in an assembly line, if the operation time of a certain station is extended due to changes in product model or changes in worker skill level, the production line cycle time slows down, but the feeding speed remains at its original relatively fast speed. This will cause a large amount of material to accumulate in front of that station, not only taking up space but also potentially causing damage due to material compression. Conversely, if the production line cycle time speeds up but the feeding speed does not increase accordingly, subsequent stations will have to stop due to material shortages, seriously affecting production efficiency.

[0004] Furthermore, existing feeding speed adjustment methods are slow to respond to unexpected situations in the production line and lack flexible adaptive capabilities, making it difficult to meet the needs of efficient and stable operation of modern production lines. Summary of the Invention

[0005] The purpose of this invention is to solve the problem that the feeding speed adjustment in the prior art is not flexible enough and cannot adapt to changes in the production line cycle time, and to propose a dynamic feeding speed adjustment method that adapts to the production line cycle time.

[0006] To achieve the above objectives, the present invention employs the following method for dynamically adjusting the feeding speed of an adaptive production line cycle, comprising the following steps:

[0007] Step 1: Construct a production line cycle time sensing system. Set up sensing units at each key workstation of the production line and set up multiple monitoring points on the material transport path of the production line. Connect the sensing units and monitoring points to the central processing unit to form a cycle time sensing network covering the entire production line.

[0008] Step 2: Establish a dynamic model of the production line cycle time. The central processing unit performs multi-dimensional analysis and processing on the real-time information obtained from the sensing system, extracts feature parameters, and combines them with the historical operating data of the production line to establish a dynamic model of the production line cycle time that can be updated in real time.

[0009] Step 3: Determine the feed speed adjustment benchmark. Based on the production line cycle dynamic model, analyze the material demand and demand speed of the production line under different cycle states, determine the initial benchmark, and re-determine the adjustment benchmark when the production line cycle changes.

[0010] Step 4: Implement dynamic adjustment of feeding speed. The feeding device adjusts the speed in segments according to the adjustment instructions sent by the central processing unit, while monitoring and correcting signs of material accumulation or insufficient supply in real time.

[0011] Step 5: Construct a feedback optimization mechanism to feed back the production line operation status information after the feeding speed adjustment to the central processing unit, compare and analyze the actual and expected effects, and optimize and adjust the production line cycle dynamic model and feeding speed adjustment strategy.

[0012] Furthermore, the sensing unit includes an optical sensing component, a mechanical contact sensor, and a time recording module. The optical sensing component is installed above the workstation entrance and exit to detect the moment when materials enter and leave the workstation in real time. The mechanical contact sensor is installed on the edge of the material carrying platform at the workstation to record the material's arrival status. The time recording module is linked to the former two to record the time when the material arrives at the workstation, the duration of its stay at the workstation, and the time when it leaves the workstation.

[0013] Furthermore, the monitoring point is equipped with a position identifier and a motion state capture device. The position identifier reads the electronic tag on the surface of the material through radio frequency identification technology to determine the specific position of the material on the transmission path. The motion state capture device obtains the material's moving speed, acceleration, and whether there is any jamming motion state information through high-speed image acquisition and analysis.

[0014] Furthermore, the sensing units and monitoring points are connected to the central processing unit via an industrial Ethernet network. The central processing unit is equipped with a data receiving buffer to sort and initially filter the received information according to timestamps, and remove obviously abnormal data.

[0015] Furthermore, the characteristic parameters include the average time for material processing at each workstation, the average moving speed of the material on the transmission path, and the time interval for material transfer between each workstation; the multi-dimensional analysis and processing includes taking the average value after removing the maximum and minimum values ​​when calculating the average time for material processing at each workstation, taking the overall average value after considering different road segment conditions when calculating the average moving speed of the material on the transmission path, and calculating the average time difference of multiple consecutive materials when analyzing the time interval for material transfer between each workstation.

[0016] Furthermore, the historical operating data includes cycle time parameters for different product models during production and cycle time changes during equipment maintenance; the production line cycle time dynamic model is established through data fitting and trend prediction algorithms, and a real-time update module is set within the production line cycle time dynamic model. Each time a new set of sensing information is received, the feature parameters in the production line cycle time dynamic model are corrected.

[0017] Furthermore, when the production line is in a stable operating state, the average cycle time under this state is used as the initial reference for adjusting the feeding speed; when the production line cycle time changes, a graded setting method is used to redetermine the adjustment reference, dividing the production line cycle time into multiple levels, with each level corresponding to a feeding speed range.

[0018] Furthermore, the feeding device is equipped with a speed adjustment execution module, which includes a drive motor controller and a transmission mechanism adjuster; the central processing unit sends adjustment commands to the speed adjustment execution module through a wireless communication module.

[0019] Furthermore, the segmented adjustment method is as follows:

[0020] When the feeding speed deviates significantly from the speed required by the current production line cycle time, a rapid adjustment is made to quickly bring the feeding speed close to the target speed; when the deviation is small, a fine adjustment is made to ensure that the feeding speed remains stable within the target speed range.

[0021] Furthermore, the feedback optimization mechanism specifically includes:

[0022] After the feeding speed is adjusted, the central processing unit starts the effect evaluation module to collect production line operation status information and compile an evaluation report.

[0023] The central processing unit compares and analyzes the actual operating status information with the expected results. If the deviation exceeds a fixed threshold, it optimizes and adjusts the production line cycle dynamic model and feeding speed adjustment strategy, and conducts feedback analysis and optimization adjustments regularly.

[0024] In summary, due to the adoption of the above-mentioned adaptive production line cycle dynamic feeding speed adjustment method, the beneficial effects of this invention are:

[0025] 1. This method constructs a comprehensive and accurate production line cycle time sensing system, breaking through the limitations of fragmented and one-sided information collection in traditional production lines. By setting up multiple sensing components at key workstations and combining them with monitoring points along the transmission path, it can capture various status information of materials in real time from entering the workstation to the transmission process, and the data is filtered to ensure the reliability of the information. This enables the central processing unit to have a comprehensive grasp of the actual operation of the production line, providing solid data support for subsequent adjustments and fundamentally solving the problem of inaccurate adjustments due to insufficient information.

[0026] 2. The established production line cycle dynamic model has dynamic update capability and can reflect the changes in the production line cycle in real time. The model is based on feature parameters of multi-dimensional analysis and is continuously corrected by combining historical data, avoiding the defects of traditional fixed models that cannot adapt to cycle fluctuations. This allows the feeding speed adjustment to always be based on the current real production line cycle, ensuring the timeliness and pertinence of the adjustment, and effectively reducing the material supply and demand imbalance caused by model lag.

[0027] 3. The method for determining the feed speed adjustment benchmark is flexible and precise. The initial benchmark is based on the average cycle time of stable operation. When the cycle time changes, it can respond quickly through graded adjustments, so that the feed speed is always coordinated with the material demand of the production line. Whether the feed speed is increased when the cycle time is accelerated or decreased when the cycle time is slowed down, it can be matched precisely, avoiding the problems of material accumulation or insufficient supply under the traditional fixed benchmark, and improving the smoothness of the connection between various links of the production line.

[0028] 4. The feeding speed adjustment method, which combines segmented adjustment with real-time error correction, balances both efficiency and stability. Rapid adjustment when deviations are large shortens response time, allowing the feeding speed to quickly approach the target; fine adjustment when deviations are small ensures speed stability and reduces the impact of fluctuations on production. Simultaneously, real-time monitoring and correction of signs of material accumulation or insufficient supply form a dynamic risk prevention mechanism, significantly reducing the probability of production interruptions and improving the stability of the production line operation.

[0029] 5. A feedback optimization mechanism forms a closed loop of continuous improvement, enabling the method to possess self-learning and adaptive capabilities. By evaluating the adjustment effects and comparing them with expectations, the dynamic model and adjustment strategies are continuously optimized, making the feeding speed adjustment increasingly precise as the production line operates. This continuous optimization not only adapts to the cycle time changes during the production of different products but also copes with the long-term effects of equipment aging and changes in operating habits. This significantly improves the method's adaptability to various production lines and complex operating conditions. Long-term operation can significantly improve the overall efficiency and stability of the production line, reduce production costs, and enhance production flexibility and reliability. Attached Figure Description

[0030] Figure 1 A flowchart of the method of the present invention is shown. Detailed Implementation

[0031] The dynamic feeding speed adjustment method for the adaptive production line cycle time of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0032] To more clearly and intuitively demonstrate the practical application effects and advantages of the dynamic feeding speed adjustment of the adaptive production line cycle time of this invention, and to verify its feasibility and effectiveness, the invention is further described below with reference to embodiments. Through specific scenario simulations and data calculations, the method is explained in detail how it plays a role in the dynamic feeding speed adjustment of the actual adaptive production line cycle time, helping readers to better understand the technical details and practical value of the invention. The invention is further described below with reference to embodiments;

[0033] See Figure 1 The method for dynamic feeding speed adjustment of adaptive production line cycle time provided by the present invention includes the following steps:

[0034] Step 1: Install sensing units at each key workstation of the production line. These units include optical sensing components, mechanical contact sensors, and time recording modules. Optical sensing components are installed above the workstation entrance and exit, emitting beams of specific wavelengths to detect the instant materials enter and leave the workstation in real time. Mechanical contact sensors are installed on the edge of the material-carrying platform at the workstation; when the material is fully placed on the platform, the sensor is triggered and records the material's arrival status. The time recording module works in conjunction with the other two, accurately recording the time the material arrives at the workstation, its dwell time at the workstation, and its departure time. By installing sensing units containing optical sensing components, mechanical contact sensors, and time recording modules at each key workstation, the material handling status at each workstation can be accurately captured throughout the entire process from material entry and arrival to departure, ensuring accurate time recording and status judgment at each stage and avoiding potential misjudgments due to a single sensing method.

[0035] Meanwhile, along the material transport path of the production line, a monitoring point is set at specific intervals according to the length of the conveyor belt. Each monitoring point is equipped with a position identifier and a motion capture device. The position identifier uses radio frequency identification (RFID) technology to read the electronic tags on the surface of the material to determine the specific position of the material on the transport path. The motion capture device acquires and analyzes high-speed images to obtain information on the material's moving speed, acceleration, and whether there are any jams or other motion statuses. By setting up monitoring points equipped with position identifiers and motion capture devices along the material transport path, combined with RFID technology and high-speed image analysis, the position and motion details of the material during the transport process can be monitored in real time, including whether there are any abnormalities such as jams, allowing the central processing unit to have more comprehensive control over the material transport.

[0036] These sensing units and monitoring points are connected to the central processing unit via industrial Ethernet. The central processing unit has a data receiving buffer that sorts and initially filters the received information according to timestamps, eliminating obviously abnormal data. This forms a cycle time sensing network covering the entire production line, ensuring that the central processing unit can comprehensively and in real-time acquire various information reflecting the production line's cycle time. By connecting the sensing units and monitoring points to the central processing unit using industrial Ethernet, and sorting information and eliminating abnormal data through the data receiving buffer, the cycle time sensing network overcomes the limitations of fragmented and delayed information collection in traditional production lines. This allows the central processing unit to acquire various information reflecting the production line's cycle time in real-time, comprehensively, and reliably, laying a solid data foundation for subsequent precise adjustments.

[0037] Step Two: A data processing module is set up within the central processing unit to perform multi-dimensional analysis and processing of the large amount of real-time information acquired from the sensing system. First, the average material handling time at each workstation is calculated by continuously recording the dwell time of multiple materials at the same workstation, removing the maximum and minimum values, and then taking the average. Second, the average moving speed of materials along the transmission path is statistically analyzed. Combining the location information of each monitoring point and the material passage time, the moving speed of different road segments is calculated, and the overall average is taken. Third, the time interval for material transfer between each workstation is analyzed, i.e., the time difference between the departure time of material at the previous workstation and the arrival time of material at the next workstation, and the average of this time difference for multiple consecutive materials is calculated. The data processing module of the central processing unit performs multi-dimensional analysis of the sensing information, removing extreme values ​​when calculating the average material handling time at each workstation, considering different road segment conditions when calculating the average material transmission speed, and taking the average value of consecutive materials when analyzing the time interval for material transfer between workstations. These processing methods effectively reduce the interference of random factors on the data, making the extracted feature parameters more accurately reflect the actual operating status of the production line.

[0038] Based on these characteristic parameters and combined with historical operating data of the production line (including cycle time parameters for different product models over the past six months and cycle time changes during equipment maintenance), a dynamic cycle time model for the production line is established using data fitting and trend prediction algorithms. This model incorporates a real-time update module. Each time new sensing information is received, the characteristic parameters in the model are corrected, enabling the model to reflect the current cycle time status of the production line in real time and continuously update based on newly acquired information to adapt to dynamic changes in the production line's cycle time. By combining historical operating data and using data fitting and trend prediction algorithms to establish the dynamic cycle time model, and with the real-time update module continuously correcting the characteristic parameters, the model can dynamically track changes in the production line's cycle time. Compared to traditional fixed models that cannot adapt to cycle time fluctuations, this dynamic model can always remain synchronized with the current state of the production line, providing a precise cycle time reference for adjusting the feeding speed and avoiding adjustment deviations caused by model lag.

[0039] Step 3: Based on the production line cycle time dynamic model, the demand analysis module in the central processing unit analyzes the material demand and speed under different cycle time states. When the production line is in a stable operating state, i.e., the fluctuation range of material processing time at each workstation is less than 5% within 10 consecutive minutes, the average cycle time under this state is used as the initial benchmark for adjusting the feeding speed. Based on the production line cycle time dynamic model, the demand analysis module can accurately analyze the material demand under different cycle times. When the production line is running stably, the average cycle time is used as the initial benchmark, ensuring that the starting point for adjusting the feeding speed is reasonable.

[0040] When the production line cycle time changes, such as when the material processing time for five consecutive materials at a certain station increases or decreases by more than 10% compared to the previous average time, the production line cycle time is considered to have changed. The adjustment benchmark is then redefined based on the changed cycle time. The adjustment benchmark is determined using a tiered setting method, dividing the production line cycle time into five levels: ultra-high speed, high speed, normal, low speed, and ultra-low speed. Each level corresponds to a feeding speed range. For example, when the production line cycle time increases, entering the high-speed level, and the material demand increases, the feeding speed adjustment benchmark is correspondingly increased to the range corresponding to the high-speed level; when the production line cycle time decreases, entering the low-speed level, and the material demand decreases, the feeding speed adjustment benchmark is decreased to the range corresponding to the low-speed level. When the production line cycle time changes, the change is determined by the changes in the processing time of multiple consecutive materials, and the adjustment benchmark is redefined using a tiered setting method, allowing the feeding speed adjustment to quickly respond to changes in the cycle time. For example, when the processing time at a certain workstation increases significantly, causing the cycle time to slow down, the feed rate benchmark is reduced in time to avoid material accumulation at that workstation; when the cycle time speeds up, the benchmark is increased accordingly to prevent material shortages at subsequent workstations. This solves the problem that traditional fixed benchmarks or simple adjustment methods cannot adapt to dynamic changes in cycle time and improves the coordination of material supply and demand on the production line.

[0041] Step 4: A speed adjustment execution module is installed within the feeding device. This module includes a drive motor controller and a transmission mechanism adjuster. The central processing unit generates a feeding speed adjustment command based on the determined feeding speed adjustment benchmark and sends it to the feeding device's speed adjustment execution module via a wireless communication module.

[0042] During the adjustment process, a segmented adjustment method is adopted: when the deviation of the feeding speed from the speed required by the current production line cycle exceeds 30%, a rapid adjustment is performed. The drive motor controller increases the current output, enabling the motor speed to increase rapidly in a short time. At the same time, the transmission mechanism regulator adjusts the gear transmission ratio to reduce energy loss during transmission, allowing the feeding speed to quickly approach the target speed. When the deviation is less than 10%, a fine adjustment is performed. The drive motor controller uses pulse width modulation technology to fine-tune the motor speed, and the transmission mechanism regulator compensates for transmission gaps in real time, ensuring that the feeding speed is stable within the target speed range with fluctuations not exceeding 2%. The speed adjustment execution module of the feeding device receives instructions from the central processing unit and adopts a segmented adjustment method. When the speed deviation is large, a rapid adjustment is performed. By increasing the output of the drive motor controller and reducing the obstruction of the transmission mechanism regulator, the feeding speed quickly approaches the target value, shortening the adjustment response time. When the deviation is small, a fine adjustment is performed. Precision control technology and transmission gap compensation are used to ensure that the feeding speed is stable within the target range, avoiding the impact of excessive speed fluctuations on production.

[0043] Meanwhile, during the adjustment process, the central processing unit receives real-time information on the material transmission status and material handling status of each workstation from the sensing system. If it detects that the amount of material at a certain workstation exceeds 80% of its normal capacity, it determines that material accumulation is imminent. If no material enters a certain workstation for 30 consecutive seconds, it determines that a supply shortage is imminent and immediately corrects the feeding speed, such as reducing the feeding speed by 20% when there is accumulation and increasing the feeding speed by 15% when there is a shortage, to avoid serious production problems. During the adjustment process, the central processing unit receives sensing information in real time and makes timely corrections to signs of impending material accumulation or supply shortage, such as reducing the speed when there is accumulation and increasing the speed when there is a shortage. This forms a dynamic real-time error correction mechanism, which effectively prevents serious production problems from occurring. Compared with traditional adjustment methods that lack real-time monitoring and correction, this significantly improves the stability of the production line operation.

[0044] Step 5: Within 3 minutes of adjusting the feeding speed, the central processing unit activates the effect evaluation module to collect production line operating status information, including the average material processing time at each station, the average material dwell time during transmission, and the overall operating efficiency of the production line (i.e., the amount of material processed per unit time). This information is processed by the data aggregation module to generate an evaluation report, which is then fed back to the central processing unit. After adjusting the feeding speed, the effect evaluation module collects information such as the processing status at each station, material dwell time, and overall production line efficiency, and generates an evaluation report through data aggregation, allowing the central processing unit to fully understand the adjustment effect.

[0045] The central processing unit compares and analyzes the actual operating status information in the evaluation report with the expected adjustment effect, which is determined based on the production line design parameters and historical best operating conditions. If the deviation between the actual operating status and the expected effect exceeds 5%, the production line cycle dynamic model and feeding speed adjustment strategy are optimized and adjusted according to the degree of deviation. For example, when the actual feeding speed fluctuation is greater than expected, the weights of the characteristic parameters in the production line cycle dynamic model are adjusted, and the influence factor of the motion state parameters is increased; when the adjusted material accumulation rate is higher than expected, the switching threshold between rapid adjustment and fine adjustment in the feeding speed adjustment strategy is modified; the actual operating status is compared with the expected effect, and when the deviation exceeds a fixed threshold, the dynamic model and adjustment strategy are optimized, such as adjusting the weights of model characteristic parameters and modifying the adjustment switching threshold, so that the model and strategy can continuously adapt to changes in the production line;

[0046] By performing feedback analysis and optimization adjustments every hour, the feeding speed regulation becomes more precise and better adapts to changes in the production line's cycle time. This hourly feedback optimization forms a closed loop of continuous improvement, allowing the feeding speed regulation to become increasingly precise as the production line operates. This solves the problem of traditional adjustment methods being difficult to optimize and upgrade once set, significantly improving the method's adaptability to different production lines and operating conditions. Long-term operation can significantly improve the overall efficiency and stability of the production line. Specific implementation examples:

[0048] The electronic product assembly line includes several key workstations such as component soldering, motherboard assembly, casing assembly, and functional testing. The first step is to establish a production line cycle time sensing system. Optical sensing components are installed above the entrance and exit of each workstation, using devices that emit stable light beams. When materials enter or leave the workstation, the light beam is blocked, and the components record these two moments. Mechanical contact sensors are installed at the edge of the material-carrying platform at each workstation. When the material is fully placed on the platform, the sensor is triggered by the pressure of the material, thus recording the accurate placement of the material. Each workstation is also equipped with a time recording module connected to the optical sensing components and mechanical contact sensors. When the optical sensing component detects material entering, the time recording module starts timing and stops timing when the optical sensing component detects material leaving, thus accurately recording the duration of the material's stay at that workstation, as well as the specific arrival and departure times of the material.

[0049] Along the material transport path of the production line, i.e., the conveyor belt, multiple monitoring points are evenly set according to the length of the conveyor belt. Each monitoring point is equipped with a position identifier, and a unique electronic tag is attached to the surface of the material. When the material passes through the monitoring point, the position identifier can read the information in the tag to determine the specific position of the material on the conveyor belt. Each monitoring point is also equipped with a motion capture device, which continuously collects images of the material on the conveyor belt and analyzes the collected images to determine whether the material's movement speed is fast or slow, whether it is accelerating, or whether it has stopped moving.

[0050] These sensing units and monitoring points are all connected to the central processing unit via a stable industrial network. The central processing unit has a dedicated data receiving area that receives information from each sensing unit and monitoring point, arranges the information according to the time sequence of its generation, and removes data that is obviously inconsistent with the actual situation, such as material dwell time recorded at a certain workstation that far exceeds the reasonable range of normal production. This forms a cycle time sensing network that fully covers the production line, allowing the central processing unit to understand the cycle time information of the production line in a timely and comprehensive manner.

[0051] Next, a dynamic model of the production line cycle time is established. The central processing unit processes and analyzes the received information. For each workstation, the dwell time of multiple materials is continuously recorded. Extremely long and short times are removed, and the average of the remaining times is calculated to obtain the average material processing time for that workstation. Regarding the movement of materials on the conveyor belt, the material's speed on different sections of the conveyor belt is calculated by combining the material passage time and position information recorded at each monitoring point. The average speed along the entire transmission path is then derived. Simultaneously, the time difference between the departure time of materials at the previous workstation and the arrival time of materials at the next workstation is analyzed. This time difference is continuously recorded for multiple materials, and the average value is calculated to understand the time interval of material transfer between each workstation.

[0052] By combining historical data such as the production line's cycle time when producing different models of electronic products over a period of time, and the changes in the production line's cycle time during equipment maintenance, a dynamic model of the production line's cycle time is established using data processing and trend prediction methods. This model continuously receives new sensor information, and whenever new information is received, the relevant parameters reflecting the production line's cycle time in the model are adjusted so that the model can always accurately reflect the current cycle time status of the production line.

[0053] Then, the feed rate adjustment benchmark is determined. The central processing unit analyzes the material demand of the production line at different cycle times based on the production line cycle time dynamic model. When the production line has been running continuously for a period of time and the material processing time at each station varies little, indicating a stable operating state, the average cycle time at this point is used as the initial benchmark for feed rate adjustment.

[0054] When the production line's cycle time changes—for example, if the processing time for multiple consecutive materials at a certain workstation significantly increases or decreases compared to the previous average time—the central processing unit (CPU) will determine that the production line's cycle time has changed and then redetermine the feed speed adjustment benchmark based on the changed cycle time. For instance, if the material processing time at the motherboard assembly workstation increases significantly, it indicates that the production line's cycle time has slowed down, and the CPU will lower the feed speed adjustment benchmark to reduce the feed speed accordingly.

[0055] When dynamically adjusting the feeding speed, the speed adjustment module within the feeding device receives adjustment commands from the central processing unit. When the feeding speed differs significantly from the speed required by the current production line cycle time, rapid adjustment is performed. The drive motor controller increases its output, rapidly increasing the motor speed, while the transmission mechanism adjusts its internal structure to reduce resistance during transmission, quickly bringing the feeding speed closer to the target speed. When the difference between the feeding speed and the target speed is small, fine adjustment is performed. The drive motor controller uses a more precise control method to subtly adjust the motor speed, and the transmission mechanism adjusts in real time to compensate for minor gaps in the transmission process, ensuring the feeding speed remains stable near the target speed without significant fluctuations.

[0056] Throughout the adjustment process, the central processing unit continuously receives information from the sensing system, closely monitoring the material quantity and transmission status at each workstation. If it detects that the material quantity at a certain workstation is about to exceed the normal capacity of that workstation, it will immediately instruct the feeding device to slow down the feeding speed; if no material enters a certain workstation for an extended period, it will instruct the feeding device to speed up the feeding speed to avoid material accumulation or insufficient supply.

[0057] After the feeding speed is adjusted, the central processing unit will activate the performance evaluation module for a period of time to collect information on the material handling status of each workstation, such as the smoothness of material handling at each workstation and the number of products assembled per unit time for the entire production line. This information is then processed into an evaluation report and fed back to the central processing unit.

[0058] The central processing unit compares the actual operating status with the expected results. If a significant discrepancy is found, it optimizes the production line cycle time dynamic model and the feeding speed adjustment strategy. For example, if the feeding speed is consistently unstable, the importance of parameters reflecting material movement in the model is adjusted; if material accumulation is greater than expected, the switching conditions between rapid and fine adjustment are modified. Through this continuous feedback and optimization, the feeding speed adjustment becomes increasingly precise, better adapting to changes in the production line cycle time.

[0059] This method plays a continuous role throughout the entire production line operation. When the production line switches to produce different models of electronic products, the operation content and time of each workstation change, and the production line cycle time also changes accordingly. This method can detect these changes in a timely manner and adjust the feeding speed accordingly to ensure that the material supply matches the production line cycle time, so that the production line always maintains an efficient and stable operating state.

[0060] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the dynamic feeding speed adjustment method for adaptive production line cycle and its inventive concept, should be covered within the scope of protection of the present invention.

Claims

1. A method for dynamically adjusting the feeding speed of an adaptive production line, characterized in that, Includes the following steps: Step 1: Construct a production line cycle time sensing system. Set up sensing units at each key workstation of the production line and set up multiple monitoring points on the material transport path of the production line. Connect the sensing units and monitoring points to the central processing unit to form a cycle time sensing network covering the entire production line. Step 2: Establish a dynamic model of the production line cycle time. The central processing unit performs multi-dimensional analysis and processing on the real-time information obtained from the sensing system, extracts feature parameters, and combines them with the historical operating data of the production line to establish a dynamic model of the production line cycle time that can be updated in real time. Step 3: Determine the feed speed adjustment benchmark. Based on the production line cycle dynamic model, analyze the material demand and demand speed of the production line under different cycle states, determine the initial benchmark, and re-determine the adjustment benchmark when the production line cycle changes. Step 4: Implement dynamic adjustment of feeding speed. The feeding device adjusts the speed in segments according to the adjustment instructions sent by the central processing unit, while monitoring and correcting signs of material accumulation or insufficient supply in real time. Step 5: Construct a feedback optimization mechanism to feed back the production line operation status information after the feeding speed adjustment to the central processing unit, compare and analyze the actual and expected effects, and optimize and adjust the production line cycle dynamic model and feeding speed adjustment strategy.

2. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The sensing unit includes an optical sensing component, a mechanical contact sensor, and a time recording module. The optical sensing component is installed above the workstation entrance and exit to detect the moment when materials enter and leave the workstation in real time. The mechanical contact sensor is installed on the edge of the material carrying platform at the workstation to record the material's arrival status. The time recording module is linked to the former two to record the time when the material arrives at the workstation, the duration of its stay at the workstation, and the time when it leaves the workstation.

3. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The monitoring point is equipped with a position identifier and a motion state capture device. The position identifier reads the electronic tag on the surface of the material through radio frequency identification technology to determine the specific position of the material on the transmission path. The motion state capture device obtains the material's moving speed, acceleration, and whether there is any jamming motion state information through high-speed image acquisition and analysis.

4. The dynamic feeding speed adjustment method for adaptive production line cycle time according to claim 1, characterized in that, The sensing units and monitoring points are connected to the central processing unit via an industrial Ethernet. The central processing unit is equipped with a data receiving buffer, which sorts and preliminarily filters the received information according to timestamps, and removes obviously abnormal data.

5. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The characteristic parameters include the average time for material processing at each workstation, the average speed of material movement on the transmission path, and the time interval for material transfer between each workstation; the multi-dimensional analysis and processing include taking the average value after removing the maximum and minimum values ​​when calculating the average time for material processing at each workstation, taking the overall average value after considering different road segment conditions when calculating the average speed of material movement on the transmission path, and calculating the average time difference of multiple consecutive materials when analyzing the time interval for material transfer between each workstation.

6. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The historical operating data includes cycle time parameters for different product models during production and cycle time changes during equipment maintenance. The production line cycle time dynamic model is established through data fitting and trend prediction algorithms. A real-time update module is set in the production line cycle time dynamic model. Each time a new set of sensing information is received, the feature parameters in the production line cycle time dynamic model are corrected.

7. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, When the production line is in a stable operating state, the average cycle time under this state is used as the initial reference for adjusting the feeding speed. When the cycle time of the production line changes, the adjustment reference is redefined using a graded setting method, dividing the production line cycle time into multiple levels, with each level corresponding to a feeding speed range.

8. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The feeding device is equipped with a speed adjustment execution module, which includes a drive motor controller and a transmission mechanism adjuster; the central processing unit sends adjustment commands to the speed adjustment execution module through a wireless communication module.

9. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The segmented adjustment method is as follows: When the feeding speed deviates significantly from the speed required by the current production line cycle time, a rapid adjustment is made to quickly bring the feeding speed close to the target speed; when the deviation is small, a fine adjustment is made to ensure that the feeding speed remains stable within the target speed range.

10. The method for dynamic feeding speed adjustment of adaptive production line cycle time according to claim 1, characterized in that, The feedback optimization mechanism is specifically as follows: After the feeding speed is adjusted, the central processing unit starts the effect evaluation module to collect production line operation status information and compile an evaluation report. The central processing unit compares and analyzes the actual operating status information with the expected results. If the deviation exceeds a fixed threshold, it optimizes and adjusts the production line cycle dynamic model and feeding speed adjustment strategy, and conducts feedback analysis and optimization adjustments regularly.

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