Intelligent hanger whole life cycle management method based on PLM system

Abstract

The application discloses a full life cycle management method of an intelligent hanger based on a PLM system, relates to the technical field of intelligent manufacturing and PLM, and comprises the following steps: a digital master model integrated with a three-dimensional model, a material list and process rules is established in a PLM system; a standardized two-way data interface with a production execution system and a workshop Internet of Things is constructed to acquire state data flow in the process of hanger manufacturing, use and maintenance in real time; based on real-time data and preset rules, the system dynamically evaluates the suitability of the hanger, generates process recommendations or risk warning instructions, and issues the instructions to a production system; and the digital master model is synchronously updated according to the state data to form an evolution model mapping the real state of the entity. The method realizes real-time linkage between the state of the hanger and the design model, improves the precision of process adaptation and the risk control capability, and provides a dynamic data basis for the full life cycle optimization of assets.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing and PLM technology, specifically a method for intelligent hanger lifecycle management based on a PLM system. Background Technology

[0002] In surface treatment and material handling industries such as electroplating and coating, fixtures are crucial tooling for supporting workpieces and ensuring process quality. Currently, fixture management generally relies on traditional methods. Their design models, process parameters, and maintenance records typically exist as static paper documents or scattered electronic files, isolated from the actual operating status on the production floor. This management model leads to a disconnect between fixture design information, load history, wear status, and their real-time physical condition, failing to form a complete data link spanning design, manufacturing, use, and maintenance.

[0003] Existing technical solutions suffer from two main drawbacks. First, the assessment of fixture suitability relies heavily on manual experience. Operators must rely on memory or consult static manuals to determine whether a specific fixture is suitable for the current production task, making it difficult to assess in real-time performance changes due to fatigue, deformation, or contamination, which can easily lead to process risks or efficiency losses. Second, PLM systems typically function only as data management tools during the design phase. The 3D models and bills of materials they manage become fixed after the fixtures are put into use and cannot be updated to reflect changes in the physical fixtures' condition in the real production environment. This results in a separation between the "design model" and the "physical entity," leaving model-based accurate maintenance, lifespan prediction, and optimization decisions without a data foundation.

[0004] Breaking down information silos and achieving real-time linkage between fixture status and design models, as well as automatically and accurately evaluating and guiding the process applicability of fixtures based on real-time data, has become a key issue in improving the level of intelligent tooling management and production safety and reliability. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes a method for intelligent hanger lifecycle management based on a PLM system, including:

[0007] A digital master model of the fixture is created in the PLM system, wherein the digital master model integrates the fixture's three-dimensional geometric model, material specification list, design allowable load, and preset process applicability rules;

[0008] A standardized two-way data interface is built in the PLM system with the production execution system and the workshop Internet of Things system. The real-time status data stream of the smart hanger during the manufacturing, use and maintenance process is received through the data interface.

[0009] Based on the real-time status data stream and the preset process applicability rules in the digital master model, the smart fixture is dynamically applicable, and dynamic management instructions containing recommended processes, risk warnings, or maintenance suggestions are generated.

[0010] The dynamic management instructions are sent to the production execution system or related terminal equipment through the data interface;

[0011] Based on the real-time status data stream, the digital master model is synchronously updated in the PLM system to form an evolution model that reflects the true state of the smart hanger.

[0012] Furthermore, a digital master model of the fixture is created in the PLM system. This digital master model integrates the fixture's three-dimensional geometric model, material specifications, design allowable load, and preset process applicability rules, including:

[0013] An assembly model of the intelligent hanger is established in the three-dimensional design module of the PLM system. The assembly model includes the three-dimensional geometric models of all components and the assembly constraint relationships.

[0014] The material information of the smart fixture is extracted from the three-dimensional design module, and a structured material specification list is generated in the material management module of the PLM system.

[0015] In the simulation analysis module of the PLM system, various load conditions are applied to the three-dimensional geometric model to perform mechanical analysis, and the safe working load range obtained from the analysis is defined as the design allowable load.

[0016] Based on historical process data and expert experience, the preset process suitability rules are defined in the PLM system. These process suitability rules are associated with the material properties in the material specification list, the key dimensions of the three-dimensional geometric model, and the design allowable load.

[0017] The three-dimensional geometric model, the material specification list, the design allowable load, and the preset process applicability rules are associated and bound together, and stored in the database of the PLM system as the digital master model.

[0018] Furthermore, a standardized bidirectional data interface is constructed within the PLM system to connect with the Production Execution System and the workshop IoT system. This data interface receives real-time status data streams of the intelligent fixture during manufacturing, use, and maintenance, including:

[0019] Configure the application programming interface between the PLM system and the production execution system for transmitting work orders, process paths, and fixture scheduling instructions;

[0020] Configure the data acquisition interface between the PLM system and the workshop IoT system deployed in the workshop, wherein the workshop IoT system is connected to sensors installed on the smart hanger;

[0021] The data acquisition interface continuously receives the real-time status data stream from the sensor. The real-time status data stream includes the real-time load weight, tilt angle, vibration amplitude, strain value at key locations, ambient temperature, and cumulative usage time of the smart hanger.

[0022] The application programming interface (API) receives current processing work order information from the production execution system. The current processing work order information includes the type, weight, processing technology type, and process parameters of the workpiece to be loaded.

[0023] Furthermore, based on the real-time status data stream and the preset process suitability rules in the digital master model, a dynamic suitability assessment is performed on the intelligent fixture, generating dynamic management instructions that include recommended processes, risk warnings, or maintenance suggestions, including:

[0024] Extract the current load weight and current cumulative usage time of the smart hanger from the real-time status data stream;

[0025] Obtain the design permission load from the digital master model;

[0026] The current load weight is compared with the maximum value of the design allowable load. If the current load weight exceeds the maximum value of the design allowable load, an overload risk warning is triggered, and maintenance suggestions to reduce the load or replace the mounting bracket are generated.

[0027] Obtain the lifespan warning threshold associated with the current cumulative usage time from the digital master model;

[0028] The current cumulative usage time is compared with the lifespan warning threshold. If the current cumulative usage time exceeds the lifespan warning threshold, a lifespan warning is triggered, and a maintenance suggestion to perform preventive maintenance checks is generated.

[0029] Extract the processing technology type from the current processing work order information from the real-time status data stream;

[0030] Based on the processing technology type, query the preset process applicability rules associated with the digital master model to obtain the specific requirements of the processing technology type for the key dimensions, material corrosion resistance, or structural rigidity of the smart hanger.

[0031] The relevant sensor data in the real-time status data stream is compared with the specific requirements. If the requirements are met, an applicability confirmation instruction is generated as part of the recommended process; if the requirements are not met, a process mismatch warning is generated as a risk warning.

[0032] Furthermore, the dynamic management instructions are sent to the production execution system or related terminal equipment through the data interface, including:

[0033] When the dynamic management instruction is the recommended process, the recommended process instruction containing specific process parameters is written into the current work order execution sequence of the production execution system through the application programming interface;

[0034] When the dynamic management instruction is the risk warning or the maintenance suggestion, a warning notification is sent to the production execution system through the application programming interface to trigger production scheduling adjustments.

[0035] At the same time, through the message push service of the PLM system, the detailed content of the risk warning or maintenance suggestion is sent to the preset mobile terminal of maintenance personnel or workshop dashboard system;

[0036] The dynamic management command contains a unique identifier for the smart fixture, which is used for matching and locating in the production execution system or terminal equipment.

[0037] Furthermore, based on the real-time status data stream, the digital master model is synchronously updated in the PLM system to form an evolutionary model reflecting the true state of the smart hanger, including:

[0038] Create an evolution log for the fixture instance associated with the digital master model in the PLM system;

[0039] Key data representing changes in the status of the fixture in the real-time status data stream, including the weight value of each load, the maximum strain value detected, the number of high-temperature processes experienced, and the maintenance records completed, are recorded in the fixture instance evolution log in chronological order.

[0040] Based on the data in the evolution log of the hanging fixture instance, the cumulative fatigue damage index of the smart hanging fixture is calculated periodically;

[0041] Based on the cumulative fatigue damage index, the value of the design allowable load in the digital master model is reversed to generate the current actual allowable load;

[0042] The current actual permitted load, the latest cumulative usage time, and the critical dimension measurement value after the most recent maintenance are used as attributes to update the digital master model, forming the evolution model.

[0043] Furthermore, the method also includes the steps of optimizing the design of the mounting brackets and determining their decommissioning based on the evolutionary model:

[0044] A fixture performance degradation analysis task is set up in the PLM system, and the task periodically calls the historical state data in the evolution model;

[0045] Analyze the degradation trend of key performance parameters in the historical state data and compare it with the preset performance degradation threshold curve;

[0046] If the degradation trend exceeds the performance degradation threshold curve, a draft redesign proposal for strengthening key components or upgrading materials of the hanger is generated, and the draft redesign proposal is associated with the design change process of the digital master model.

[0047] At the same time, a retirement determination rule for the fixture is set, which is based on the ratio of the current actual allowable load to the initial design allowable load in the evolution model, the permanent deformation of the key structural dimensions, and the predicted remaining life of the main materials.

[0048] When the data of the evolution model meets the fixture retirement determination rules, a fixture retirement application form is automatically generated, and the fixture is locked in any new task scheduling in the production execution system.

[0049] Furthermore, the method also includes steps for constructing and reusing a knowledge base for hanging fixtures:

[0050] The complete lifecycle data of retired or significantly redesigned smart hangers, including their initial digital master model, complete hanger instance evolution logs, final evolution model status, and related fault and maintenance records, will be archived in a structured manner.

[0051] A knowledge base for fixture design and application is established in the PLM system, and the archived complete life cycle data is stored as knowledge entries in the knowledge base for fixture design and application.

[0052] When a new fixture design task is initiated in the PLM system, similar cases are searched in the fixture design and application knowledge base based on the design requirements of the new fixture.

[0053] The complete lifecycle data of similar cases retrieved will be pushed to designers as design input references.

[0054] Furthermore, the method also includes a virtual verification step for the hanger based on a digital twin:

[0055] The current state data of the evolution model, including geometric changes, material property decay, and current actual allowable load, is imported into the simulation analysis module of the PLM system to create the current digital twin of the smart hanger.

[0056] In the simulation analysis module, for new candidate process schemes, the current digital twin is virtually loaded and the process is simulated;

[0057] Analyze the results of the virtual loading and process simulation to predict the stress distribution, deformation, and potential failure risk of the smart fixture under the new process;

[0058] The prediction results are automatically compared with the preset process applicability rules to generate a virtual verification report for the candidate process scheme. The virtual verification report serves as the decision-making basis for whether to approve the candidate process scheme for application to the smart hanger.

[0059] Furthermore, the real-time status data stream also includes surface defect image data of key parts of the hanger obtained through a visual recognition system, as well as hanger identification and location information obtained through a radio frequency identification reader.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] By constructing standardized two-way interfaces with the Production Execution System (MES) and the workshop IoT, the system continuously receives real-time status data streams of smart fixtures throughout their entire lifecycle. Based on pre-set process suitability rules in the digital master model, it performs instant calculations and analyses, enabling dynamic and objective assessment of fixture suitability. The system can automatically generate dynamic management instructions containing specific process parameter recommendations or immediate risk warnings, directly replacing the traditional decision-making model that relies on manual experience and static documents. This transforms management from reactive responses to proactive prediction and control based on real-time data, reducing the probability of product quality defects and production interruptions caused by mismatches between the actual fixture status and process requirements, and improving the accuracy and reliability of production scheduling and process execution.

[0062] Based on real-time collected multi-dimensional status data such as load, deformation, and cumulative usage count, the digital master model of the fixture is synchronously iterated and incrementally updated in the PLM system, driving the static design model to evolve into a dynamic evolution model that accurately maps the historical and current state of the physical entity. This solution extends the boundaries of product lifecycle management from the design and manufacturing stage to the entire process of physical use, realizing a closed-loop data link. Based on this continuously evolving high-fidelity digital model, more accurate predictions of remaining lifespan and performance degradation trends can be performed, supporting the formulation of personalized preventive maintenance plans closely related to the individual fixture status, and providing objective data basis for fixture scrapping and replacement decisions. Thus, it constitutes the dynamic data core and decision-making foundation for optimizing the value of assets throughout their entire lifecycle. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the steps of the intelligent hanger lifecycle management method based on a PLM system described in this invention.

[0064] Figure 2 A flowchart illustrating the construction of the data interface and the reception of real-time status data streams;

[0065] Figure 3 A bar chart comparing the load safety of intelligent hanging devices;

[0066] Figure 4 A comparison chart of multi-dimensional indicators throughout the entire lifecycle of smart hanging devices;

[0067] Figure 5 Feature analysis diagram of archived data for PLM smart hanger knowledge base. Detailed Implementation

[0068] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0069] Please see Figure 1The specific implementation of the intelligent fixture lifecycle management method based on the PLM system is as follows: This method creates a digital master model of the fixture within the PLM system. This digital master model integrates the fixture's three-dimensional geometric model, material specification list, design allowable load, and preset process applicability rules. A standardized bidirectional data interface is constructed within the PLM system to connect with the production execution system and the workshop IoT system. This data interface receives real-time status data streams of the intelligent fixture during manufacturing, use, and maintenance. Based on the real-time status data streams and the preset process applicability rules in the digital master model, the intelligent fixture's applicability is dynamically assessed, generating dynamic management instructions that include recommended processes, risk warnings, or maintenance suggestions. These dynamic management instructions are then sent to the production execution system or relevant terminal equipment via the data interface. Finally, the digital master model is synchronously updated within the PLM system based on the real-time status data streams, forming an evolutionary model reflecting the true state of the intelligent fixture.

[0070] In one embodiment of the present invention, an assembly model of the intelligent fixture is established in the 3D design module of the PLM system. The assembly model includes the 3D geometric models of all components and assembly constraints. Material information of the intelligent fixture is extracted from the 3D design module, and a structured material specification list is generated in the material management module of the PLM system. In the simulation analysis module of the PLM system, various load conditions are applied to the 3D geometric model for mechanical analysis, and the safe working load range obtained from the analysis is defined as the design allowable load. Based on historical process data and expert experience, preset process applicability rules are defined in the PLM system. The process applicability rules are associated with the material properties in the material specification list, the key dimensions of the 3D geometric model, and the design allowable load. The 3D geometric model, the material specification list, the design allowable load, and the preset process applicability rules are associated and bound, and stored as a digital master model in the database of the PLM system.

[0071] In a specific implementation, this embodiment discloses one method for creating a digital master model of a smart hanger in a PLM system. In the 3D design module of the PLM system, designers establish an assembly model of the smart hanger. This assembly model includes the 3D geometric models of all components of the smart hanger and the assembly constraints between the components. The 3D geometric model defines the complete geometric shape and spatial structure of the smart hanger. In some embodiments, the 3D geometric models of all components of the smart hanger need to be parametrically modeled in the 3D design module. Parametric modeling ensures the consistency of model updates during subsequent design changes.

[0072] In practical implementation, material information for the intelligent fixture is extracted from the 3D design module. This information includes the identifier, name, quantity, and material specifications of each component constituting the intelligent fixture. In the PLM system's material management module, the system automatically generates a structured material specification list based on the extracted information. This list is displayed in a hierarchical tree structure, clearly reflecting the material composition of the intelligent fixture. In the PLM system's simulation analysis module, various load conditions are applied to the 3D geometric model of the intelligent fixture for mechanical analysis. These load conditions include static loads, dynamic loads, and impact loads. The stress, strain, and deformation distribution of the intelligent fixture under different conditions are calculated through mechanical analysis. The load range that ensures the safe and stable operation of the intelligent fixture is defined as the design allowable load. In practical implementation, the determination of the design allowable load follows the formula:

[0073]

[0074] Where: symbol Indicates the calculated design allowable load range value; symbol Indicates the yield strength of the selected material; symbol Represents the minimum critical cross-sectional area in the intelligent hanging fixture structure; symbol This represents a pre-set safety factor. It can be understood that this formula is used to quantitatively calculate the theoretical safe working boundary from the perspectives of material strength and structure.

[0075] Based on historical process data and expert experience, preset process applicability rules are defined in the knowledge rule module of the PLM system. These preset rules are implemented through logical statements or decision matrices. They explicitly link to material properties in the material specification list, such as temperature limit, conductivity, and corrosion resistance. They also link to key dimensions of the 3D geometric model, such as hook opening size and support arm spacing. Furthermore, they link to design allowable loads; for example, electrophoretic coating process rules require the fixture material to be resistant to acid and alkali corrosion, and the structural design allowable load must be greater than the dynamic fluid impact force when the workpiece is immersed in the bath. In some embodiments, the preset process applicability rules can be edited and maintained by process engineers in the graphical interface of the PLM system. The graphical interface supports dragging and dropping logic blocks to combine rules. In specific implementations, the 3D geometric model, material specification list, design allowable load, and preset process applicability rules are linked and bound together. This linking is achieved through the PLM system's internal data linking function, ensuring that any modification to any data can be tracked and affect related data. Ultimately, a complete and traceable digital master model is stored in the central database of the PLM system. Optionally, the storage of the digital master model adopts version management, and each design change or status update will generate a new model version and retain historical records.

[0076] See Figure 2 In one embodiment of the present invention, an application programming interface (API) is configured between the PLM system and the production execution system for transmitting work orders, process paths, and fixture scheduling instructions; a data acquisition interface is configured between the PLM system and a workshop IoT system deployed in the workshop, wherein the workshop IoT system is connected to sensors installed on the smart fixtures; through the data acquisition interface, real-time status data streams from the sensors are continuously received, including the real-time load weight, tilt angle, vibration amplitude, strain values ​​at key locations, ambient temperature, and cumulative usage time of the smart fixtures; through the API, current processing work order information from the production execution system is received, including the type, weight, processing technology type, and process parameters of the workpiece to be mounted; the real-time status data stream also includes surface defect image data of key parts of the fixtures obtained through a visual recognition system, and fixture identification and location information obtained through an RFID reader.

[0077] In a specific implementation, this embodiment discloses one method for constructing a data interface and receiving real-time status data streams. It configures an application programming interface (API) between the PLM system and the production execution system. The API adopts a RESTful architecture based on the HTTP protocol or an architecture based on industry standard protocols such as OPCUA. The API is used for bidirectional transmission of work orders, process paths, and fixture scheduling instructions between the production execution system and the PLM system, enabling synchronization of production plans and fixture management information. In some embodiments, the fixture scheduling instructions sent by the production execution system to the PLM system include a unique identifier for the target fixture and the planned time window for use. Meanwhile, the instructions fed back from the PLM system to the production execution system through the API include the current availability status of the fixture or dynamic management instructions.

[0078] In practical implementation, a data acquisition interface is configured between the PLM system and the workshop IoT system deployed within the workshop. This data acquisition interface typically uses the MQTT protocol or connects directly via industrial Ethernet. The workshop IoT system is connected to various sensors installed on the smart fixtures. These sensors include pressure sensors for measuring load weight, tilt sensors for measuring posture, accelerometers for monitoring vibration, strain gauges for measuring structural strain, and temperature sensors for sensing ambient temperature. Through the data acquisition interface, the PLM system continuously receives real-time status data streams from the sensors. These streams are transmitted in the form of time-series data packets, containing the smart fixture's real-time load weight, tilt angle, vibration amplitude, strain values ​​at key locations, ambient temperature, and cumulative usage time. The cumulative usage time is calculated based on the fixture's power-on or entry into the work area signal. It can be understood that the reception of the real-time status data stream is a continuous and concurrent process, reflecting the instantaneous operating status of the smart fixtures on the production line.

[0079] Through the application programming interface (API), the PLM system synchronously receives current processing order information from the production execution system. This current processing order information serves as production context data, including the type of workpiece to be mounted, its weight, processing technology type, and specific process parameters such as temperature, humidity, and pH. This information, along with real-time physical status data from sensors, constitutes a complete description of the intelligent mounting fixture's working environment. Optionally, the data integrity of the real-time status data stream can be evaluated using the following formula:

[0080]

[0081] Where: symbol Indicates the data integrity index; symbol Indicates the number of valid data packets actually received within a preset time window; symbol This represents the total number of data packets that should theoretically be received within the same time window. When the data integrity index is lower than the set threshold, the system will mark that the data for that time period may be missing.

[0082] In some embodiments, the sources of the real-time status data stream are further expanded to include surface defect image data of key parts of the hangers acquired by a vision recognition system deployed above the production line. The vision recognition system performs real-time analysis on the acquired images to identify defect features such as cracks, deformation, or coating peeling, and pushes the feature data along with a timestamp to the data acquisition interface. Simultaneously, the real-time status data stream also includes hanger identification and real-time location information acquired by RFID readers fixed at workshop logistics nodes. The RFID readers read the RFID tags installed on the smart hangers to determine the smart hanger's movement trajectory and current workstation within the workshop. The location information is then bound to the time information and uploaded to the PLM system. In specific implementations, image data from the vision recognition system and location data from the RFID readers are aligned and fused with sensor data and production work order data within the PLM system using a unified time reference, forming a multi-dimensional status data stream with spatiotemporal consistency. Optionally, the PLM system includes a data buffer for temporarily storing high-frequency real-time status data streams to ensure that data is not lost when the data processing module is busy.

[0083] In one embodiment of the present invention, the current load weight and current cumulative usage time of the intelligent hanger are extracted from the real-time status data stream; the design-permitted load is obtained from the digital master model; the current load weight is compared with the maximum value of the design-permitted load, and if the current load weight exceeds the maximum value of the design-permitted load, an overload risk warning is triggered, and a maintenance suggestion to reduce the load or replace the hanger is generated; a lifespan warning threshold associated with the current cumulative usage time is obtained from the digital master model; the current cumulative usage time is compared with the lifespan warning threshold, and if the current cumulative usage time exceeds the lifespan warning threshold, a lifespan warning is triggered, and a maintenance suggestion to perform preventive maintenance checks is generated; the processing technology type in the current processing work order information is extracted from the real-time status data stream; based on the processing technology type, the preset process applicability rules associated with it in the digital master model are queried to obtain the key processing technology type for the intelligent hanger. Specific requirements for dimensions, material corrosion resistance, or structural stiffness; comparing relevant sensor data in the real-time status data stream with these specific requirements; if the requirements are met, an applicability confirmation instruction is generated as part of the recommended process; if the requirements are not met, a process mismatch warning is generated as a risk warning; when the dynamic management instruction is a recommended process, the recommended process instruction containing specific process parameters is written into the current work order execution sequence of the production execution system through the application programming interface; when the dynamic management instruction is a risk warning or maintenance suggestion, a warning notification is sent to the production execution system through the application programming interface, triggering production scheduling adjustments; simultaneously, the detailed content of the risk warning or maintenance suggestion is sent to the preset maintenance personnel mobile terminal or workshop dashboard system through the message push service of the PLM system; the dynamic management instruction contains a unique identifier for the smart fixture, used for matching and positioning in the production execution system or terminal equipment.

[0084] In this implementation, the present embodiment discloses one approach to dynamic suitability assessment and instruction generation and issuance. The dynamic suitability assessment module extracts the current load weight and current cumulative usage time of the smart fixture from the received real-time status data stream. The current load weight is directly derived from the real-time reading of the pressure sensor on the smart fixture, and the current cumulative usage time is derived from the calculation result of the PLM system continuously accumulating the time period of each time the smart fixture enters the working state. The design allowable load is obtained from the created digital master model, which defines the static and dynamic load range that the smart fixture is allowed to withstand. In the specific implementation, the logic for performing the load safety check is to directly compare the current load weight with the maximum value of the design allowable load. If the current load weight exceeds the maximum value of the design allowable load, an overload risk warning is immediately triggered, and a maintenance suggestion to reduce the load or replace the fixture is generated. The maintenance suggestion will list the current overload value and the identifiers of other fixtures that can be replaced and match the load capacity.

[0085] The system retrieves a lifespan warning threshold associated with the current cumulative usage time from the digital master model. This threshold is based on a comprehensive set of factors including the fixture's design life, material fatigue characteristics, and historical maintenance cycles. The current cumulative usage time is compared to the lifespan warning threshold. If the current cumulative usage time exceeds the threshold, a lifespan warning is triggered, and a maintenance recommendation for preventative maintenance is generated, specifying a list of components requiring focused inspection. The system extracts the processing technology type from the current processing work order information from the real-time status data stream. Processing technology types include, for example, "electro-zinc plating," "anodizing," or "powder coating." In some embodiments, based on the extracted processing technology type, the system queries the associated preset process applicability rules in the digital master model. These rules explicitly specify the specific requirements of this processing technology type on the smart fixture's critical dimensions, material corrosion resistance, or structural rigidity. For example, the "electro-zinc plating" process requires the fixture's contact points to be made of titanium alloy, and the structural design's permissible load must additionally consider the buoyancy and agitation impact of the plating solution. The relevant sensor data in the real-time status data stream is compared with these specific requirements. If the sensor data meets all the requirements of the preset process suitability rules, a suitability confirmation instruction is generated as part of the recommended process. If the sensor data does not meet any requirement of the preset process suitability rules, a process mismatch warning is generated as a risk warning. It can be understood that the above evaluation process can be summarized into a single evaluation value, the calculation of which follows the formula:

[0086]

[0087] Where: symbol Indicates the overall evaluation value; symbol Represents the load safety weighting factor; symbol Indicates the load safety check result (compliance is 1, overload is 0); symbol Represents the lifetime state weighting factor; symbol Indicates the lifespan check result (1 for not exceeding the threshold, 0 for exceeding the threshold); symbol Indicates the process matching weight factor; symbol This indicates the matching result of the process rules (1 for a match, 0 for no match).

[0088] When the dynamic management instruction is a recommended process, the system writes the recommended process instruction, containing specific process parameters, into the current work order execution sequence of the production execution system via the application programming interface (API). These specific process parameters may include the suspension angle, travel speed, or tank dwell time optimized for the smart fixture. When the dynamic management instruction is a risk warning or maintenance suggestion, the system sends a warning notification to the production execution system via the API. The warning notification triggers the scheduling module of the production execution system to re-plan production tasks, such as pausing the allocation of new workpieces to the smart fixture or routing it to a maintenance station. Simultaneously, through the message push service integrated into the PLM system, the detailed content of the risk warning or maintenance suggestion, the identification of the involved smart fixture, and the suggested measures are sent to preset maintenance personnel mobile terminals or workshop dashboards for visual alerts. In some embodiments, all dynamic management instructions are required to include a unique identifier for the smart fixture. This unique identifier is used for rapid matching and accurate positioning of the target fixture on the production execution system or maintenance personnel mobile terminals, ensuring that the instruction is executed correctly. Optionally, for high-priority risk warnings, the message push service uses a combination of sound, flashing lights, and repeated push notifications for alerts. In practice, instructions issued to the production execution system are recorded in the operation log and associated with the corresponding smart fixture's unique identifier, generation timestamp, and execution status to achieve full-process traceability.

[0089] See Figure 3 This is a bar chart comparing the load safety of 10 smart fixtures, visually comparing their design allowable loads with their current loads. Five of the ten fixtures are overloaded, accounting for 50%, indicating a high risk. HG-010 is overloaded by 80kg, the highest risk among all fixtures, requiring immediate action. From HG-002 to HG-010, the overload value increases with the design load, reflecting the need for stronger load control for heavy-duty fixtures. Immediately reduce the load or replace severely overloaded fixtures such as HG-010 and HG-006 to prevent structural failure. Add real-time load verification rules to the PLM system to automatically prevent work orders from being issued when overload is detected. Perform structural flaw detection on all overloaded fixtures, assess fatigue damage, and update the actual allowable load in the evolution model. Analyze the causes of overload, such as incorrect work order allocation, workpiece weight estimation deviations, or unclear fixture load markings.

[0090] In one embodiment of the present invention, a fixture instance evolution log associated with the digital master model is created in the PLM system; key data characterizing fixture state changes from the real-time status data stream, including the weight value of each load, the maximum strain value detected, the number of high-temperature processes experienced, and the maintenance records completed, are recorded in the fixture instance evolution log in chronological order; based on the data in the fixture instance evolution log, the cumulative fatigue damage index of the intelligent fixture is periodically calculated; according to the cumulative fatigue damage index, the design allowable load value in the digital master model is reverse-corrected to generate the current actual allowable load; the current actual allowable load, the latest cumulative usage time, and the key dimension measurement value after the most recent maintenance are updated as attributes to the digital master model to form an evolution model; in the PLM system... A task for analyzing the performance degradation of the hanger is set up. This task periodically calls historical state data from the evolution model; analyzes the degradation trend of key performance parameters in the historical state data and compares it with a preset performance degradation threshold curve; if the degradation trend exceeds the performance degradation threshold curve, a draft redesign proposal for strengthening key components or upgrading materials of the hanger is generated, and the draft redesign proposal is linked to the design change process of the digital master model; at the same time, a hanger retirement judgment rule is set, which is based on the ratio of the current actual allowable load to the initial design allowable load in the evolution model, the permanent deformation of key structural dimensions, and the predicted remaining life of major materials; when the data of the evolution model meets the hanger retirement judgment rule, a hanger retirement application form is automatically generated, and the hanger is locked in any new task scheduling in the production execution system.

[0091] In its specific implementation, this embodiment discloses an implementation method for constructing an evolutionary model and performing optimized design and decommissioning determination. In the PLM system, an evolutionary log for each intelligent fixture in service is created, associated with its digital master model. This log is a structured database table arranged chronologically. Key data characterizing fixture status changes from the real-time status data stream, including the weight value of each load, the maximum detected strain value, the number of high-temperature processes experienced, and completed maintenance records, are recorded chronologically in the fixture instance evolutionary log. Each record includes a precise timestamp and a corresponding working condition description. Based on the data in the fixture instance evolutionary log, the system periodically calls an analysis algorithm to calculate the cumulative fatigue damage index of the intelligent fixture. The calculation of the cumulative fatigue damage index considers the load spectrum, the number of stress cycles, and the material SN curve, following the formula:

[0092]

[0093] Where: symbol Indicates the cumulative fatigue damage index; symbol Indicates the actual number of load cycles at the k-th stress level; symbol Represents the number of fatigue life cycles of a material at the k-th stress level; symbol This represents the total number of stress levels. Based on the calculated cumulative fatigue damage index, the system reverse-corrects the design allowable load value in the digital master model to generate the current actual allowable load. The correction logic follows the damage tolerance principle, meaning that the higher the cumulative fatigue damage index, the greater the reduction in the current actual allowable load compared to the initial design allowable load. In specific implementation, the calculated current actual allowable load, the latest cumulative usage time, and the key dimension measurement value after the most recent maintenance are written as update attributes and overwrite the corresponding fields in the digital master model, forming an evolutionary model that reflects the true physical state and performance degradation of the intelligent hanger.

[0094] A fixture performance degradation analysis task is set up in the PLM system. This task is automatically executed according to a preset cycle and calls historical state data from the evolution model. The degradation trend of key performance parameters in the historical state data is analyzed. Key performance parameters include structural stiffness, material thickness, and electrical conductivity, and compared with a preset performance degradation threshold curve. If the degradation trend exceeds the performance degradation threshold curve, a redesign proposal draft for strengthening or upgrading key fixture components is automatically generated. The redesign proposal draft lists the specific component numbers requiring reinforcement and recommended material specifications, and is associated with the design change process of the digital master model to trigger engineering changes. In some embodiments, the preset performance degradation threshold curve is derived from statistical analysis of the service history data of similar fixtures. Simultaneously, fixture retirement judgment rules are set in the PLM system's rule engine. These rules are based on a combination of multiple conditions, including the ratio of the current actual allowable load to the initial design allowable load in the evolution model, the permanent deformation of key structural dimensions, and the predicted remaining life of major materials. Table 1 illustrates an exemplary logical combination of fixture retirement judgment rules.

[0095] Table 1. Rules for Determining the Retirement of Smart Hangers

[0096]

[0097] When the data in the evolutionary model meets any of the conditions in the fixture retirement determination rules shown in Table 1, the system automatically generates a fixture retirement application form. This application form includes the fixture identifier, retirement reason, and supporting data, and automatically locks any new task scheduling of the fixture in the production execution system to prevent its continued use. In some embodiments, the fixture retirement application form automatically flows to the equipment management department for approval. Optionally, for fixtures approaching but not yet reaching the retirement threshold, the system generates maintenance recommendations to strengthen monitoring and shorten inspection cycles. It can be understood that the continuous updating of the evolutionary model provides a dynamic data foundation for performance degradation analysis and retirement determination.

[0098] See Figure 4 This is a comparative chart of multi-dimensional indicators throughout the entire lifecycle of intelligent hangers, showcasing the changing characteristics of data volume, processing time, number of decision points, and number of system interactions across five stages: design, manufacturing, use, maintenance, and decommissioning. Data volume is positively correlated with operational complexity; the data volume during the use stage is five times that of the design stage, indicating that hangers generate a large amount of real-time status data during service, which is the core basis for dynamic suitability assessment and evolutionary model updates. The data value gradient throughout the entire lifecycle shows an upward trend in data volume from the design to the use stage and a downward trend from the use to decommissioning stage, reflecting that data value peaks during the operational period and is primarily used for knowledge reuse after decommissioning.

[0099] In one embodiment of the present invention, the complete lifecycle data of retired or significantly redesigned smart hangers, including their initial digital master model, complete hanger instance evolution logs, final evolution model states, and related fault and maintenance records, are structurally archived. A hanger design and application knowledge base is established in the PLM system, and the archived complete lifecycle data is stored as knowledge entries in the hanger design and application knowledge base. When a new hanger design task is initiated in the PLM system, similar cases are searched in the hanger design and application knowledge base based on the design requirements of the new hanger. The complete lifecycle data of the retrieved similar cases are pushed to the designers as design input. Reference: Import the current state data of the evolution model, including geometric changes, material property decay, and current actual allowable load, into the simulation analysis module of the PLM system to create the current digital twin of the smart fixture; in the simulation analysis module, perform virtual loading and process simulation on the current digital twin for new candidate process schemes; analyze the results of virtual loading and process simulation to predict the stress distribution, deformation, and potential failure risks of the smart fixture under the new process; automatically compare the prediction results with the preset process applicability rules to generate a virtual verification report for the candidate process scheme, which serves as the basis for deciding whether to approve the application of the candidate process scheme to the smart fixture.

[0100] In its specific implementation, this embodiment discloses an implementation method for constructing and reusing a hanger knowledge base and for virtual verification based on digital twins. The complete lifecycle data of retired or significantly redesigned smart hangers is structurally archived. This complete lifecycle data includes the initial digital master model, a complete hanger instance evolution log, the final evolution model state, and related fault and maintenance records. A hanger design and application knowledge base is established within the PLM system. This knowledge base adopts a database architecture with classification and tagging functions. The archived complete lifecycle data is stored as independent knowledge entries in the hanger design and application knowledge base, with each knowledge entry associated with a unique smart hanger historical identifier. In some embodiments, the archiving process includes keyword extraction and structuring of unstructured data (such as text descriptions in fault records) to facilitate subsequent retrieval.

[0101] When a designer initiates a new fixture design task in the PLM system, the system searches for similar cases in the fixture design and application knowledge base based on the new fixture's design requirements. These requirements include target load-bearing capacity, applicable process type, material constraints, and spatial size limitations. The core of the similarity search is calculating the similarity between the new fixture's design requirement vector and the feature vectors of historical cases in the knowledge base. This calculation follows the formula:

[0102]

[0103] Where: symbol Indicates query similarity; symbol This represents the feature vector composed of the design requirements parameters of the new mounting fixture; symbol This represents a feature vector composed of key parameters from the digital master model and evolution model of historical cases. The system automatically pushes the complete lifecycle data of several highly similar cases to the designer's workbench interface as design input references. The pushed data includes the historical case's 3D geometric model, material specifications, typical failure modes encountered during service, and redesign records. Optionally, the system supports designers to quickly clone and modify based on a similar case to accelerate the design process of new fixtures.

[0104] In specific implementations, the virtual verification steps of the fixture based on digital twin are executed independently of the knowledge base retrieval steps. The current state data of the target intelligent fixture evolution model is imported into the simulation analysis module of the PLM system. The current state data includes geometric dimension changes, material property attenuation, and the current actual allowable load. The simulation analysis module uses this data to create the current digital twin of the target intelligent fixture, and the current digital twin reflects the actual physical state of the fixture after service wear. In the simulation analysis module, for a new candidate process plan planned to be applied to the target intelligent fixture, virtual loading and process simulation are performed on the current digital twin. The virtual loading simulates the actual clamping state and force distribution of the workpiece on the fixture in the candidate process plan, and the process simulation reproduces the complete processing flow involved in the candidate process plan, such as lifting, tilting, immersing in a chemical tank, or undergoing a temperature cycle. Analyze the results of the virtual loading and process simulation to predict the stress distribution, deformation conditions, and potential failure risks of the intelligent fixture under the new process, such as predicting whether the fixture will resonate at a specific vibration frequency or predicting the fatigue life of the key weld under thermal cycling. In some embodiments, the simulation results are output in the form of cloud diagrams, animations, and numerical reports. Automatically compare the prediction results with the preset process applicability rules. The preset process applicability rules specify the maximum allowable stress and deformation threshold under different processes. The system generates a virtual verification report for the candidate process plan, and the virtual verification report clearly lists the comparison results, risk items, and compliance conclusions. It can be understood that the virtual verification report serves as the decision-making basis for process engineers or production planners to decide whether to approve the application of this candidate process plan to this intelligent fixture. Optionally, if the virtual verification report shows high risks, the system can automatically reject the process plan and prompt to replace the fixture or modify the process parameters.

[0105] Refer to Figure 5 , this is a characteristic analysis diagram of the archived data in the PLM intelligent fixture knowledge base, showing the data volume and processing time of different types of archived data. The data volume of the evolution log is 5 times that of the initial model, indicating that the value of the dynamic data generated by the fixture during service is much higher than the static design data, which is also the core basis for constructing the evolution model and digital twin. The processing time proportion of the evolution log is the highest. It is recommended to introduce automated data cleaning and structuring tools to reduce the manual processing cost. For high-frequency accessed data such as evolution logs and maintenance records, higher-performance storage devices can be configured to improve the knowledge base retrieval speed. The figure reveals that "evolution logs" and "maintenance records" are the most core data assets in the knowledge base, and their dynamic data directly supports the construction of the evolution model and digital twin. Promote the establishment of standardized document templates to reduce the complexity of subsequent processing and improve the quality of knowledge reuse.

[0106] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent hanging fixture lifecycle management based on a PLM system, characterized in that, The method includes: A digital master model of the fixture is created in the PLM system, wherein the digital master model integrates the fixture's three-dimensional geometric model, material specification list, design allowable load, and preset process applicability rules; A standardized two-way data interface is built in the PLM system with the production execution system and the workshop Internet of Things system. The real-time status data stream of the smart hanger during the manufacturing, use and maintenance process is received through the data interface. Based on the real-time status data stream and the preset process suitability rules in the digital master model, a dynamic suitability assessment is performed on the intelligent fixture, generating dynamic management instructions that include recommended processes, risk warnings, or maintenance suggestions, including: Extract the current load weight and current cumulative usage time of the smart hanger from the real-time status data stream; Obtain the design permission load from the digital master model; The current load weight is compared with the maximum value of the design allowable load. If the current load weight exceeds the maximum value of the design allowable load, an overload risk warning is triggered, and maintenance suggestions to reduce the load or replace the mounting bracket are generated. Obtain the lifespan warning threshold associated with the current cumulative usage time from the digital master model; The current cumulative usage time is compared with the lifespan warning threshold. If the current cumulative usage time exceeds the lifespan warning threshold, a lifespan warning is triggered, and a maintenance suggestion to perform preventive maintenance checks is generated. Extract the processing technology type from the current processing work order information from the real-time status data stream; Based on the processing technology type, query the preset process applicability rules associated with the digital master model to obtain the specific requirements of the processing technology type for the key dimensions, material corrosion resistance, or structural rigidity of the smart hanger. The relevant sensor data in the real-time status data stream is compared with the specific requirements. If the requirements are met, an applicability confirmation instruction is generated as part of the recommended process; if the requirements are not met, a process mismatch warning is generated as a risk warning. The dynamic management instructions are sent to the production execution system or related terminal equipment through the data interface; Based on the real-time status data stream, the digital master model is synchronously updated in the PLM system to form an evolution model that reflects the true state of the smart hanger.

2. The intelligent hanger lifecycle management method based on a PLM system according to claim 1, characterized in that, A digital master model of the fixture is created in the PLM system. This digital master model integrates the fixture's three-dimensional geometric model, material specification list, design allowable load, and preset process applicability rules, including: An assembly model of the intelligent hanger is established in the three-dimensional design module of the PLM system. The assembly model includes the three-dimensional geometric models of all components and the assembly constraint relationships. The material information of the smart fixture is extracted from the three-dimensional design module, and a structured material specification list is generated in the material management module of the PLM system. In the simulation analysis module of the PLM system, various load conditions are applied to the three-dimensional geometric model to perform mechanical analysis, and the safe working load range obtained from the analysis is defined as the design allowable load. Based on historical process data and expert experience, the preset process suitability rules are defined in the PLM system. These process suitability rules are associated with the material properties in the material specification list, the key dimensions of the three-dimensional geometric model, and the design allowable load. The three-dimensional geometric model, the material specification list, the design allowable load, and the preset process applicability rules are associated and bound together, and stored in the database of the PLM system as the digital master model.

3. The intelligent hanger lifecycle management method based on a PLM system according to claim 2, characterized in that, A standardized bidirectional data interface is built in the PLM system to connect with the Production Execution System and the workshop IoT system. This data interface receives real-time status data streams of the intelligent fixture during manufacturing, use, and maintenance. Configure the application programming interface between the PLM system and the production execution system for transmitting work orders, process paths, and fixture scheduling instructions; Configure the data acquisition interface between the PLM system and the workshop IoT system deployed in the workshop, wherein the workshop IoT system is connected to sensors installed on the smart hanger; The data acquisition interface continuously receives the real-time status data stream from the sensor. The real-time status data stream includes the real-time load weight, tilt angle, vibration amplitude, strain value at key locations, ambient temperature, and cumulative usage time of the smart hanger. The application programming interface (API) receives current processing work order information from the production execution system. The current processing work order information includes the type, weight, processing technology type, and process parameters of the workpiece to be loaded.

4. The intelligent hanger lifecycle management method based on a PLM system according to claim 3, characterized in that, Sending the dynamic management instructions to the production execution system or related terminal devices through the data interface includes: When the dynamic management instruction is the recommended process, the recommended process instruction containing specific process parameters is written into the current work order execution sequence of the production execution system through the application programming interface; When the dynamic management instruction is the risk warning or the maintenance suggestion, a warning notification is sent to the production execution system through the application programming interface to trigger production scheduling adjustments. At the same time, through the message push service of the PLM system, the detailed content of the risk warning or maintenance suggestion is sent to the preset mobile terminal of maintenance personnel or workshop dashboard system; The dynamic management command contains a unique identifier for the smart fixture, which is used for matching and locating in the production execution system or terminal equipment.

5. The intelligent hanger lifecycle management method based on a PLM system according to claim 4, characterized in that, Based on the real-time status data stream, the digital master model is synchronously updated in the PLM system to form an evolutionary model reflecting the true state of the smart hanger, including: Create an evolution log for the fixture instance associated with the digital master model in the PLM system; Key data representing changes in the status of the fixture in the real-time status data stream, including the weight value of each load, the maximum strain value detected, the number of high-temperature processes experienced, and the maintenance records completed, are recorded in the fixture instance evolution log in chronological order. Based on the data in the evolution log of the hanging fixture instance, the cumulative fatigue damage index of the smart hanging fixture is calculated periodically; Based on the cumulative fatigue damage index, the value of the design allowable load in the digital master model is reversed to generate the current actual allowable load; The current actual permitted load, the latest cumulative usage time, and the critical dimension measurement value after the most recent maintenance are used as attributes to update the digital master model, forming the evolution model.

6. The intelligent hanger lifecycle management method based on a PLM system according to claim 5, characterized in that, The method also includes the steps of optimizing the design of the mounting brackets and determining their decommissioning based on the evolutionary model: A fixture performance degradation analysis task is set up in the PLM system, and the task periodically calls the historical state data in the evolution model; Analyze the degradation trend of key performance parameters in the historical state data and compare it with the preset performance degradation threshold curve; If the degradation trend exceeds the performance degradation threshold curve, a draft redesign proposal for strengthening key components or upgrading materials of the hanger is generated, and the draft redesign proposal is associated with the design change process of the digital master model. At the same time, a retirement determination rule for the fixture is set, which is based on the ratio of the current actual allowable load to the initial design allowable load in the evolution model, the permanent deformation of the key structural dimensions, and the predicted remaining life of the main materials. When the data of the evolution model meets the fixture retirement determination rules, a fixture retirement application form is automatically generated, and the fixture is locked in any new task scheduling in the production execution system.

7. The intelligent hanger lifecycle management method based on a PLM system according to claim 6, characterized in that, The method also includes steps for constructing and reusing a knowledge base for hangers: The complete lifecycle data of retired or significantly redesigned smart hangers, including their initial digital master model, complete hanger instance evolution logs, final evolution model status, and related fault and maintenance records, will be archived in a structured manner. A knowledge base for fixture design and application is established in the PLM system, and the archived complete life cycle data is stored as knowledge entries in the knowledge base for fixture design and application. When a new fixture design task is initiated in the PLM system, similar cases are searched in the fixture design and application knowledge base based on the design requirements of the new fixture. The complete lifecycle data of similar cases retrieved will be pushed to designers as design input references.

8. The intelligent hanger lifecycle management method based on a PLM system according to claim 7, characterized in that, The method also includes a virtual verification step for the hanger based on digital twins: The current state data of the evolution model, including geometric changes, material property decay, and current actual allowable load, is imported into the simulation analysis module of the PLM system to create the current digital twin of the smart hanger. In the simulation analysis module, for new candidate process schemes, the current digital twin is virtually loaded and the process is simulated. Analyze the results of the virtual loading and process simulation to predict the stress distribution, deformation, and potential failure risk of the smart fixture under the new process; The prediction results are automatically compared with the preset process applicability rules to generate a virtual verification report for the candidate process scheme. The virtual verification report serves as the decision-making basis for whether to approve the candidate process scheme for application to the smart hanger.

9. The intelligent hanger lifecycle management method based on a PLM system according to claim 1, characterized in that, The real-time status data stream also includes surface defect image data of key parts of the hanger obtained through a visual recognition system, as well as hanger identification and location information obtained through a radio frequency identification reader.

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