A method for controlling the production process of aluminum trim locking components

Abstract

This invention relates to the field of production management technology and discloses a method for controlling the production process of aluminum decorative locking components. The method includes: analyzing production orders to generate a process chain containing sealing, temporary storage in a humid environment, and threaded locking assembly; configuring production site resources, setting the temporary storage area as a buffer station resource with humidity indicators and capacity limitations; establishing a time mapping mechanism to determine the dynamic response relationship between the actual operation time of the locking assembly process and the waiting time after sealing and the environmental humidity index, reflecting the influence of oxide film hydration product evolution on tribological performance; and performing global production scheduling using waiting time and resource allocation as adjustment factors, outputting execution instructions containing specified temporary storage resources and assembly target cycle parameters. This invention solves the problem of cycle instability caused by tribological performance fluctuations by incorporating the evolution of material surface state into the production scheduling logic, thus achieving precise production instructions.

Description

Technical Field

[0001] This invention relates to the field of production management technology, and more specifically, to a method for controlling the production process of aluminum decorative locking components. Background Technology

[0002] Aluminum alloy exterior parts typically require passivation (e.g., anodizing) and sealing to achieve the desired corrosion resistance and decorative appearance. These surface-treated aluminum parts are then assembled with brackets or bases using threaded fasteners (such as steel screws) to form the final locking assembly. In existing industrial production control systems, production planning and scheduling systems usually establish work schedules for each process based on standard operating procedures (SOPs). For passivation and sealing processes, the system typically focuses on the compliance of process parameters; while for subsequent locking assembly processes, the system usually treats the assembly time of a single product as a fixed constant (standard time). Scheduling logic often arranges production cycles based on this fixed standard time, without considering the dynamic relationship between preceding processes and subsequent assembly. However, the surface state of the passivation film is not completely static after the sealing process. Porous alumina structures undergo a continuous hydration evolution process after sealing; the hydrates within the pores age or densify at the microstructural level over time and under the influence of the storage environment (especially humidity). This material-level evolution directly alters the tribological properties of aluminum surfaces. During the tightening process of steel-aluminum threaded pairs, fluctuations in the coefficient of friction significantly affect the torque-angle conversion relationship. In automated or semi-automated tightening operations, tightening tools are typically set with a fixed target torque. When the coefficient of friction at the contact interface changes due to variations in storage time after sealing or ambient humidity, the rotation angle required to reach the target torque also changes, leading to deviations in actual tightening time from the preset standard time. Existing control methods ignore this time drift caused by material surface evolution, treating the waiting period between sealing completion and assembly start as merely a logistical stagnation, rather than an active process affecting product processing performance. Therefore, when work-in-process storage time or ambient humidity fluctuates on the production floor, production scheduling instructions generated using fixed time logic often do not match actual execution, easily leading to assembly station cycle imbalances, task backlogs, or decreased consistency in tightening axial force, making it difficult to meet the control requirements of high-precision manufacturing. Summary of the Invention

[0003] This invention provides a method for controlling the production process of aluminum decorative locking components, which solves the technical problems mentioned in the background art.

[0004] This invention provides a method for controlling the production process of aluminum trim locking components, including:

[0005] The production order is parsed and the corresponding process operation chain is generated. The process operation chain includes at least the sequentially executed hole sealing process, the humid and hot environment temporary storage process, and the thread locking assembly process.

[0006] Configure production site resource data and set the storage area used to perform the hot and humid environment temporary storage process as a buffer station resource with environmental humidity index and carrying capacity limit;

[0007] A correlation mechanism between surface film state and processing cycle is established, and the dynamic response relationship between the actual operation time of the thread locking assembly process, the waiting time after the completion of the sealing process, and the environmental humidity index of the buffer station resources is established; the dynamic response relationship is based on the influence of the saturated growth of oxide film hydration products on the friction performance of the thread pair contact surface.

[0008] Using the waiting time and the allocation of buffer station resources as production scheduling adjustment factors, and combining the association mechanism, a production scheduling scheme is generated, and an execution instruction containing the target cycle time parameters of the buffer station resources specified for the process chain and the corresponding thread locking assembly process is output.

[0009] The beneficial effects of this invention include: By treating the humid and hot environment temporary storage process as a buffer resource node with environmental regulation attributes and capacity limitations, and by constructing a correlation mechanism between the surface film state and the processing cycle, this invention effectively solves the problem of assembly cycle distortion caused by oxide film hydration evolution and environmental humidity fluctuations under traditional control modes. This invention can accurately quantify the actual operation time of the thread locking assembly process based on the dwell time after the sealing process and storage environment indicators, and generate execution instructions containing temporary storage area allocation and target cycle parameters accordingly. This achieves deep coupling between production planning and the natural evolution law of the material surface, thereby significantly improving the stability of the production cycle and resource utilization efficiency of the locking assembly, ensuring the consistency of assembly axial force, and avoiding workstation congestion or resource conflicts caused by deviations in time estimation. Attached Figure Description

[0010] Figure 1 This is a flowchart of a production process control method for an aluminum trim locking assembly product according to the present invention;

[0011] Figure 2 This is a schematic diagram illustrating a specific implementation of the present invention. Detailed Implementation

[0012] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0013] like Figure 1 As shown, a method for controlling the production process of an aluminum trim locking assembly includes:

[0014] The production order is parsed and the corresponding process operation chain is generated. The process operation chain includes at least the sequentially executed hole sealing process, the humid and hot environment temporary storage process, and the thread locking assembly process.

[0015] Configure production site resource data and set the storage area used to perform the hot and humid environment temporary storage process as a buffer station resource with environmental humidity index and carrying capacity limit;

[0016] A correlation mechanism between surface film state and processing cycle is established, and the dynamic response relationship between the actual operation time of the thread locking assembly process, the waiting time after the completion of the sealing process, and the environmental humidity index of the buffer station resources is established; the dynamic response relationship is based on the influence of the saturated growth of oxide film hydration products on the friction performance of the thread pair contact surface.

[0017] Using the waiting time and the allocation of buffer station resources as production scheduling adjustment factors, and combining the association mechanism, a production scheduling scheme is generated, and an execution instruction containing the target cycle time parameters of the buffer station resources specified for the process chain and the corresponding thread locking assembly process is output.

[0018] Preferably, parsing the production order and generating the corresponding process operation chain includes:

[0019] Based on the following batch calculation formula, any one of the production orders... Disassembled into Minimum production batch unit:

[0020]

[0021] In the formula, This represents the total order demand data for the aforementioned production orders; This represents the preset standard batch quantity parameter; This represents the rounding up operation;

[0022] Based on the following set mapping formula, a task set for the process operation chain is generated for each of the minimum scheduleable batch units. :

[0023]

[0024] In the formula, Represents the union operation of sets; The index representing the sequence number of the smallest production batch unit, and ; Representative regarding the first The sealing process generated by the minimum production batch unit; Representative regarding the first The humid and hot environment temporary storage process generated by the minimum production batch unit is defined as a mandatory independent operation node; Representative regarding the first The thread-locking assembly process is generated by the smallest production batch unit described above.

[0025] The total order demand data represents the total quantity of aluminum decorative locking components explicitly required in the customer's order. This can be entered into the enterprise's order management system or obtained directly from the customer's ordering platform.

[0026] The standard single-batch quantity parameter is a preset minimum production batch quantity for splitting orders. It is preferably 50 to 200 pieces to meet the maximum single-time processing capacity of oxidation tanks and sealing tanks on the production floor, the reasonable operating cycle range of assembly stations, and the controllable range of work-in-process inventory.

[0027] The total number of batches is the total number of production batches obtained by rounding up the total order demand by the standard batch quantity.

[0028] The batch sequence index is a sequential number used to distinguish each smallest available production batch. It is preferably a consecutive positive integer starting from 1 to facilitate end-to-end batch traceability and resource allocation management.

[0029] The process operation chain task set is a combination of processes, including hole sealing, temporary storage in a humid and hot environment, and thread locking assembly, configured for each minimum schedulable batch.

[0030] The humid and hot environment temporary storage process is defined as an independent work node occupying buffer station resources because the oxide film on the surface of the aluminum trim parts after sealing will continue to undergo a hydration reaction. This reaction is affected by the humidity and time of the temporary storage environment, which is related to the friction performance and operation time of the subsequent locking assembly. In traditional production scheduling, temporary storage is only regarded as a logistics stagnation link. This solution clearly defines it as an independent work node, which is a prerequisite for realizing subsequent hydration status control and accurate calculation of working hours. For example, after an order is broken down into 6 batches, the process chain of each batch includes a temporary storage process that immediately enters the designated buffer station after the sealing process, and then enters the locking assembly, ensuring that the temporary storage conditions of each batch are traceable and controllable.

[0031] Orders are broken down into smallest achievable production batches, and independent process chains are generated for each batch because different batches may have different storage times and buffer workstations. Independent process chains enable precise matching of storage conditions and assembly time for each batch. For example, if the total order demand is 1200 units and the standard batch quantity is 200 units, it can be broken down into 6 batches. Each batch has its own dedicated process chain, which can be allocated different buffer workstations and scheduling times to avoid interference between batches.

[0032] The determination of the standard single-batch quantity parameter needs to consider both production resource capacity and process stability: if the oxidation tank can process a maximum of 200 pieces at a time, then the upper limit is set to 200 pieces; if the assembly station achieves the most stable cycle time with 100 pieces per batch, then it can be set to 100 pieces. Simultaneously, it must meet the requirement that work-in-process inventory does not exceed 24 hours of production. For example, if a factory's oxidation tank has a single-batch processing capacity of 150 pieces, and the optimal cycle time for the assembly station corresponds to 100 pieces per batch, then the standard single-batch quantity can be set to 100 pieces.

[0033] The matching rules between the minimum production batch unit and production resources are as follows: oxidation tanks and sealing tanks are allocated sequentially according to batch number to ensure the continuity of resources for preceding and following processes within the same batch; buffer stations are allocated according to the waiting time requirements in the production schedule, with priority given to batches with longer waiting times and stable humidity buffer stations. For example, if batch 1 needs to wait 8 hours after sealing before assembly, it is prioritized to be allocated a buffer station with humidity fluctuation ≤5%, while batch 2 can be allocated any available buffer station immediately after sealing.

[0034] Preferably, the production site resource data is configured, and the storage area used to perform the temporary storage process in the humid and hot environment is set as a buffer workstation resource with environmental humidity index and carrying capacity limit, including:

[0035] Based on the following resource set definition formula, establish the set of buffer workstation resources. :

[0036]

[0037] In the formula, This represents the set of buffer workstation resources; Represents a single buffer workstation resource in the set; This represents the total number of buffer workstation resources;

[0038] For set Each of the aforementioned buffer workstation resources Define the following static property parameters:

[0039]

[0040] In the formula, Represents the buffer workstation resources The environmental humidity index is expressed in terms of relative humidity percentage. Represents the buffer workstation resources The carrying capacity limit is expressed in terms of the number of the minimum production batch units that can be accommodated simultaneously.

[0041] The resource capacity constraint logic is defined based on the following inequality:

[0042]

[0043] In the formula, Represents any production schedule moment; Represents the moment Occupying the buffer workstation resources The cumulative work-in-process quantity of the smallest dispatchable batch unit that performs the hot and humid environment temporary storage process; This means it applies to all elements.

[0044] The buffer workstation resource set is a comprehensive classification of all available temporary storage areas on the production site.

[0045] A single buffer workstation resource is a specific, independent temporary storage area on the production floor. This can be achieved by physically inventorying the storage areas on the production floor and assigning them unique numbers.

[0046] The total number of buffer workstation resources is the total number of temporary storage areas that are actually available for use on the production floor. This number can be obtained by counting the number of independent temporary storage areas after inventory.

[0047] The environmental humidity index is a relative humidity percentage standard set for each buffer workstation resource. Monitoring data can be collected in real time using humidity sensors installed within the temporary storage area.

[0048] The capacity limit is the maximum number of minimum production batch units that a single buffer station resource can accommodate simultaneously. Preferably, it is 2 to 8 batches, based on the physical footprint of the temporary storage area, the space occupied by a single batch unit, and the ease of material handling.

[0049] The work-in-process quantity at time t is the cumulative total of the smallest batch units that can be scheduled to perform a temporary storage process in a hot and humid environment while occupying a certain buffer station resource at any production schedule time.

[0050] Any production plan time is a specific time node within the production scheduling cycle. A 1-minute interval is preferred, and the value is determined based on the matching degree between the time accuracy requirements of the production scheduling and the on-site process cycle time.

[0051] The storage area used for temporary storage in humid and hot environments is designated as a buffer station resource with environmental humidity indicators and capacity limits because the hydration reaction rate of the oxide film on the surface of the sealed aluminum parts is directly related to the environmental humidity. This hydration reaction alters the frictional properties of the oxide film, thus affecting subsequent tightening and assembly time. In traditional production management, temporary storage areas are merely considered temporary material storage spaces, not included in the formal production resource category, and lack clear environmental and capacity control standards. This solution standardizes them as buffer station resources with clearly defined attributes, making the environmental conditions and space occupancy of the temporary storage process quantifiable and schedulable controllable objects. For example, a production site may have three temporary storage areas, with environmental humidity indicators set at 30%, 50%, and 70%, and capacity limits of 4, 6, and 8 batches respectively. Each temporary storage area is included in the scheduling system as an independent buffer station resource.

[0052] The resource capacity constraint logic stipulates that the number of work-in-process items at any given time cannot exceed the carrying capacity limit. This is to avoid material accumulation, handling congestion, and disruption of environmental humidity uniformity caused by excessive occupation of the temporary storage area. For example, if the carrying capacity limit of a certain buffer station is 4 batches, and 4 batches of units are already temporarily stored in that area, the scheduling system will automatically allocate new temporary storage needs to other idle or unused buffer station resources to ensure a stable temporary storage environment and smooth operation.

[0053] The control accuracy requirement for the environmental humidity index is that the fluctuation range of the set value should not exceed ±5%. For example, when the environmental humidity index is set to 50%, the actual monitored humidity value should be between 45% and 55%. If it exceeds this range, the environmental conditioning equipment needs to be activated for correction.

[0054] The capacity limit is calculated by multiplying the floor area of ​​a single batch unit by the effective usable area of ​​the buffer station resource, and then reserving 10% of the material handling passage space. For example, if the effective usable area of ​​a buffer station resource is 20 square meters and the floor area of ​​a single batch unit is 2.2 square meters, the calculation yields 20 ÷ 2.2 ≈ 9.09. After reserving 10% of the space, the capacity limit is set to 8 batches.

[0055] The humidity level in the temporary storage area is monitored every minute. The monitoring data is uploaded to the production resource database in real time via sensors, serving as the basis for subsequent scheduling decisions and hydration rate calculations. If the monitoring data exceeds the control accuracy range three times consecutively, the system will issue an early warning and trigger the environmental adjustment process.

[0056] Preferably, a mechanism for linking the surface film state with the processing cycle is constructed, including:

[0057] Define the state variable of hydration filling degree. The minimum production batch unit is used to characterize the waiting time during which the production period ends. The degree of evolution of the surface physical state during the period;

[0058] Calculate the rate correction formula based on the selected buffer station resources. Equivalent hydration evolution rate in :

[0059]

[0060] In the formula, This represents the preset baseline hydration evolution rate parameter; This represents the preset humidity sensitivity parameter; Represents the buffer workstation resources The relative humidity percentage value;

[0061] Calculate the state variable of the degree of hydration filling based on the following first-order saturation dynamics formula. :

[0062]

[0063] In the formula, This represents the waiting time after the sealing process is completed; Represents the operation of the natural exponential function; This represents the saturation limit value of complete hydration.

[0064] The hydration filling degree state variable is a quantitative indicator that characterizes the effective occupation of passivation film pores and surface hydration products by the smallest dispensable batch unit during the waiting period.

[0065] The baseline hydration evolution rate parameter is a preset basic hydration reaction rate bound to the sealing route. It is preferably 0.01 to 0.05 per hour, which meets the basic hydration reaction rate range corresponding to different sealing processes (hot water sealing, medium temperature sealing, etc.), and is determined through statistical analysis of previous process experiments.

[0066] The humidity sensitivity coefficient is a quantitative coefficient characterizing the degree of influence of environmental humidity on the hydration evolution rate. It is preferably between 0.5 and 2.0, based on statistical analysis of the variation in hydration reaction rate under different humidity conditions, ensuring that the correction of the rate by humidity conforms to actual process dynamics.

[0067] The equivalent hydration evolution rate is the actual hydration reaction rate obtained by correcting the baseline hydration evolution rate for ambient humidity.

[0068] The waiting time is the time interval from when the smallest available production batch unit completes the sealing process to when the thread locking assembly process begins.

[0069] An exponential evolution model based on first-order saturation kinetics is established, using an exponential function to describe the relationship between the degree of hydration filling and the residence time. This is because the hydration reaction of the passivation film after sealing follows a pattern of rapid initial growth followed by saturation. Traditional techniques only know that the hydration reaction will occur, but do not quantify it. This approach uses this model to achieve macroscopic quantification of the material's microscopic evolution. For example, if a batch is stored in the buffer station for 0.5 hours, the equivalent hydration evolution rate is 0.03 per hour, and the hydration filling degree calculated by the model is approximately 14%. After 24 hours of residence, the hydration filling degree approaches 95%, which conforms to the actual saturation characteristics of the hydration reaction.

[0070] The baseline hydration evolution rate is corrected based on the humidity of the buffer station because the hydration reaction rate of the passivation film is directly related to the ambient humidity; the higher the humidity, the faster the hydration products are formed. This scheme introduces a humidity sensitivity coefficient to transform the influencing factor of ambient humidity into a rate correction term, enabling accurate calculation of the hydration rate under different humidity conditions. For example, if the baseline hydration evolution rate is 0.02 per hour, the humidity of a certain buffer station is 70%, and the humidity sensitivity coefficient is 1.2, the calculated equivalent hydration evolution rate is 0.02 × (1 + 1.2 × 70 ÷ 100) = 0.0368 per hour, accurately reflecting the promoting effect of humidity on the hydration rate.

[0071] The initial range of the baseline hydration evolution rate parameter needs to be determined in conjunction with the specific sealing process: the preferred value for hot water sealing process is 0.03 to 0.05 per hour, for medium temperature sealing process it is 0.02 to 0.04 per hour, and for room temperature rapid sealing process it is 0.01 to 0.03 per hour.

[0072] The humidity sensitivity coefficient is the factor by which the hydration evolution rate changes relative to a unit change in humidity (each 1% increase in relative humidity). For example, when the humidity sensitivity coefficient is 1.0, the hydration evolution rate increases by 10% for every 10% increase in humidity. Its value needs to be determined through multiple sets of humidity gradient tests to ensure coverage of the possible humidity range (30% to 70% relative humidity) at the production site.

[0073] The saturation threshold 1 of the hydration filling degree state variable corresponds to a microscopic state where the pores of the passivation film are completely filled with hydration products, forming a continuous and dense hydration layer on the surface. At this point, the hydration reaction essentially stops, the friction coefficient tends to stabilize, and this can be verified by observing the pore filling and testing the friction coefficient using electron microscopy.

[0074] Preferably, the actual working time of the thread-locking assembly process and the waiting time after the sealing process are established include:

[0075] Calculate the corresponding waiting time based on the following linear mapping formula. and the selected buffer workstation resources Equivalent friction coefficient variable :

[0076]

[0077] In the formula, This represents the preset baseline friction coefficient parameter; Represents the preset friction evolution amplitude parameter; The state variable representing the degree of hydration filling;

[0078] Calculate the actual working time of the thread-locking assembly process based on the following time function formula. :

[0079]

[0080] In the formula, This represents the preset reference time parameter for the non-tightening action; This represents the preset torque parameters for this order; The tool torque-angle conversion factor represents the conversion factor of the equipment performing the process; The station angular velocity parameter represents the equipment performing this process; This represents the equivalent friction coefficient variable calculated in the previous step.

[0081] The baseline friction coefficient parameter is the initial friction coefficient between the passivation film on the aluminum trim surface and the contact surface of the threaded fastener immediately after the sealing process is completed. It is preferably between 0.15 and 0.35, which meets the measured statistical range of the oxide film friction coefficient after common sealing processes (hot water sealing, room temperature rapid sealing, etc.) and covers the initial friction state in most production scenarios.

[0082] The friction evolution amplitude parameter is a quantitative indicator characterizing the influence of the hydration filling degree state variable on the equivalent friction coefficient. It is preferably between 0.05 and 0.20, based on statistics of the maximum change in the friction coefficient during the growth of hydration products, ensuring it closely matches the evolution range of friction performance in actual processes.

[0083] The equivalent friction coefficient variable is the actual friction coefficient of the threaded pair contact surface after taking into account the influence of the baseline friction coefficient and the degree of hydration filling.

[0084] The reference time parameter for non-tightening actions is the cumulative time consumed by fixed actions unrelated to friction, such as feeding, positioning, and tool retraction, during the thread locking assembly process. It can be collected by continuously recording the non-tightening action times of 100 assembly cycles and taking the average value.

[0085] The torque parameter is set for a specific production order, representing the target torque value required for the threaded locking assembly process. This can be obtained by reading the torque standards from the order's process requirements or product design documents.

[0086] The tool torque-angle conversion factor is the proportional coefficient by which assembly equipment converts rotation angle into output torque, reflecting the equipment's torque-angle response characteristics. It can be obtained by performing torque-angle curve tests on standard test specimens and fitting the slope of the linear segment.

[0087] The station angular velocity parameter is the fixed rotational angular velocity of the tightening tool at the assembly station. It can be acquired by reading the setting parameters of the tightening tool controller or by directly measuring the tool's rotational speed.

[0088] The actual operation time of the locking assembly is the total time spent on the thread locking assembly process, which consists of fixed non-tightening action time and variable tightening action time.

[0089] The equivalent friction coefficient variable is set as the linear superposition of the product of the baseline friction coefficient parameter, the friction evolution amplitude parameter, and the hydration filling degree state variable. This is because the influence of the growth of hydration products of the passivation film after sealing on the friction coefficient exhibits an approximately linear relationship within the mass production process window. Traditional technologies only know that hydration reactions change frictional properties, but have not established this quantitative linear mapping relationship. This scheme achieves a precise conversion from the microscopic state of the material to its macroscopic frictional properties through this model. For example, if the baseline friction coefficient is 0.25, the friction evolution amplitude is 0.12, and the hydration filling degree of a certain batch is 0.6, then the equivalent friction coefficient is 0.25 + 0.12 × 0.6 = 0.322, accurately reflecting the promoting effect of hydration state on friction.

[0090] The actual operation time of the locking assembly is broken down into fixed non-tightening action time and variable tightening action time. It is clearly stated that the tightening action time is directly proportional to the set torque and inversely proportional to the product of the tool torque angle conversion coefficient, the station angular velocity, and the equivalent friction coefficient. This is because the tightening action overcomes the friction of the threaded pair to achieve the target torque, and its time is directly affected by the friction state. For example, if the set torque is 5 Nm, the tool torque angle conversion coefficient is 0.8 Nm per radian, the station angular velocity is 2 radians per second, and the equivalent friction coefficient is 0.3, then the tightening action time is 5 ÷ (0.8 × 2 × 0.3) ≈ 10.42 seconds. Combining this with the fixed non-tightening action time of 3 seconds, the total operation time is approximately 13.42 seconds, achieving accurate calculation of the working time.

[0091] The specific measurement method for the tool torque-angle conversion coefficient is as follows: Select a standard friction sample, install it on the assembly station, start the tightening tool, and record the real-time torque and rotation angle data from contact to reaching the set torque. Extract the linear segment of the torque-angle curve, and calculate the slope of this segment, which is the tool torque-angle conversion coefficient. The calibration cycle is once a month to ensure the accuracy of the coefficient.

[0092] The test environment conditions for the baseline friction coefficient parameters are: temperature controlled between 23 and 27 degrees Celsius, relative humidity controlled between 45% and 55%, and threaded fasteners consistent with those used in actual production are used during the test to avoid the influence of environmental and tool differences on the test results.

[0093] The rule for determining the positive or negative value of the friction evolution amplitude parameter is as follows: if the growth of hydration products leads to an increase in the friction coefficient through preliminary process experiments, the value is positive; if, under a specific sealing process (such as certain room temperature rapid sealing processes), the hydration products reduce the friction coefficient, the value is negative. In actual production, the friction evolution amplitude corresponding to most sealing processes is positive, and the value is preferentially taken within the positive range. For special processes, the positive or negative value is determined through experiments.

[0094] Preferably, a dynamic response relationship is established between the environmental humidity indicators of the buffer station resources. This dynamic response relationship is based on the influence of the saturated growth of oxide film hydration products on the frictional performance of the threaded contact surface, including:

[0095] Define time threshold sequence The aforementioned waiting time Discretize the domain as An ordered discrete interval of waiting time:

[0096]

[0097] In the formula, Representing the The time threshold; the first The discrete interval of the waiting time is defined as follows: ;

[0098] Construct a discrete time lookup table For any of the aforementioned buffer workstation resources and any interval number The table entries are generated based on the following pre-calculation formula:

[0099]

[0100] In the formula, Representing the The midpoint or a preset representative time of the discrete interval of the waiting time; This represents the function of the time calculation model;

[0101] Implement step mapping logic: if the aforementioned waiting time... satisfy The actual working time of the threaded locking assembly process is then... The judgment is as follows:

[0102]

[0103] The time threshold sequence is a set of monotonically increasing values ​​used to divide the continuous domain of dwell time. The preferred values ​​are 0, 2, 8, 24, and 72 hours, based on the phased characteristics of the hydration reaction after pore sealing, where the initial reaction is rapid and tends towards saturation in the later stages. This sequence can cover the main evolutionary phases.

[0104] The number of discrete intervals for waiting time is a preset number of waiting time segments. Five intervals are preferred to balance calculation accuracy and scheduling efficiency; too many intervals will increase the computational load, while too few will reduce the accuracy of time matching.

[0105] The target time for each interval is a characteristic time selected within each discrete interval of waiting time for pre-calculating working hours. Ideally, it should be the midpoint of each interval, as the midpoint reflects the average level of hydration within the interval, reducing bias caused by a single moment.

[0106] The discrete time lookup table entries are pre-calculated values ​​of the actual working time for thread locking assembly, based on specific buffer station resources and discrete intervals of waiting time.

[0107] The target operation time is the final execution time of the thread locking assembly process, which is obtained by matching the discrete time lookup table based on the discrete interval to which the waiting time belongs.

[0108] A segmented step mapping mechanism for waiting time is constructed because the dynamic response relationship between waiting time and locking time is non-linear, and directly incorporating it into the scheduling model would lead to complex and inefficient solutions. Traditional techniques do not consider this non-linear solution challenge. This solution discretizes continuous duration into finite intervals and pre-calculates the time entries for each interval, transforming the non-linear relationship into a discrete mapping that can be quickly queried, thus adapting to the computational requirements of the scheduling system. For example, the time threshold sequence is set to 0, 2, 8, 24, and 72 hours, forming 5 discrete intervals. For each interval and each buffer workstation, the time at the midpoint is pre-calculated as an entry. During scheduling, there is no need to calculate the non-linear function in real time; the entries can be directly matched.

[0109] The step-locking logic, which determines the working time based on the time spent in a waiting period within a certain range, is used to quickly determine the working time and efficiently execute the scheduling. For example, if a batch of holes is sealed and then left for 5 hours, falling within the 2-8 hour range, the corresponding discrete working time lookup value for that range is directly retrieved. This avoids frequent changes in working time due to small fluctuations in duration, ensuring the stability of the scheduling plan.

[0110] The time threshold sequence is set based on hydration kinetics test data: 0 hours corresponds to the moment immediately after sealing, 2 hours to the end of the rapid growth phase of the hydration reaction, 8 hours to the point where the reaction rate slows down significantly, 24 hours to the early stage of near saturation, and 72 hours to the state of near saturation. For example, experiments have shown that under most sealing processes, the hydration filling degree exceeds 90% after 24 hours, and the change in time is less than 5%. Therefore, 72 hours is set as the last threshold.

[0111] The standard for selecting the midpoint of the target time interval is: for any interval, the degree of hydration at the midpoint deviates from the average degree of hydration at all times within the interval by no more than 3%. For example, the midpoint of the 2- to 8-hour interval is 5 hours. The degree of hydration at this time deviates very little from the average level at any time within the 2- to 8-hour interval, which can ensure the accuracy of the pre-calculated working hours.

[0112] The discrete time lookup table is updated monthly. It must be updated immediately if there are changes in the sealing process, humidity control accuracy at buffer stations, or threaded fastener specifications. During the update, the pre-calculation process is re-executed to ensure that the table values ​​match the current production conditions, avoiding time mismatch due to process changes.

[0113] Preferably, the production scheduling scheme is generated by using the waiting time and the allocation of buffer workstation resources as production scheduling adjustment factors, combined with the correlation mechanism, and outputting an execution instruction containing the target cycle time parameters of the buffer workstation resources specified for the process chain and the corresponding thread locking assembly process, including:

[0114] Construct the following global optimization objective function and solve it using a mixed-integer linear programming solver:

[0115]

[0116] In the formula, Represents the maximum completion time across all described process chains; Represents the preset minimum weight disambiguation constant; Representing binary decision variables, indicating the task In resources At the moment start; Represents the variable Preset unique lexicographical encoding coefficients;

[0117] Based on the optimal binary decision variables obtained from the solution Determine each of the minimum production batch units. The only buffer station resource allocated to the hot and humid environment temporary storage process. Generate temporary planning instructions:

[0118]

[0119] In the formula, This represents the set of buffer workstation resources; The task identifier represents the temporary storage process in the hot and humid environment;

[0120] Based on the optimal start time obtained from the solution, calculate the optimal waiting time. And based on the step locking logic, determine the discrete interval of the stuck waiting time that was hit. Extract the corresponding target beat parameters :

[0121]

[0122] In the formula, This represents the discrete working hours lookup table entry.

[0123] The maximum completion time is the end time of the latest process in the entire process chain.

[0124] The disambiguation constant is a preset, minimal weighting coefficient used to ensure the uniqueness of the scheduling solution. It is preferably 10 to the power of negative six multiplied by the scheduling time granularity, thus not affecting the optimization effect of the primary objective, while effectively distinguishing equivalent optimal solutions and avoiding execution ambiguity caused by multiple solutions.

[0125] The lexicographically ordered coding coefficients are unique integer codes assigned to each task-resource-time binary decision variable. They are generated in ascending order of task number, resource number, and time number to ensure that each variable is uniquely coded and to provide explicit weights for the disambiguation penalty term.

[0126] Binary decision variables are variables that characterize whether a task should be executed at a specific time for a specific resource. Preferably, they are 0 or 1, where 0 indicates no execution and 1 indicates execution, thus meeting the variable domain requirements of mixed-integer linear programming.

[0127] The optimal buffer station resource is obtained through optimization and is the only temporary storage area allocated to the smallest available production batch unit to perform the hot and humid environment temporary storage process.

[0128] The optimal waiting time is determined by optimization, which is the optimal time interval from when the smallest available production batch unit completes the sealing process to when the thread locking assembly begins.

[0129] The hit interval number is the discrete interval number of the waiting time to which the optimal waiting time belongs.

[0130] The target cycle time parameter is extracted from the discrete time lookup table based on the hit interval sequence number and is issued to the assembly station for the execution time of the thread locking assembly process.

[0131] The global optimization objective function, consisting of a linearly weighted sum of the primary objective term and a disambiguation penalty term, is constructed because traditional optimization objectives only focus on minimizing the production cycle, which may result in multiple equivalent optimal scheduling schemes, leading to ambiguity during on-site execution. This scheme introduces a disambiguation penalty term based on lexicographical encoding to ensure that only a unique scheduling scheme is output after solving, without affecting the primary objective. For example, when two scheduling schemes both have a maximum completion time of 48 hours, the scheme with the smaller total encoding sum is selected as the final solution through lexicographical weighted calculation, avoiding execution confusion.

[0132] The time-indexed mixed-integer linear programming model incorporates constraints such as temporary resource allocation, waiting time control, and dynamic working time into a unified framework, breaking through the limitations of traditional scheduling models that only focus on processing equipment scheduling. For example, the model simultaneously considers resource constraints of oxidation tanks, sealing tanks, temporary storage areas with different humidity levels, and assembly stations, as well as the temporal relationships of each batch, the correlation between dynamic working time and waiting time, and achieves overall optimization of resources throughout the entire process.

[0133] The optimization solver engine obtains the optimal solution in a single run, avoiding the fluctuations in the solution caused by multiple iterations and ensuring the stability and authority of scheduling commands. For example, the solver processes all constraints and objectives at once, outputting the optimal resource allocation and start time.

[0134] The specific logic for determining the disambiguation constant is as follows: when the scheduling time granularity is set to 1 minute, the disambiguation constant is 1e-6; when the time granularity is 5 minutes, it is 5e-6, ensuring that the weight of the disambiguation penalty term is much smaller than the main objective and does not interfere with the overall cycle optimization. For example, if the time granularity is 1 minute and the coding coefficient of a certain variable is 100, its penalty term contribution is 100×1e-6=1e-4, which is much smaller than the 1-minute time unit and does not affect the main objective.

[0135] The rule for generating lexicographically ordered coding coefficients is as follows: first, sort by task type (sealing, temporary storage in humid and hot environments, threaded locking assembly, inspection), then sort by resource number in ascending order, and finally sort by time sequence number in ascending order, assigning consecutive integers in sequence. For example, the coding coefficient for Task 1-Resource 2-Time 3 is 5, and the coding coefficient for Task 1-Resource 2-Time 4 is 6, ensuring that the coding is unique and ordered.

[0136] The specific type of solver to optimize can be a commercial solver such as GUROBI or CPLEX, or an open-source solver such as COIN-OR, depending on the company's existing software resources, to ensure solver efficiency and stability.

[0137] The solution termination condition is set to zero optimality gap, meaning that a globally optimal solution must be found, approximate solutions are not accepted, and the optimality of the scheduling scheme is ensured. For example, the solver stops when no better solution is found, and the output scheme is the theoretically optimal solution.

[0138] Preferably, the reference hydration evolution rate parameter, the humidity sensitivity coefficient parameter, the baseline friction coefficient parameter, and the friction evolution amplitude parameter are determined through the following calibration procedure:

[0139] right Groups covering different relative humidity and different waiting times Standard tests were performed on the standard test specimens to obtain the corresponding measured torque coefficient for each group. ;

[0140] Based on the linear prior, let the first... Measured equivalent friction coefficient of the sample And calculate the first according to the following normalization formula. Measured hydration filling degree of the sample :

[0141]

[0142] In the formula, and Represent The minimum and maximum measured equivalent friction coefficients in the set of data;

[0143] The baseline friction coefficient parameter is determined by fitting the following linear regression equation. With the friction evolution amplitude parameter :

[0144]

[0145] Based on the following nonlinear regression equation, the baseline hydration evolution rate parameter is determined by least squares fitting. With the humidity sensitivity parameter :

[0146]

[0147] The number of test groups is the preset total number of combinations of ambient humidity and waiting time used to calibrate parameters. Preferably, it is 12 groups, thus covering 3 common humidity levels and 4 typical waiting time points, ensuring that the data can reflect the evolution of hydration and friction under different operating conditions.

[0148] The humidity of the m-th test group is the controlled relative humidity set in the m-th calibration experiment. Preferably, it is 30%, 50%, or 70%, which meets the common humidity range of temporary storage areas in the production site and can cover the possible environmental conditions in actual production.

[0149] The waiting time for the m-th test group is the controlled residence time of the standard test sample after sealing the hole and before testing in the m-th calibration experiment. It is preferably 0.5 hours, 2 hours, 8 hours, or 24 hours, thus covering the critical stages of the hydration reaction, including the initial rapid evolution and the later saturation process.

[0150] The measured torque coefficient of group m is the correlation coefficient between the torque and clamping force obtained after standard testing of the standard test sample in group m. It can be obtained by collecting torque and clamping force data in real time during the test using torque and clamping force testing equipment, and then calculating it according to the standard formula.

[0151] The measured equivalent friction coefficient of the m-th group is obtained by directly linearly mapping the measured torque coefficient of the m-th group, and is a quantitative index characterizing the friction performance of the threaded pair contact surface.

[0152] The minimum measured friction coefficient is the smallest among all the measured equivalent friction coefficients of all test groups.

[0153] The maximum measured friction coefficient is the largest value among all the measured equivalent friction coefficients of all test groups.

[0154] The measured hydration filling degree of group m is an index characterizing the degree of hydration products occupying the passivation film, obtained by normalizing the measured equivalent friction coefficient of group m.

[0155] The measured torque coefficient obtained from standard tests is directly linearly mapped to the measured equivalent friction coefficient because, in the steel-aluminum threaded pair locking scenario, the torque coefficient and the friction coefficient have a fixed correlation. This mapping is based on the threaded pair fastening mechanics principle and does not require the introduction of additional complex correction factors. In traditional technologies, the torque coefficient and the friction coefficient are independent evaluation indicators. This solution simplifies the conversion process from standard tests to model parameters through this direct mapping. For example, if a set of tests yields a measured torque coefficient of 0.28, it can be directly used as the measured equivalent friction coefficient.

[0156] The measured hydration filling degree is obtained by normalizing the minimum and maximum values ​​of the measured equivalent friction coefficient. This is because the hydration filling degree is a relative indicator, and normalization can eliminate systematic errors between different test batches, making the data comparable. For example, the minimum measured equivalent friction coefficient of 12 test groups is 0.18 and the maximum is 0.36. The measured value of a certain group is 0.27. After normalization, the measured hydration filling degree is (0.27-0.18)÷(0.36-0.18)=0.5, which directly reflects the hydration evolution degree of the sample group.

[0157] The reason for using linear regression and least-squares nonlinear regression to fit parameters based on different data types is that the baseline friction coefficient has a linear relationship with the friction evolution amplitude and the degree of hydration filling, while the benchmark hydration evolution rate and the humidity sensitivity coefficient follow an exponential evolution law. For example, linear regression is used to fit the measured equivalent friction coefficient and the measured hydration filling rate data to obtain the baseline friction coefficient and friction evolution amplitude; least-squares nonlinear regression is used to fit the exponential relationship between the measured hydration filling rate and humidity and waiting time to obtain the benchmark hydration evolution rate and humidity sensitivity coefficient, ensuring that the fitting results closely match the actual laws.

[0158] The preparation process parameters of the standard test sample must be consistent with those of the actual production: the passivation current density is 1.0 to 1.5 amperes per square decimeter, the oxidation time is 20 to 40 minutes, and the sealing process is the same as that used in production (e.g., the medium-temperature sealing temperature is 50 to 60 degrees Celsius, and the sealing time is 15 to 25 minutes) to ensure that the film characteristics of the sample are consistent with those of the actual production part.

[0159] The specific operating steps of the standard test are as follows: assemble the standard test sample with the actual production threaded fastener, set the tightening speed to 10 to 20 revolutions per minute, repeat the test 3 times for each sample, and take the average value of the torque coefficient as the measured torque coefficient of the group to avoid random errors in a single test.

[0160] The accuracy requirements for regression fitting are: the coefficient of determination for linear regression should be no less than 0.9, and the coefficient of determination for nonlinear regression should be no less than 0.85, ensuring that the fitted parameters accurately reflect the data patterns. For example, a coefficient of determination of 0.92 obtained from linear regression indicates that the baseline friction coefficient and friction evolution amplitude can explain 92% of the measured equivalent friction coefficient variation.

[0161] The rule for removing outlier data is as follows: using the three-standard-deviation method, when the measured equivalent friction coefficient of a certain set of data exceeds the range of the mean of all data plus or minus three standard deviations, it is judged as outlier data and removed, and then the fitting calculation is re-performed to avoid outliers affecting the accuracy of parameters.

[0162] like Figure 2 As shown, Figure 2 The invention demonstrates the workstation layout and resource allocation logic of the aluminum trim locking assembly production process, presenting a sequentially connected sealing treatment area, a humid and hot environment temporary storage area, and a thread locking assembly station. The humid and hot environment temporary storage area serves as a buffer station resource with environmental humidity index control, equipped with a humidity sensor to collect environmental humidity data in real time. The orderly arrangement of the three areas and the humidity monitoring function of the temporary storage area together provide a foundation for establishing a dynamic response relationship between waiting time, environmental humidity, and locking assembly time, supporting the accurate adaptation of production scheduling to the hydration evolution law of the material surface.

[0163] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A method for controlling the production process of aluminum decorative locking components, characterized in that, include: The production order is parsed and the corresponding process operation chain is generated. The process operation chain includes at least the sequentially executed hole sealing process, the humid and hot environment temporary storage process, and the thread locking assembly process. Configure production site resource data and set the storage area used to perform the hot and humid environment temporary storage process as a buffer station resource with environmental humidity index and carrying capacity limit; A correlation mechanism between surface film state and processing cycle is established, and the dynamic response relationship between the actual operation time of the thread locking assembly process, the waiting time after the completion of the sealing process, and the environmental humidity index of the buffer station resources is established; the dynamic response relationship is based on the influence of the saturated growth of oxide film hydration products on the friction performance of the thread pair contact surface. Using the waiting time and the allocation of buffer station resources as production scheduling adjustment factors, and combining the association mechanism, a production scheduling scheme is generated, and an execution instruction containing the target cycle time parameters of the buffer station resources specified for the process chain and the corresponding thread locking assembly process is output.

2. The method for controlling the production process of an aluminum decorative locking assembly according to claim 1, characterized in that, Parse production orders and generate corresponding process operation chains, including: Obtain the total order demand data of the production order and the preset standard single batch quantity parameters; The total number of batches to be broken down is determined by rounding up the ratio of the total order demand data to the standard single batch quantity parameter, and the production order is broken down into the corresponding number of smallest schedulable batch units. For each of the minimum production batch units, an independent process operation chain is generated; In the generated process chain, the humid and hot environment temporary storage process is an independent operation node that occupies the buffer station resources, and its timing position is configured after the completion time of the hole sealing process and before the start time of the thread locking assembly process.

3. The method for controlling the production process of an aluminum decorative locking assembly according to claim 2, characterized in that, Configure production site resource data, and set the storage area used to perform the temporary storage process in the humid and hot environment as a buffer workstation resource with environmental humidity index and carrying capacity limit, including: Establish a database containing a set of buffer workstation resources that includes all available storage areas; For each buffer workstation resource in the buffer workstation resource set database, a relative humidity percentage value is set as the environmental humidity index, and the maximum number of the smallest production batch units that can be accommodated at the same time is set as the carrying capacity limit. A resource capacity constraint logic is constructed, stipulating that at any given time, the cumulative total number of the minimum production batch units allocated to each of the buffer workstations to perform the humid and hot environment temporary storage process shall not exceed the carrying capacity limit corresponding to the buffer workstation resource.

4. The method for controlling the production process of an aluminum trim locking assembly according to claim 3, characterized in that, Construct a correlation mechanism between surface film state and processing cycle, including: A hydration filling state variable was set to characterize the effective occupation of hydration products in the pores and surface of the passivation film, and a baseline hydration evolution rate parameter and a humidity sensitivity coefficient parameter were pre-calibrated. Based on the relative humidity percentage value corresponding to the buffer station resource, the baseline hydration evolution rate parameter is corrected, and the corresponding equivalent hydration evolution rate is calculated. An exponential evolution model based on first-order saturation dynamics is established, which establishes that the value of the state variable of the degree of hydration filling tends to saturate as the retention waiting time after the completion of the sealing process increases, and the rate of the growth relationship is determined by the equivalent hydration evolution rate.

5. The method for controlling the production process of an aluminum decorative locking assembly according to claim 4, characterized in that, The actual operation time of the thread-locking assembly process and the waiting time after the completion of the hole-sealing process are determined, including: Baseline friction coefficient parameters and friction evolution amplitude parameters are pre-calibrated, and a friction coefficient mapping model is constructed. Based on the friction coefficient mapping model, an equivalent friction coefficient variable is calculated. The equivalent friction coefficient variable is set as the linear superposition result of the product of the baseline friction coefficient parameters, the friction evolution amplitude parameters, and the hydration filling degree state variable. Pre-calibrate the reference time parameters for non-tightening actions, set the torque parameters, station angular velocity parameters, and tool torque-angle conversion coefficient, and construct a time calculation model; Based on the time calculation model, the actual working time of the thread locking assembly process is determined to consist of a first part and a second part; wherein, the first part is a fixed reference time parameter for the non-tightening action; the second part is the time consumed by the tightening action, which is directly proportional to the set torque parameter and inversely proportional to the product of the tool torque angle conversion coefficient, the station angular velocity parameter, and the equivalent friction coefficient variable.

6. The method for controlling the production process of an aluminum decorative locking assembly according to claim 5, characterized in that, The dynamic response relationship between the environmental humidity indicators of the buffer station resources is established. This dynamic response relationship is based on the influence of the saturated growth of oxide film hydration products on the friction performance of the threaded pair contact surface, including: A segmented step mapping mechanism is constructed for the aforementioned waiting time. A set of monotonically increasing time threshold sequences is pre-defined to divide the continuous domain of the waiting time into several preset discrete intervals of waiting time. For each discrete interval of the waiting time and each buffer workstation resource, a target representative time within the discrete interval of the waiting time is selected, and the working time calculation model and the friction coefficient mapping model are called to pre-calculate the value of the actual working time at the target representative time as the corresponding discrete working time lookup table item. When generating the production scheduling scheme, a step locking logic is executed: when the waiting time after the completion of the sealing process falls into a certain discrete interval of the waiting time, the actual working time of the thread locking assembly process is taken from the value of the discrete working time lookup table entry corresponding to the discrete interval of the waiting time.

7. The method for controlling the production process of an aluminum trim locking assembly according to claim 6, characterized in that, Using the aforementioned waiting time and the allocation of buffer workstation resources as production scheduling adjustment factors, and combining the aforementioned correlation mechanism, a production scheduling scheme is generated. The output is an execution instruction containing the target cycle time parameters for the specified buffer workstation resources and the corresponding thread-locking assembly process within the process chain, including: A time-indexed mixed-integer linear programming model is constructed, and a global optimization objective function is established. The global optimization objective function is composed of a linearly weighted principal objective term and a disambiguation penalty term. The principal objective term is to minimize the maximum completion time of all the process operation chains, and the disambiguation penalty term is a weighted summation term based on the lexicographical order of task and resource codes. The mixed-integer linear programming model is solved once using an optimization solution engine to obtain the optimal task resource allocation variable and the optimal start time variable. Based on the optimal task resource allocation variables, the buffer station resources occupied by each of the minimum schedulable batch units when performing the hot and humid environment temporary storage process are parsed out, and a temporary storage planning instruction is generated. Based on the optimal start time variable, the difference between the start time of the thread locking assembly process and the completion time of the hole sealing process is calculated, the discrete interval of the dwell time is determined, and the value of the discrete time lookup table item corresponding to the discrete interval of the dwell time is extracted and sent to the corresponding workstation as the target cycle time parameter.

8. The method for controlling the production process of an aluminum decorative locking assembly according to claim 5, characterized in that, The reference hydration evolution rate parameter, the humidity sensitivity coefficient parameter, the baseline friction coefficient parameter, and the friction evolution amplitude parameter are determined through the following calibration procedure: Construct a test sample matrix, set several different combinations of environmental humidity conditions and waiting time conditions covering a preset range, and perform sealing treatment and controlled environment temporary storage treatment on the standard test samples under each combination of conditions. Standardized torque clamping force test is performed on each group of standard test specimens after processing to obtain the measured torque coefficient of each group of specimens, and it is directly linearly mapped to the corresponding measured equivalent friction coefficient. The minimum and maximum values ​​of the measured equivalent friction coefficients were used to normalize all the data to obtain the measured hydration filling degree of each group of samples. Based on the measured equivalent friction coefficient data, linear regression fitting is performed to determine the baseline friction coefficient parameter and the friction evolution amplitude parameter; Based on the measured hydration filling degree data, the baseline hydration evolution rate parameter and the humidity sensitivity coefficient parameter are determined by least squares nonlinear regression fitting using the exponential evolution model of the first-order saturation dynamics.

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