Line multi-process joint control system for forklift half shaft production

Abstract

The present application belongs to the technical field of forklift axle production control, specifically a production line multi-process joint control system for forklift axle production, comprising an electromagnetic oven heating module, a bar end upset processing module, a flange plate extrusion forming module, a mechanical arm transfer module, a multi-process joint control module and a production line supervision center; the bar is uniformly heated at the heated end by the electromagnetic oven heating module, the bar end upset processing module precisely applies axial pressure to ensure that the heated end of the bar becomes thicker and shorter, and the flange plate extrusion forming module relies on a customized die to push the steel to completely fill the cavity to form a flange plate, realizing unified and collaborative control of the production line multi-process of forklift axle production, replacing manual operation to reduce errors, and through the joint control risk early warning module, potential hazards of each device are identified in advance and early warning is generated, effectively ensuring the continuous, safe and stable operation of the production line, and significantly reducing the difficulty of production line operation supervision of forklift axle production.

Description

Technical Field

[0001] This invention relates to the field of forklift half-shaft production control technology, specifically to a multi-process joint control system for forklift half-shaft production lines. Background Technology

[0002] As the core force transmission component of the forklift drive system, the flange thermoforming process of the forklift half shaft requires multiple key processes, including electromagnetic heating, bar transfer, upsetting, extrusion molding, and cooling to prevent cracking. Each process has strict requirements for temperature control, pressure parameters, transfer accuracy, and coordination rhythm, which directly affect the mechanical performance and forming quality of the half shaft.

[0003] For example, Chinese invention patent with publication number CN105063468A involves a forging process for a forklift drive axle half shaft. The patent document clearly points out that in the traditional forklift half shaft forging and processing process, the number of heating cycles leads to a significant increase in the heat consumption and energy consumption of the forging, the forging time is long and the efficiency is low, and the forging precision is difficult to control effectively. The labor intensity of employees is high, which is not conducive to energy conservation, emission reduction and high-quality production requirements.

[0004] Currently, even with the introduction of automated equipment in some production lines, it is still difficult to achieve unified and collaborative control of multiple processes in the production of forklift half shafts. Furthermore, there is a general lack of risk warning and operational effect evaluation mechanisms for the entire process, making it difficult to predict in advance and take reasonable improvement measures in a timely manner. At the same time, the assessment of the effectiveness of isolating safety hazards in the production line area is missing, making it impossible to grasp the situation of risk spillover and diffusion in a timely manner. As a result, it is difficult to meet the needs of large-scale and high-precision production of forklift half shafts in terms of quality control, production efficiency, and safety management. Therefore, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-process joint control system for forklift half-shaft production lines to address the technical deficiencies mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-process joint control system for forklift half-shaft production line, including an induction cooker heating module, a bar end upsetting module, a flange extrusion forming module, a robotic arm transfer module, a multi-process joint control module, and a production line monitoring center; The multi-process joint control module controls the induction cooker heating module, the bar end upsetting module, the flange extrusion forming module, and the robotic arm transfer module, and sends the control information and the operation monitoring information of each equipment module to the production line monitoring center. The robotic arm transfer module fixes the pre-treated bar and places it into the induction cooker heating module. The induction cooker heating module heats one end of the pre-treated bar through electromagnetic heating. Once the bar stock has been heated to the required temperature, the robotic arm transfer module transfers the bar stock to the bar stock end upsetting module. The bar stock end upsetting module applies axial pressure to the heated end of the bar stock through a hydraulic press, causing the heated end of the bar stock to undergo plastic deformation to achieve the upsetting effect of becoming thicker and shorter. The robotic arm transfer module transfers the upset bar stock to the flange extrusion forming module. The flange extrusion forming module uses an extruder to extrude the upset section of the bar stock, causing the steel to flow plastically within the extruder's customized mold, completely filling the mold cavity to form the initial shape of the flange.

[0007] Furthermore, the multi-process joint control module is connected to the joint control risk early warning module. The multi-process joint control module sends control information and operation monitoring information of each equipment module to the joint control risk early warning module. The joint control risk early warning module analyzes the operation and management performance of each equipment and determines whether to generate a joint control early warning signal. When a joint control early warning signal is generated, it is sent to the production line monitoring center. When the production line monitoring center receives the joint control early warning signal, it issues a corresponding early warning.

[0008] Furthermore, the specific analysis process of the joint control risk early warning module includes: By capturing and analyzing the evaluation, it is determined whether there are maintenance difficulties parameters for the corresponding module equipment within a unit time. If maintenance difficulties parameters exist for the corresponding module equipment within a unit time, the corresponding module equipment is marked as a non-favorable cooperation device. If maintenance difficulties parameters do not exist for the corresponding module equipment within a unit time, normalization analysis is performed to determine whether the corresponding module equipment is a non-favorable cooperation device. If a non-favorable cooperation device exists, a joint control early warning signal is generated.

[0009] Furthermore, the specific process of capturing and evaluating is as follows: The parameters that need to be monitored during the operation of the corresponding device module are obtained, and the real-time data of the corresponding parameters are compared with the corresponding set standard value requirements. If the real-time data of the corresponding parameters does not meet the corresponding set standard value requirements, the corresponding parameters are judged to be in a mismatch state. The duration of the corresponding parameter being in a non-matching state within a unit time is obtained and the ratio of this duration to the total working time of the corresponding device module within a unit time is calculated to obtain the parameter matching time value. If the corresponding parameter is in a non-matching state and fails to be restored to a normal state within the corresponding standard duration, a recovery anomaly symbol QK-1 is assigned. The number of times the recovery anomaly symbol QK-1 is assigned to the corresponding parameter within a unit time is obtained and marked as the recovery anomaly standard frequency value. The parameter matching time-varying value and the recovery anomaly frequency standard value are compared with the corresponding preset parameter matching time-varying threshold and preset recovery anomaly frequency standard threshold respectively. If the parameter matching time-varying value or the recovery anomaly frequency standard value exceeds the corresponding preset threshold, the corresponding parameter is marked as a maintenance difficulty parameter for the corresponding module device.

[0010] Furthermore, the specific analysis process for performing normalization analysis is as follows: The first feature value is obtained by comparing the ratio of the parameter matching time-varying value of the corresponding parameter with the corresponding preset parameter matching time-varying threshold, and the second feature value is obtained by comparing the recovery anomaly standard frequency value of the corresponding parameter with the corresponding preset recovery anomaly standard frequency threshold. The first feature value and the second feature value are then weighted and summed to obtain the feature weight value. Each parameter is pre-set to correspond to a set of preset weight values. The feature weight value of the corresponding parameter is multiplied by the corresponding preset weight value to obtain the feature influence value. The feature influence values ​​of all parameters that the corresponding device module needs to monitor are summed to obtain the feature comprehensive coefficient. The feature comprehensive coefficient is compared with the corresponding preset feature comprehensive coefficient threshold. If the feature comprehensive coefficient exceeds the preset feature comprehensive coefficient threshold, the corresponding module device is marked as a non-advantageous device.

[0011] Furthermore, the joint control risk warning module is connected to the production line operation effect output module. If no joint control warning signal is generated within a unit of time, the production line operation effect of the production line used for forklift half shaft production within a unit of time is analyzed through the production line operation effect output module. The analysis is used to determine whether a production line operation efficiency warning signal should be generated. When a production line operation efficiency warning signal is generated, it is sent to the production line monitoring center. When the production line monitoring center receives the production line operation efficiency warning signal, it issues a corresponding warning.

[0012] Furthermore, the specific analysis process of the production line operation effect output module is as follows: The number of forklift half shafts produced and scrapped within a unit time is obtained and the ratio is calculated with the total number of forklift half shafts processed within a unit time to obtain the forklift half shaft scrap rate value. The forklift half shaft scrap rate value is compared with the preset forklift half shaft scrap rate threshold. If the forklift half shaft scrap rate value exceeds the preset forklift half shaft scrap rate threshold, a production line operation efficiency warning signal is generated. If the scrap rate of the forklift half shaft does not exceed the preset scrap rate threshold of the forklift half shaft, the number of production line interruptions per unit time is marked as the production line interruption detection frequency value, and the production line interruption duration per unit time is marked as the production line interruption detection time value. The production line interruption detection frequency value and the production line interruption detection time value are compared with the preset production line interruption detection frequency threshold and the preset production line interruption detection time threshold respectively. If the production line interruption detection frequency value or the production line interruption detection time value exceeds the corresponding preset threshold, a production line operation efficiency warning signal is generated. If the production line operation interruption frequency and production line operation interruption time do not exceed the corresponding preset threshold, the variance of the processing time for each forklift half shaft per unit time is calculated to obtain the efficiency fluctuation value, and the ratio of the number of forklift half shafts whose processing time exceeds the preset processing time threshold to the total number of forklift half shafts processed per unit time is calculated to obtain the efficiency buffer value. The efficiency fluctuation value and efficiency buffer value are compared with the preset efficiency fluctuation threshold and preset efficiency buffer threshold respectively. If the efficiency fluctuation value or efficiency buffer value exceeds the corresponding preset threshold, a production line operation efficiency warning signal is generated.

[0013] Furthermore, the production line operation effect output module communicates with the isolation effectiveness assessment module. If no production line operation effect early warning signal is generated within a unit of time, the isolation effectiveness assessment module analyzes the degree of safety hazards that the production line poses to the surrounding space within a unit of time. The analysis determines whether to generate an isolation effectiveness early warning signal. When an isolation effectiveness early warning signal is generated, it is sent to the production line monitoring center. When the production line monitoring center receives the isolation effectiveness early warning signal, it issues a corresponding warning.

[0014] Furthermore, the specific analysis process for the isolation effectiveness assessment module includes: Obtain the edge contour of the area involved in the production line and mark it as the target contour. Set several target points on the target contour. Extend L1 meters outward from the target point to determine the diffusion point. Connect all the diffusion points one by one to form the diffusion contour. Mark the area enclosed by the diffusion contour and the target contour as the isolation area. Environmental monitoring equipment is deployed at several locations within the isolation area. The analysis is used to determine whether there are any isolation vulnerabilities within the isolation area. If there are any isolation vulnerabilities, an isolation effectiveness warning signal is generated. If there are no isolation vulnerabilities, the average of the isolation effectiveness anomalies at all locations within the isolation area is calculated to obtain an effectiveness characteristic value. The effectiveness characteristic value is then compared with a preset effectiveness characteristic threshold. If the effectiveness characteristic value exceeds the preset effectiveness characteristic threshold, an isolation effectiveness warning signal is generated.

[0015] Furthermore, the specific methods for analyzing and determining the location of isolation vulnerabilities are as follows: The average temperature at the corresponding location within a unit time is obtained and marked as the temperature hazard characteristic value. The average dust concentration and average noise decibel value at the corresponding location within a unit time are marked as the dust hazard characteristic value and noise hazard characteristic value, respectively. The types of harmful gases that need to be monitored in the isolated area are obtained. The concentrations of all types of harmful gases at the corresponding location are collected in real time and summed to obtain the harmful gas concentration coefficient. The average of all harmful gas concentration coefficients within a unit time is calculated to obtain the gas hazard characteristic value. The isolation effectiveness anomaly value is obtained by weighted summation of temperature hazard characteristic value, dust hazard characteristic value, noise hazard characteristic value and air hazard characteristic value. The isolation effectiveness anomaly value is compared with the preset isolation effectiveness anomaly value threshold. If the isolation effectiveness anomaly value exceeds the preset isolation effectiveness anomaly value threshold, the corresponding location is marked as the isolation vulnerability location of the isolation area.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, the heating end of the bar is uniformly heated by the induction cooker heating module, and the bar end upsetting module makes the heating end of the bar thicker and shorter. The flange extrusion forming module forms a flange at the heating end through extrusion. This realizes unified collaborative control of multiple processes in the forklift half-shaft production line, replaces manual operation and reduces errors. Furthermore, the joint control risk warning module identifies potential hazards of each piece of equipment in advance and generates warnings, effectively ensuring the continuous safe and stable operation of the production line and significantly reducing the difficulty of production line operation supervision for forklift half-shaft production.

[0017] 2. In this invention, the production line operation effect output module monitors the production effect from multiple dimensions and promptly detects quality decline and efficiency abnormalities when there is no joint control warning, ensuring the production line operation effect. In addition, the isolation effectiveness assessment module accurately judges the safety hazards caused by the production line to the surrounding space, avoiding the risk spillover of the production line area, which is conducive to protecting personnel health and production environment compliance, and further reducing the difficulty of production line operation supervision for forklift half shaft production. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1 As shown, the multi-process joint control system for forklift half-shaft production line proposed in this invention includes an induction cooker heating module, a bar end upsetting module, a flange extrusion forming module, a robotic arm transfer module, a multi-process joint control module, a joint control risk early warning module, and a production line monitoring center; The multi-process joint control module controls the induction cooker heating module, the bar end upsetting module, the flange extrusion molding module, and the robotic arm transfer module. It also sends the control information and the operation monitoring information of each equipment module to the production line monitoring center, realizing full-process automated control of forklift half-shaft production. This replaces the tedious manual operation of repeatedly adjusting temperature, pressure, and transfer path, reduces operational errors caused by differences in human experience, and provides visual production status monitoring information for supervisors, ensuring the stable and continuous production rhythm of the production line and reducing the difficulty of production management.

[0021] The robotic arm transfer module (which achieves stable clamping and transfer of bar stock through precise posture adjustment and preset path planning, improving the connection efficiency and continuity of the entire process) uses a robotic arm to fix the pre-treated bar stock and place it on the induction cooker heating module. The induction cooker heating module heats one end of the pre-treated bar stock (the part that needs to be formed into a flange) through electromagnetic heating, providing qualified hot bar stock for subsequent upsetting and forming, reducing forming defects caused by uneven heating or temperature deviation from the source, and ensuring the quality of flange thermoforming; It should be noted that, based on the temperature requirements for flange hot forming during the production and processing of forklift half shafts, a fixed heating temperature range of 1100 to 1250°C is preset (this range allows the steel to reach the austenitic state and has good plasticity); furthermore, electromagnetic heating is achieved through electromagnetic induction, resulting in uniform and efficient heating, and precise control of the heating area to prevent the overall overheating of the bar.

[0022] Once the bar stock has reached the required heating temperature, a "heating complete signal" is sent. The robotic arm transfer module then transfers the bar stock to the bar stock end upsetting module. Specifically, after adjusting the robotic arm's posture, the bar stock is precisely gripped and then transferred to the press table along a preset path, ensuring that the heated end of the bar stock (i.e., the part to be upset) is aligned with the hydraulic press's pressure application position. After placing the bar stock on the press table, a "bar stock positioning signal" is sent to the hydraulic press, triggering the upsetting process. The bar stock end upsetting module applies axial pressure to the heated end of the bar stock using a hydraulic press (typically with a tonnage of 1000–2000 tons), causing the heated end of the bar stock to undergo plastic deformation, thus achieving the upsetting effect of "becoming thicker and shorter," and reserving sufficient material volume for subsequent flange forming.

[0023] Once the upsetting process is completed, an "upsetting completion signal" is issued. The robotic arm transfer module then uses the robotic arm to transfer the upset bar stock to the flange extrusion forming module. The flange extrusion forming module uses an extruder to extrude the upset section of the bar stock, causing the steel to plastically flow within the extruder's customized mold (the mold cavity is consistent with the shape of the finished flange, including the flange thickness and bolt hole pre-formed structure), completely filling the mold cavity and ultimately forming the preliminary shape of the flange (forming a preliminary shape including the flange body and connection hole pre-formed grooves). This effectively ensures the integrity and dimensional consistency of the flange pre-formed structure, significantly reducing the rework rate caused by structural defects or dimensional deviations after forming, and improving the stability of flange forming quality.

[0024] Furthermore, the multi-process joint control module sends control information and operation monitoring information of each equipment module to the joint control risk early warning module. The joint control risk early warning module analyzes the operation and management performance of each equipment and determines whether to generate a joint control early warning signal. When a joint control early warning signal is generated, it is sent to the production line monitoring center. When the production line monitoring center receives a joint control early warning signal, it issues a corresponding warning, which can provide early warning and remind supervisory personnel to check and repair, preventing potential hazards from escalating and causing equipment damage or safety accidents. This effectively ensures the continuous safe and stable operation of the production line and reduces the increase in scrap rate and production losses caused by equipment problems. The specific analysis process of the joint control risk early warning module is as follows: The parameters that need to be monitored during the operation of the corresponding equipment module are obtained (such as the heating temperature of the induction cooker heating module, the hydraulic pressure of the bar end upsetting module, the extrusion pressure of the flange extrusion molding module, etc.). The real-time data of the corresponding parameters are compared with the corresponding set standard values. If the real-time data of the corresponding parameters does not meet the corresponding set standard values, it indicates that the operation of the corresponding parameters is deviating, which is not conducive to the safe and stable operation of the corresponding module equipment. Therefore, the corresponding parameters are judged to be in a mismatch state. The duration of the corresponding parameter being in a mismatched state within a unit time is obtained and the ratio of this duration to the total working time of the corresponding device module within a unit time is calculated to obtain the parameter matching time value. If the corresponding parameter is in a mismatched state and fails to be restored to a normal state within the corresponding standard duration, it indicates that the control efficiency for the corresponding mismatched state is slow. In this case, a recovery anomaly symbol QK-1 is assigned. The number of times the recovery anomaly symbol QK-1 is assigned to the corresponding parameter within a unit time is obtained and marked as the recovery anomaly standard frequency value. The parameter matching time-of-flight value and the recovery anomaly frequency value are compared with the corresponding preset parameter matching time-of-flight threshold and the preset recovery anomaly frequency value, respectively. If the parameter matching time-of-flight value or the recovery anomaly frequency value exceeds the corresponding preset threshold, it indicates that the maintenance and control status of the corresponding parameter is not good within a unit of time. Then the corresponding parameter is marked as the maintenance difficulty parameter of the corresponding module device. If the corresponding module equipment has parameters that are difficult to maintain within a unit of time, it indicates that the risk level of multi-process joint control of the forklift half shaft production line is high within a unit of time. In this case, the corresponding module equipment is marked as equipment that is not favorable for cooperation. If the corresponding module equipment does not have parameters that are difficult to maintain within a unit of time, the first feature value is obtained by comparing the parameter matching time-varying value of the corresponding parameter with the corresponding preset parameter matching time-varying threshold value. The second feature value is obtained by comparing the recovery anomaly standard frequency value of the corresponding parameter with the corresponding preset recovery anomaly standard frequency threshold value. The first feature value is obtained by weighted summation of the first and second feature values. Specifically, the first and second feature values ​​are assigned corresponding preset weight coefficients, and the first and second feature values ​​are multiplied by their respective preset weight coefficients. The sum of the two product results is then marked as the feature weight value. It should be noted that the larger the feature weight value, the worse the overall performance of the corresponding parameter. Each parameter is pre-set to correspond to a set of preset weight values ​​(all values ​​are greater than zero, and the more severe the adverse effect of the abnormality of the corresponding parameter on the module device, the larger the value of the preset weight value that matches it). The feature weight value of the corresponding parameter is multiplied by the corresponding preset weight value to obtain the feature influence value, and the feature influence values ​​of all parameters that the corresponding device module needs to monitor are summed to obtain the feature comprehensive coefficient. It should be noted that the larger the value of the feature comprehensive coefficient, the worse the overall performance of the corresponding module equipment in operation and control within a unit of time. The feature comprehensive coefficient is compared with the corresponding preset feature comprehensive coefficient threshold. If the feature comprehensive coefficient exceeds the preset feature comprehensive coefficient threshold, it indicates that the overall performance of the corresponding module equipment in operation and control within a unit of time is poor, and the corresponding module equipment is marked as non-cooperative equipment. If there is non-cooperative equipment within a unit of time, it indicates that the degree of hidden danger in the joint control of multiple processes in the forklift half shaft production line within a unit of time is high, and a joint control early warning signal is generated.

[0025] Example 2: Figure 2 As shown, the difference between this embodiment and embodiment one is that the joint control risk warning module is connected to the production line operation effect output module. If no joint control warning signal is generated within a unit time, the production line operation effect of the production line used for forklift half shaft production within a unit time is analyzed through the production line operation effect output module to determine whether to generate a production line operation effect warning signal. When a production line operation efficiency early warning signal is generated, it is sent to the production line monitoring center. Upon receiving the signal, the center issues a corresponding warning, enabling analysis from three dimensions: production line operation quality, stability, and efficiency. This allows for comprehensive monitoring of the actual production line operation, timely detection of issues such as quality decline, frequent interruptions, and efficiency fluctuations, and helps supervisors quickly pinpoint the causes and implement improvement measures and strengthen subsequent production line operation monitoring. This further ensures the production line maintains a consistently high-efficiency and high-quality operating state, reducing cost waste and resource consumption. The specific analysis process of the production line operation efficiency output module is as follows: The number of forklift half shafts produced and scrapped within a unit time is obtained and the ratio is calculated with the total number of forklift half shafts processed within a unit time to obtain the forklift half shaft scrap rate value. The forklift half shaft scrap rate value is compared with the preset forklift half shaft scrap rate threshold. If the forklift half shaft scrap rate value exceeds the preset forklift half shaft scrap rate threshold, it indicates that the operating quality of the production line within a unit time is not good, and a production line operation efficiency warning signal is generated. If the scrap rate of the forklift half shaft does not exceed the preset scrap rate threshold, the number of production line interruptions per unit time is marked as the production line interruption detection frequency value, and the duration of production line interruptions per unit time is marked as the production line interruption detection time value. The production line interruption detection frequency value and the production line interruption detection time value are compared with the preset production line interruption detection frequency threshold and the preset production line interruption detection time threshold respectively. If the production line interruption detection frequency value or the production line interruption detection time value exceeds the corresponding preset threshold, it indicates that the operation stability of the production line is not good per unit time, and a production line operation efficiency warning signal is generated. If the production line downtime detection frequency and downtime value do not exceed the corresponding preset thresholds, the variance of the processing time for each forklift half-shaft per unit time is calculated to obtain the efficiency fluctuation value, and the ratio of the number of forklift half-shafts whose processing time exceeds the preset processing time threshold to the total number of forklift half-shafts processed per unit time is calculated to obtain the efficiency buffer value. The efficiency fluctuation value and efficiency buffer value are then compared with the preset efficiency fluctuation threshold and preset efficiency buffer threshold, respectively. If the efficiency fluctuation value or efficiency buffer value exceeds the corresponding preset threshold, it indicates that the production line's operating efficiency is poor per unit time, and a production line operation efficiency warning signal is generated.

[0026] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the production line operation effect output module is connected to the isolation effectiveness evaluation module. If no production line operation effect warning signal is generated within a unit time, the isolation effectiveness evaluation module analyzes the degree of safety hazards that the production line brings to the surrounding space within a unit time, and determines whether to generate an isolation effectiveness warning signal through analysis. Furthermore, when an isolation effectiveness warning signal is generated, it is sent to the production line monitoring center. Upon receiving the warning signal, the production line monitoring center issues a corresponding warning, which can effectively control the safety hazards of the production line to the surrounding space, prevent the risk spillover and spread from the production line area, protect the health and safety of personnel in the surrounding area, and ensure that the production environment of the production line meets safety regulations, thereby improving the overall safety and compliance of the production scenario and further reducing the difficulty of production line operation supervision. The specific analysis process of the isolation effectiveness assessment module is as follows: Obtain the edge contour of the area involved in the production line and mark it as the target contour. Set several target points on the target contour. Extend L1 meters outward from the target point to determine the diffusion point. Preferably, L1 is three meters. Connect all the diffusion points one by one to form the diffusion contour. Mark the area enclosed by the diffusion contour and the target contour as the isolation area. Environmental monitoring equipment is deployed at several locations within the isolation area. Based on the environmental monitoring equipment, monitoring and data collection are carried out to obtain the average temperature of the corresponding location per unit time and mark it as the temperature hazard characteristic value. The average dust concentration and average noise decibel value of the corresponding location per unit time are marked as the dust hazard characteristic value and noise hazard characteristic value, respectively. The types of harmful gases that need to be monitored in the isolation area (such as carbon monoxide, sulfides, nitrogen oxides, etc.) are also obtained. The concentrations of all types of harmful gases at the corresponding location are collected in real time and summed to obtain the harmful gas concentration coefficient. The average value of all harmful gas concentration coefficients per unit time is calculated to obtain the gas hazard characteristic value. The isolation effectiveness anomaly value is obtained by weighted summation of temperature hazard characteristic values, dust hazard characteristic values, noise hazard characteristic values, and air hazard characteristic values. Specifically, each of the temperature hazard characteristic value, dust hazard characteristic value, noise hazard characteristic value, and air hazard characteristic value is assigned a corresponding preset weight coefficient, and then each of these four characteristics is multiplied by its respective preset weight coefficient. The sum of the four product results is then marked as the isolation effectiveness anomaly value. It should be noted that the larger the isolation effectiveness anomaly value, the worse the isolation effect at the corresponding location per unit time. The abnormal isolation effectiveness value is compared with the preset abnormal isolation effectiveness threshold. If the abnormal isolation effectiveness value exceeds the preset abnormal isolation effectiveness threshold, it indicates that the isolation effect of the corresponding location is poor per unit time. The corresponding location is then marked as the isolation vulnerability location of the isolation area. If there is an isolation vulnerability location in the isolation area, it indicates that the isolation effectiveness of the production line processing area is poor per unit time, which poses a high safety risk to external personnel. In this case, an isolation effectiveness warning signal is generated. If there are no isolation vulnerabilities within the isolation area, the average of the isolation effectiveness measurements of all locations within the isolation area is used to calculate the effectiveness feature value. The effectiveness feature value is then compared with a preset effectiveness feature threshold. If the effectiveness feature value exceeds the preset effectiveness feature threshold, it indicates that the isolation effectiveness of the production line processing area is poor per unit time, posing a high safety risk to external personnel, and an isolation effectiveness warning signal is generated.

[0027] The working principle of this invention is as follows: During use, the heating module of the induction cooker achieves uniform heating of the bar end through electromagnetic induction. The bar end upsetting module applies precise axial pressure through a hydraulic press to ensure that the heated end of the bar becomes thicker and shorter. The flange extrusion forming module relies on a customized mold to push the steel to completely fill the cavity to form the flange, ensuring the flange forming quality and process continuity. This enables unified collaborative control of multiple processes in the forklift half-shaft production line, replacing manual operation and reducing errors. Furthermore, the joint control risk warning module identifies potential hazards in each piece of equipment in advance and generates warnings, effectively ensuring the continuous safe and stable operation of the production line and significantly reducing the difficulty of production line operation supervision for forklift half-shaft production.

[0028] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0029] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-process integrated control system for forklift half-shaft production lines, characterized in that, It includes an induction cooker heating module, a bar end upsetting module, a flange extrusion molding module, a robotic arm transfer module, a multi-process joint control module, and a production line monitoring center; The multi-process joint control module controls the induction cooker heating module, the bar end upsetting module, the flange extrusion forming module, and the robotic arm transfer module, and sends the control information and the operation monitoring information of each equipment module to the production line monitoring center. The robotic arm transfer module fixes the pre-treated bar and places it into the induction cooker heating module. The induction cooker heating module heats one end of the pre-treated bar through electromagnetic heating. Once the bar is heated to the required temperature, the robotic arm transfer module transfers the bar to the bar end upsetting module. The bar end upsetting module applies axial pressure to the heated end of the bar using a hydraulic press, causing the heated end of the bar to undergo plastic deformation. The robotic arm transfer module transfers the upset bar stock to the flange extrusion forming module. The flange extrusion forming module uses an extruder to extrude the upset section of the bar stock, causing the steel to flow plastically within the custom mold of the extruder, completely filling the mold cavity to form the initial shape of the flange. The multi-process joint control module communicates with the joint control risk early warning module. The joint control risk early warning module analyzes the operation and management performance of each piece of equipment and determines whether to generate a joint control early warning signal. When a joint control early warning signal is generated, it is sent to the production line monitoring center. The specific analysis process of the joint control risk early warning module includes: By capturing and analyzing the evaluation, it is determined whether there are maintenance difficulties parameters for the corresponding module equipment within a unit time. If maintenance difficulties parameters exist for the corresponding module equipment within a unit time, the corresponding module equipment is marked as a non-favorable cooperation device. If maintenance difficulties parameters do not exist for the corresponding module equipment within a unit time, normalization analysis is performed to determine whether the corresponding module equipment is a non-favorable cooperation device. If a non-favorable cooperation device exists, a joint control early warning signal is generated. The joint control risk warning module communicates with the production line operation effect output module. If no joint control warning signal is generated within a unit of time, the production line operation effect of the production line used for forklift half shaft production within a unit of time is analyzed through the production line operation effect output module. The analysis is used to determine whether to generate a production line operation efficiency warning signal. When a production line operation efficiency warning signal is generated, it is sent to the production line monitoring center. The specific analysis process of the production line operation effect output module is as follows: If the scrap rate of the forklift half shaft exceeds the preset scrap rate threshold, a production line operation efficiency warning signal is generated; if the scrap rate of the forklift half shaft does not exceed the preset scrap rate threshold, the production line operation interruption detection frequency value and production line operation interruption detection time value are compared with the preset production line operation interruption detection frequency threshold and preset production line operation interruption detection time threshold respectively. If the production line operation interruption detection frequency value or the production line operation interruption detection time value exceeds the corresponding preset threshold, a production line operation efficiency warning signal is generated. If the production line operation interruption detection frequency value and the production line operation interruption detection time value do not exceed the corresponding preset threshold, the efficiency fluctuation value and efficiency buffer value are compared with the preset efficiency fluctuation threshold and the preset efficiency buffer threshold respectively. If the efficiency fluctuation value or efficiency buffer value exceeds the corresponding preset threshold, a production line operation efficiency warning signal is generated. The production line operation effect output module communicates with the isolation effectiveness assessment module. If no production line operation effect early warning signal is generated within a unit of time, the isolation effectiveness assessment module analyzes the degree of safety hazards that the production line brings to the surrounding space within a unit of time. The analysis determines whether to generate an isolation effectiveness early warning signal, and sends it to the production line monitoring center when an isolation effectiveness early warning signal is generated. The specific analysis process of the isolation effectiveness assessment module includes: The edge contour of the area involved in the production line is obtained and marked as the target contour. The area enclosed by the diffusion contour and the target contour is marked as the isolation area. If there is an isolation gap in the isolation area, an isolation effectiveness warning signal is generated. If there is no isolation gap in the isolation area, the isolation effectiveness anomaly measurement value of all locations in the isolation area is averaged to obtain the effectiveness feature value. If the effectiveness feature value exceeds the preset effectiveness feature threshold, an isolation effectiveness warning signal is generated. The specific methods for analyzing and determining the location of isolation vulnerabilities are as follows: The isolation effectiveness anomaly value is obtained by weighted summation of temperature hazard characteristic value, dust hazard characteristic value, noise hazard characteristic value and air hazard characteristic value. If the isolation effectiveness anomaly value exceeds the preset isolation effectiveness anomaly value threshold, the corresponding location is marked as the isolation vulnerability location of the isolation area.

2. The multi-process joint control system for forklift half-shaft production line according to claim 1, characterized in that, The specific process of capturing, parsing and evaluating is as follows: obtain the duration of the corresponding parameter being in a non-matching state within a unit time and calculate the ratio with the total working time of the corresponding device module within a unit time to obtain the parameter matching time value; and obtain the number of times the recovery anomaly symbol QK-1 corresponding to the corresponding parameter is assigned within a unit time and mark it as the recovery anomaly standard frequency value. If the parameter matches an out-of-time value or recovers an abnormal standard frequency value exceeding the corresponding preset threshold, the corresponding parameter will be marked as a maintenance difficulty parameter for the corresponding module device.

3. The multi-process joint control system for forklift half-shaft production line according to claim 2, characterized in that, The specific analysis process for performing normalization analysis is as follows: The first feature value and the second feature value are weighted and summed to obtain the feature weight value. The feature weight value of the corresponding parameter is multiplied by the corresponding preset weight value to obtain the feature influence value. The feature influence values ​​of all parameters that the corresponding device module needs to monitor are summed to obtain the feature comprehensive coefficient. If the feature comprehensive coefficient exceeds the preset feature comprehensive coefficient threshold, the corresponding module device is marked as a non-advantageous device.

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