Global digital quality monitoring and management system, method and medium

Abstract

This application relates to a global digital quality monitoring and management system, method, and medium. The global digital quality monitoring and management system for a product manufacturing process includes: a data acquisition system comprising multiple data acquisition subsystems, wherein the data acquisition system is configured to acquire source data related to the manufacturing process, the source data including real-time data from at least some of the multiple data acquisition subsystems; a centralized data interface coupled to the data acquisition system; and a cloud data processing center. The cloud data processing center is configured to: receive source data related to the manufacturing process from the data acquisition system via the centralized data interface; process the source data to obtain target data; and provide global services based on the invocation and analysis of the target data.

Description

Technical Field

[0001] This disclosure relates to quality monitoring and management, specifically to the overall digital quality monitoring and management of the product manufacturing process. Background Technology

[0002] The engine manufacturing process involves multiple manufacturing processes, multiple manufacturing workshops, numerous materials, numerous control and operating systems, numerous suppliers, numerous processing equipment, testing equipment, and sensing equipment (such as spectrometers, density meters, temperature meters, and imagers), etc.

[0003] Figure 1 This is a schematic diagram illustrating an exemplary engine production lifecycle. As shown, the production lifecycle can include three main stages: casting, machining, and final assembly.

[0004] The casting stage may involve the production of crankcase sand cores, blank processing, and tooling preparation (e.g., laser engraving, core removal and sawing, crack testing, X-ray, visual inspection, heat treatment, sandblasting production process, etc.).

[0005] The machining stage includes milling, drilling, turning, and honing of core engine components such as the crankcase and cylinder head, in conjunction with a computer control system, to provide high-precision manufacturing. Machining of the raw parts includes pre-machining, cleaning, leak testing, cylinder inspection, and arc wire coating. The cylinder head machining process can include loading the raw part, rough machining, semi-machining, component assembly, final machining, leak testing, and cleaning.

[0006] The final assembly process may involve online testing, tightening, bonding, welding, and painting. It also requires a quality management system that conforms to national / international standards.

[0007] At each stage and in each step, there are numerous factors that affect product quality, such as those related to the environment, people, materials, equipment, and processes.

[0008] The engine production process may involve various related quality control and management systems, such as factory control systems, quality information and control systems (e.g., for standardizing the documentation and evaluation of audit results), production control systems (e.g., for controlling automated flexible production), material management systems, factory and machine information systems, integrated powertrain management systems, system application and product data processing systems, and so on.

[0009] In current technology, data related to engine production at various stages and in various factories / workshops are scattered across different nodes and systems, making it difficult to fully utilize for effective monitoring and management of the production process. Summary of the Invention

[0010] According to embodiments of this disclosure, a global digital quality monitoring and management system for a product manufacturing process is provided, comprising: a data acquisition system including multiple data acquisition subsystems, wherein the data acquisition system is configured to acquire source data related to the production process, the source data including real-time data from at least some of the multiple data acquisition subsystems; a centralized data interface coupled to the data acquisition system; and a cloud data processing center. The cloud data processing center is configured to receive source data related to the production process from the data acquisition system via the centralized data interface; process the source data to obtain target data; and provide global services based on the invocation and analysis of the target data.

[0011] According to embodiments of this disclosure, the plurality of data acquisition subsystems include at least one of the following: one or more sensors; and one or more process and quality management subsystems.

[0012] According to embodiments of this disclosure, the source data includes at least one of the following: environmental data related to the environment involved in the production process; machine data related to machining; coolant data; data related to cutting tools; data related to tool lists; data related to cutting parameters; and data related to process and quality management.

[0013] According to embodiments of this disclosure, a cloud data processing center is configured to perform at least one of the following based on the invocation and analysis of target data: providing daily quality reports based on the target data; tracking and locking defective parts; predictive production equipment maintenance; production equipment failure cause analysis; supply chain management; consumable life management; supplier performance management; and cycle time analysis.

[0014] According to embodiments of this disclosure, the cloud data processing center is configured to: identify suspicious parts associated with quality problems in each production subsystem based on quality problem data and target data; and instruct each production subsystem to lock the corresponding suspicious parts.

[0015] According to embodiments of this disclosure, a cloud data processing center is configured to: analyze historical target data to determine the correlation between production equipment failures and monitoring parameters; predict whether a production equipment failure will occur based on the analysis of real-time target data and the correlation; and output a production equipment maintenance prompt in response to the prediction that a production equipment failure will occur based on the analysis of real-time target data.

[0016] According to embodiments of this disclosure, a cloud data processing center is configured to: analyze historical target data to determine a set of production equipment failures and causes; and determine the causes of production equipment failures based on production equipment failure data and the determined set of production equipment failures and causes.

[0017] According to embodiments of this disclosure, the target data includes at least one of the following: cutting tool lifespan data; scrap data; equipment operation data; actual tool data; material data; equipment data; process data; and quality data.

[0018] According to embodiments of this disclosure, a method for global digital quality monitoring and management of a product manufacturing process is provided, comprising: real-time acquisition of source data related to the manufacturing process by a data acquisition system, wherein the data acquisition system includes multiple data acquisition subsystems, and the source data includes real-time data from at least some of the multiple data acquisition subsystems; receiving the source data related to the manufacturing process from the data acquisition system by a cloud data processing center via a centralized data interface coupled to the data acquisition system, structuring the source data to obtain structured target data, and providing global services based on the invocation and analysis of the target data.

[0019] According to embodiments of the present disclosure, a computer-readable storage medium is provided that stores computer-readable program instructions thereon, which, when executed by a processor, cause the processor to perform the method described above. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating an exemplary engine production lifecycle.

[0021] Figure 2 This is a block diagram illustrating an exemplary global digital quality monitoring and management system for a product manufacturing process according to an embodiment of the present disclosure.

[0022] Figure 3 The system architecture of an exemplary global digital quality monitoring and management system according to an embodiment of this disclosure is shown.

[0023] Figure 4 This illustration shows a comparison between the analysis report formation process according to the prior art and the analysis report formation process according to embodiments of this disclosure.

[0024] Figure 5 This illustrates a comparison between a defective part tracking and locking process according to the prior art and a defective part tracking and locking process according to an embodiment of this disclosure.

[0025] Figure 6 A schematic diagram illustrating an exemplary data mapping according to an embodiment of this disclosure is shown.

[0026] Figure 7 A schematic diagram illustrating an exemplary data mapping according to an embodiment of this disclosure is shown.

[0027] Figure 8This is a flowchart illustrating a global digital quality monitoring and management method for a product manufacturing process according to an embodiment of the present disclosure.

[0028] Figure 9 This is a schematic diagram illustrating a general hardware environment in which a device according to an embodiment of the present disclosure can be implemented. Detailed Implementation

[0029] The following description is provided to enable those skilled in the art to implement and use the embodiments, and the description is provided in the context of a particular application and its requirements. Various modifications will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments and applications without departing from the spirit and scope of the embodiments. Therefore, the embodiments are not limited to the embodiments shown, but are to be given the widest scope consistent with the principles and features disclosed herein.

[0030] This disclosure aims to provide a global digital quality monitoring and management system, method, and medium for product manufacturing processes.

[0031] Figure 2 This is a block diagram illustrating an exemplary global digital quality monitoring and management system 200 for a product manufacturing process according to an embodiment of the present disclosure.

[0032] like Figure 2 As shown, the global digital quality monitoring and management system 200 may include a data acquisition system 201, a centralized data interface 203, and a cloud data processing center 205.

[0033] The data acquisition system 201 collects source data related to the production process. Source data may include, but is not limited to: environmental data related to the environment involved in the production process; machine data related to machining; coolant data; data related to cutting tools; data related to tool lists; data related to cutting parameters; and data related to process and quality management. Source data may include real-time data and non-real-time data.

[0034] Data acquisition system 201 may include multiple data acquisition subsystems 201-1 to 201-N. These data acquisition subsystems may be process and quality management subsystems, including but not limited to: factory control systems, quality information and control systems (e.g., for standardizing documentation and evaluation of audit results), production control systems (e.g., for controlling automated flexible production), material management systems, factory and machine information systems, integrated powertrain management systems, system application and product data processing systems, etc. Data acquisition subsystems may be sensors, such as temperature sensors for measuring coolant temperature, and temperature / humidity sensors for measuring the state of the production environment. Data acquisition subsystems may be IoT Edge devices to collect information related to various devices and materials. Those skilled in the art will understand that any device capable of accumulating or generating relevant data during engine production can serve as a data acquisition subsystem. These data acquisition subsystems are distributed across various locations or stages involved in the engine production process, each generating a large amount of data.

[0035] In some embodiments, at least some of the data acquisition subsystems can not only send the information they possess or collect to the cloud data processing center 205, but also receive instructions from the cloud data processing center 205 and perform corresponding operations in response to these instructions, such as issuing alarm prompts or performing locking actions.

[0036] like Figure 1 As shown, the data collected by the data acquisition subsystem 201 is sent to the cloud data processing center 205 via the centralized data interface 203. The centralized data interface 203 can be a data bus that provides streaming data transmission services / platforms, capable of streaming large amounts of data in real time. The centralized data interface 203 may include, for example, Kafka.

[0037] The cloud data processing center 205 receives source data related to the production process from the data acquisition system through a centralized data interface, processes the source data to obtain target data, and provides global services based on the call and analysis of the target data.

[0038] The cloud data processing center 205 may include a cloud data storage component 205-1, a cloud data processing component 205-2, and a cloud data analysis component 205-2. The cloud data storage component 205-1 may store source data and / or processed target data received from the data acquisition system and associated with the production process. The cloud data storage component 205-1 may also store historical data (e.g., historical source data / historical target data), as well as analysis results based on historical data.

[0039] The cloud data processing center 205 can obtain the target data by processing the source data obtained through the cloud data processing component 205-2.

[0040] Cloud data processing center 205 can structure source data to obtain structured data assets. Data assets can include common data models and data concept models. A common data model is a logical combination of data models where common relationships between domains are grouped together. Data from each domain is combined based on the purpose of the analysis. A data concept model categorizes data according to the principles of mutual exclusion and collective exhaustiveness, with each piece of data belonging to only one data domain. A data concept model is a framework with appropriate dimensions and levels to which all data can be mapped.

[0041] The cloud data processing center 205 can provide global services based on the invocation and analysis of target data through the cloud data analysis component 205-2. Here, global services refer to services provided that consider all related data in the entire production process (rather than local services based only on fragmented data of local nodes, devices, or systems in the production process).

[0042] The exemplary global digital quality monitoring and management system according to embodiments of this disclosure can be used to provide various global services, including but not limited to production availability services, zero-defect quality services, and high-efficiency work services. Production availability services include, for example, predictive maintenance of production equipment, supply chain management, and consumable lifespan management. Zero-defect quality services may include defect root cause analysis and supplier performance management. High-efficiency work services include, for example, daily analysis and reporting related to the production process, cycle time analysis, and production equipment troubleshooting.

[0043] Daily Quality Analysis Report

[0044] In production process management, various analytical reports are needed daily, such as production output reports, rework reports, and quality reports. The global digital quality monitoring and management system provided according to embodiments of this disclosure can conveniently and automatically generate various daily analytical reports in response to various user requests.

[0045] Figure 3 The system architecture of an exemplary global digital quality monitoring and management system 300 according to an embodiment of this disclosure is shown.

[0046] like Figure 3 As shown, system 300 can involve various source data, such as data related to the environment, machines, and refrigerants collected via IoT Edge; data related to cutting tools, tool lists, and cutting parameters obtained directly from the production line; and process and quality-related data obtained from various process and quality management subsystems. This data is transmitted to a cloud platform, i.e., a cloud data processing center, via a centralized data interface (e.g., Kafka). The cloud data processing center stores, processes, and analyzes the data, and provides analysis reports based on the results.

[0047] Users can access the cloud platform using various electronic devices (such as computers, mobile phones, tablets, etc.) to request analysis reports. In other words, the cloud platform can receive user requests and respond by providing corresponding analysis reports.

[0048] Although not shown, various applications that interface with the cloud platform can be developed to allow users to customize / request analytical reports through the application's user interface. The application provides / presents the requested analytical report to the user by accessing data and / or analytical results provided by the cloud platform.

[0049] Figure 4 This illustration shows a comparison between the analysis report formation process according to the prior art and the analysis report formation process according to embodiments of this disclosure.

[0050] like Figure 4 As shown on the left, in the prior art, multiple data specialists access multiple systems (e.g., factory control systems, quality information and control systems, production control systems, and material management systems) to obtain data and manually create multiple corresponding reports. Then, the data specialist performing the overall analysis retrieves these multiple reports, performs comprehensive analysis, and obtains a final analysis report.

[0051] In existing technologies, reports from various engine production workshops are manually created, with data copied, calculated, and summarized from different systems and reports, resulting in a significant waste of effort and time. The standards for analysis reports are opaque and inconsistent, making it difficult to guarantee objectivity. Furthermore, since information is collected manually from various systems and Excel files, the risk of human error is very high. In addition, the different timeframes for data analysis across workshops / departments and the complete reliance on manual analysis lead to delays in overall analysis results, often making it difficult to take timely and appropriate measures based on real-time situations.

[0052] In contrast, such as Figure 4 As shown on the right, using the global digital quality monitoring and management system according to embodiments of this disclosure, data from these multiple systems are centralized at the data processing center of a cloud platform, where they are uniformly processed and analyzed. After structured processing, the source data can be transformed into target data, such as crankcase comprehensive quality data.

[0053] Data specialists can directly access data and / or analysis results from the cloud platform. They can also utilize data visualization tools (such as Power IB) to access data at any time and automatically generate reports. Furthermore, data specialists in different departments can even utilize business self-service, meaning they can independently design interface content and functions on the cloud platform according to the needs of different departments, in order to access different data and / or analysis results as required.

[0054] As can be seen from the above, the global digital quality monitoring and management system according to embodiments of this disclosure can automatically perform data analysis and reporting based on comprehensive and accurate data through a transparent and standardized process (e.g., using unified analysis templates). This completely avoids the inefficiency, inconsistency, and errors of manual operation. Because the data is collected in real time, the analysis results are more conducive to making appropriate decisions in a timely manner. In addition, data specialists can easily access global data and customize analysis reports, enabling efficient and personalized analysis report output.

[0055] Defective parts tracking and locking

[0056] Figure 5 This illustrates a comparison between a defective part tracking and locking process according to the prior art and a defective part tracking and locking process according to an embodiment of this disclosure.

[0057] Figure 5 The left side illustrates the defective part tracking and locking process in the prior art. When a quality issue arises, a quality issue specialist accesses the factory control system to define the identification code for the problematic part (i.e., the defective part). Foundry quality specialists access the factory control system, quality information and control system, and system applications and product data processing systems to identify and lock or remove suspicious parts both inside and outside the foundry. Machining quality specialists access the production control system, material management system, and system applications and product data processing systems to identify and lock or remove suspicious parts both inside and outside the machining workshop. Final assembly quality specialists access the integrated powertrain management system to identify and lock or remove suspicious parts both inside and outside the final assembly workshop. Logistics personnel can access system applications and product data processing systems to identify suspicious parts and their locations during transportation and can lock them. The quality specialist responsible for information aggregation can summarize all the information identified above.

[0058] As can be seen from the above, when quality problems occur at the customer's site (such as crankcase and cylinder head quality issues), it is necessary to confirm the quantity of suspicious parts in each technical workshop (casting, machining, final assembly) of the engine production process, which takes a considerable amount of time to obtain results. This is because different departments have different systems (factory control system, quality information and control system, production control system, material management system, factory and machine information system, integrated powertrain management system, and system application and product data processing system, etc.). In existing technologies, it is necessary to search for related unused parts in each system to obtain detailed distribution information (such as quantity, location status, part status, etc.) and then lock these parts in the system. If suspicious parts cannot be locked in a timely manner, there is a risk that they will continue to be delivered to the customer, or they may be scrapped at a higher price. If the distribution of unused parts is not understood in a timely manner, safety stock cannot be effectively assessed, and safety stock risks will get out of control.

[0059] According to embodiments of this disclosure, the global digital quality monitoring and management system can quickly determine the distribution (including quantity and status) of related suspicious parts in all related departments / workshops based on quality problem data, and enable the suspicious parts to be quickly locked.

[0060] like Figure 5 As shown on the right, data from various systems (factory control system, quality information and control system, production control system, material management system, integrated powertrain management system, and system application and product data processing system) are uniformly transmitted to a cloud data processing center (e.g., Figure 2 (205 in the text). The source data is structured to form the target data, i.e., data assets, such as crankcase / cylinder head process data.

[0061] Users can request data analysis and retrieve results from the cloud data processing center, for example, through a customized defective parts traceability tool. The cloud data processing center can then retrieve the relevant data based on the request, perform analysis, and output the results to the user.

[0062] In some embodiments, cloud data processing centers (e.g.) Figure 2 (205) can identify suspicious parts associated with quality problems in each production subsystem based on quality problem data and target data, and instruct each production subsystem to lock the corresponding suspicious parts.

[0063] In some embodiments, quality problem data may include information such as the engine number and engine model of the engine with the quality problem. In some embodiments, quality problem data may include information such as casting identification number, machining identification number, and final assembly identification number associated with the engine with the quality problem.

[0064] In some embodiments, quality issue data may include casting time, mold number, worker group number, crucible number, crucible filling level, scrap and rework type, location, KPI category, and quantity information by shift, date, machine, etc.

[0065] The cloud data processing center can determine the quantity (e.g., unused parts used in the production of the same engine model) of suspicious parts associated with the engine experiencing quality problems, based on information from the quality problem data and its target data. This determination can be made using associated casting identification codes, machining identification codes, and assembly identification numbers. The location status (e.g., last processing stage and time, warehouse location status such as box location, warehousing time, etc.) and part status (e.g., whether it has undergone pre-processing, is locked, has a lock number, test number, etc.) can also be determined. In some embodiments, the cloud data processing center can further determine the production subsystems associated with the suspicious parts (e.g., the corresponding workshop, production line, or more specifically, the associated factory control system, quality information and control system, production control system, material management system, factory and machine information system, integrated powertrain management system, etc.) based on information from the quality problem data and its target data.

[0066] The cloud data processing center can instruct various production subsystems to lock corresponding suspicious parts. The cloud data processing center can send instructions or prompts to lock the corresponding suspicious parts to the respective production subsystems involved. In some embodiments, the cloud data processing center can send alarms or prompts to relevant personnel in each production subsystem, who can then lock the corresponding suspicious parts based on the alarms or prompts. Alarms and prompts may include the number and location of the suspicious parts. In other embodiments, the cloud data processing center can send instructions to the corresponding control systems of each production subsystem to trigger the respective control systems of each production subsystem to automatically lock the corresponding suspicious parts. Locking the corresponding suspicious parts may also mean that any operations associated with the suspicious parts, such as casting, machining, or assembly, cannot be performed.

[0067] In existing technologies, staff from various departments manually search for parts associated with the component experiencing a quality problem within their respective subsystems. This process is inefficient, time-consuming, prone to oversights, and fails to provide flexible and timely part locking. By utilizing the global digital quality monitoring and management system according to embodiments of this disclosure, the distribution of suspected unused parts can be quickly and accurately determined, including the quantity and status of suspicious parts, and the locking of suspicious parts can be achieved efficiently and promptly. The global digital quality monitoring and management system according to embodiments of this disclosure simplifies the process of determining the distribution of unused parts, confirming safety stock in a more transparent manner; it shortens the time required to lock unused parts, enabling rapid batch responses to quality problems; it reduces the risk of defective parts entering the next process, improving production efficiency; it saves scrap costs by detecting defective products early in the value stream; and it allows for better control of safety stock, ultimately improving customer satisfaction.

[0068] Predictive production equipment maintenance

[0069] In existing technology, when production equipment malfunctions, maintenance personnel are responsible for repairs. Equipment failure can lead to production stoppages and significant disruptions. The goal is to detect problems before equipment failures occur, allowing for timely maintenance and minimizing the risk of breakdowns.

[0070] The global digital quality monitoring and management system according to embodiments of this disclosure can help achieve predictive production equipment maintenance.

[0071] Cloud data processing centers (e.g.) Figure 2 205) can analyze historical target data to determine the correlation between production equipment failures and monitoring parameters, predict whether a production equipment failure will occur based on the analysis of real-time target data and the correlation, and output a production equipment maintenance prompt in response to the prediction of a production equipment failure based on the analysis of real-time target data.

[0072] As mentioned above, the cloud data processing center stores a large amount of historical target data, which originates from source data related to the entire production process in the past. By analyzing the historical target data, the correlation between production equipment failures and certain monitoring parameters can be determined.

[0073] Then, the cloud data processing center can predict whether a production equipment failure will occur based on the analysis of real-time target data and the aforementioned correlations. For example, suppose that analysis of historical target data reveals that a certain monitoring parameter's value continuously increases and eventually exceeds a threshold, indicating a type of failure in laser engraving equipment. Therefore, if the cloud data processing center's analysis of real-time target data shows that several monitoring parameters are continuously increasing and approaching the threshold, it can be determined that the corresponding failure of the laser engraving equipment is likely imminent. As another example, suppose that analysis of historical target data reveals that a type of failure in drilling equipment is associated with a non-zero value of a certain parameter (e.g., when the number of non-zero values ​​of this parameter continuously decreases and exceeds a threshold within a certain time period, this type of failure occurs in the drilling equipment). Therefore, if the cloud data processing center's analysis of real-time target data shows that the number of non-zero values ​​of this parameter continuously decreases and is about to reach the threshold within a certain time period, it means that the corresponding failure of the drilling equipment is likely imminent.

[0074] When a production equipment failure is anticipated, the cloud data processing center can output a maintenance alert. In some embodiments, the cloud data processing center can directly send alarms or alerts to the maintenance personnel (related systems and equipment) of the production equipment to ensure early maintenance. In other embodiments, for certain types of production equipment or failures (e.g., failures that may cause significant losses), control commands can be directly sent to the control system associated with the production equipment to suspend its operation.

[0075] In some embodiments, sensors can be used to acquire real-time parameters affecting the overall system performance, such as the pH value or bacterial concentration of the fluid, rotor vibration, etc., and to find the correlation between production equipment failures and these real-time parameters. The determined correlations can be used for subsequent prediction of production equipment failures.

[0076] The global digital quality monitoring and management system according to embodiments of this disclosure can help achieve predictive production equipment maintenance and ensure smooth production processes.

[0077] Analysis of the causes of production equipment failure

[0078] In existing technologies, effective analysis and handling of production equipment failures often rely on experienced operators, thus requiring a high level of technical expertise. Inexperienced operators typically need a longer time to conduct effective analysis or must resort to trial and error, resulting in prolonged repair times or unsatisfactory repair outcomes.

[0079] The global digital quality monitoring and management system according to embodiments of this disclosure can help to analyze the causes of production equipment failures and provide good guidance and assistance for operators in equipment maintenance.

[0080] Cloud data processing centers (e.g.) Figure 2 205) can analyze historical target data to determine the set of production equipment failures and causes; and determine the causes of production equipment failures based on production equipment failure data and the determined set of production equipment failures and causes.

[0081] In some embodiments, the cloud data processing center can integrate all data related to rework from historical target data, and combine the production equipment failures and their causes found in the rework data to obtain a set of production equipment failures and their causes. The set may include multiple elements, each of which may include, for example, a fault code and its corresponding cause.

[0082] In some embodiments, the cloud data processing center can also analyze historical target data to discover correlations between production equipment failures and certain factors, thereby uncovering hidden causes associated with the failures and supplementing the set of production equipment failures and their causes. In other words, these causes may not be derived from rework data, but rather through the data processing center's analysis of relevant data at the time of the production equipment failure to discover strong correlations between the failures and certain factors, thus identifying these factors as the causes corresponding to the failures.

[0083] A cloud data processing center can determine the causes of production equipment failures based on production equipment failure data and a set of identified failures and their causes. The production equipment failure data can include information such as failure type, failure description, failure code, and failure time. The cloud data processing center can extract, for example, failure codes from the production equipment failure data, and then retrieve the corresponding causes from the set of production equipment failures and their causes based on the failure codes.

[0084] Since the set of production equipment failures and their causes is obtained based on the analysis of relevant data throughout the entire production process, the causes corresponding to the failures are often quite comprehensive. Through the global digital quality monitoring and management system according to embodiments of this disclosure, even inexperienced operators can quickly obtain comprehensive analysis results of the causes of failures through simple interface operations, facilitating efficient completion of failure repairs. Therefore, the global digital quality monitoring and management system according to embodiments of this disclosure can significantly improve repair efficiency, shorten repair time, and reduce repair costs.

[0085] In addition to the above, the global digital quality monitoring and management system according to the embodiments of this disclosure can also assist in realizing various other functions, including but not limited to supply chain management, consumable life management, supplier performance management, cycle time analysis, etc.

[0086] Figure 6 A schematic diagram illustrating an exemplary data mapping according to an embodiment of this disclosure is shown.

[0087] like Figure 6 As shown, various source data from data sources (such as production control systems, measurement systems, material management systems, flexible production line systems, system applications, and product data processing systems) are processed to form various target data (i.e., data assets) for use in various use cases, such as production availability, zero defects in quality, and work efficiency.

[0088] like Figure 6 As shown, data from the data source is transmitted to the cloud platform's data pool, which includes structured data assets. These data assets include public data models and data concept models.

[0089] A common data model is a logical combination of data models in which common relationships between domains are grouped together. Data from each domain is combined based on the purpose of the analysis.

[0090] Public data models include, for example, data on the lifespan of cutting tools, scrapping data, equipment operation data, and actual tool data.

[0091] Data conceptual models categorize data based on the principles of mutual exclusion and collective exhaustiveness, with each piece of data belonging to only one data domain. A data conceptual model is a framework that maps all data to appropriate dimensions and levels.

[0092] The data conceptual model includes material data, equipment data, process data, quality data, etc.

[0093] Material data is data used to describe materials. There are two types of materials, both purchased from suppliers / vendors. One type is non-consumable materials, which are the raw materials that make up the final product. The other type is consumable materials, which are used in the production process but do not constitute the final product, such as lubricants.

[0094] Equipment data includes data related to the equipment itself or data generated by the equipment, which can be used to describe the equipment status, such as current and voltage.

[0095] Process data is data generated during production activities, triggered by the actions of equipment / people on materials and products.

[0096] Quality data is data used to describe a product. A product can include finished and semi-finished products after each process.

[0097] Figure 7 A schematic diagram illustrating an exemplary data mapping according to an embodiment of this disclosure is shown.

[0098] like Figure 7As shown, the production process generates various source data, which may be generated by different systems involved in the production process (such as production control systems, measurement systems, material management systems, flexible production line systems, tooling data systems, system applications, and product data processing systems). This source data is mapped to different data concept models, such as equipment data, process data, material data, or quality data. Data assets can be used in various use cases, such as supplier performance management, tool lifecycle management, predictive machine maintenance, and quality control.

[0099] Figure 8 This is a flowchart illustrating a global digital quality monitoring and management method 800 for a product manufacturing process according to an embodiment of the present disclosure.

[0100] like Figure 8 As shown, method 800 includes step 801, in which a data acquisition system acquires source data related to the production process in real time, wherein the data acquisition system includes multiple data acquisition subsystems, and the source data includes real-time data from at least some of the multiple data acquisition subsystems.

[0101] Method 800 may further include step 803, in which the cloud data processing center receives source data associated with the production process from the data acquisition system via a centralized data interface coupled to the data acquisition system, structures the source data to obtain structured target data, and provides global services based on the invocation and analysis of the target data.

[0102] The system and method according to embodiments of this disclosure enable end-to-end unified digital quality control of the entire engine production process. By collecting and analyzing real-time data from the entire process, the real-time status of various objects can be quantified, and the dynamic data network can be extended to all aspects related to the production process. Accurate and effective data analysis results can be used to optimize the production process, solving the problem in the prior art where information fragments are scattered across various entities and cannot be effectively utilized, and enabling the tracking and tracing of engine quality problems.

[0103] The systems and methods according to embodiments of this disclosure enable efficient and comprehensive daily analysis reports, supply chain management, operator performance evaluation, and more. They also allow for efficient analysis of the causes of production equipment failures, helping operators quickly identify the root causes, perform effective repairs, and reduce parts scrap and rework. Furthermore, they enable the prediction of production equipment failures, facilitating early equipment maintenance and preventing downtime.

[0104] Figure 9 This is a schematic diagram illustrating a general hardware environment in which a device according to an embodiment of the present disclosure can be implemented.

[0105] Now for reference Figure 9 The diagram illustrates an example of a compute node 900. Compute node 900 is merely one example of a suitable compute node and is not intended to imply any limitation on the scope of use or functionality of the embodiments described herein. In any case, compute node 900 is capable of implementing and / or performing any of the functions set forth above.

[0106] Within compute node 900, there exists a computer system / server 9012 that can operate with a wide range of other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations suitable for use with computer system / server 9012 include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the aforementioned systems or devices, etc.

[0107] A computer system / server 9012 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Generally, a program module can include routines, programs, objects, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. The computer system / server 9012 can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can reside on both local and remote computer system storage media, including memory storage devices.

[0108] like Figure 9 As shown, the computer system / server 9012 in computing node 900 is illustrated in the form of a general-purpose computing device. The components of the computer system / server 9012 may include, but are not limited to: one or more processors or processing units 9016, system memory 9028, and a bus 9018 that couples the various system components, including the system memory 9028, to the processing unit 9016.

[0109] Bus 9018 represents any one or more of several types of bus architectures, including memory buses or memory controllers, peripheral buses, accelerated graphics ports, processors, or local buses using any of the various bus architectures. By way of example and not limitation, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Microchannel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, Peripheral Component Interconnect (PCI) buses, Peripheral Component Interconnect High Speed ​​(PCIe) buses, and Advanced Microcontroller Bus Architecture (AMBA).

[0110] Computer systems / servers 9012 typically include a variety of computer system-readable media. These media can be any available media accessible by the computer system / server 9012, including volatile and non-volatile media, removable and non-removable media.

[0111] System memory 9028 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 9032. Computer system / server 9012 may also include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 9034 may be provided for reading from and writing to a non-removable non-volatile magnetic medium (not shown, and generally referred to as a "hard disk drive"). Although not shown, a disk drive may be provided for reading from and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disc drive may be provided for reading from and writing to a removable non-volatile optical disc (such as a CD-ROM, DVD-ROM, or other optical media). In these cases, each may be connected to bus 9018 via one or more data media interfaces. As will be further described and illustrated below, memory 9028 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of embodiments of this disclosure.

[0112] By way of example and not limitation, a program / utility 9040 having a set (at least one) of program modules 9042, along with an operating system, one or more application programs, other program modules, and program data, may be stored in memory 9028. Each of the operating system, one or more application programs, other program modules, and program data, or some combination thereof, may include an implementation of a network environment. Program modules 9042 generally perform functions and / or methods as described in the embodiments herein.

[0113] The computer system / server 9012 can also communicate with one or more external devices 9014 (such as a keyboard, indicating device, display 9024, etc.), one or more devices that enable a user to interact with the computer system / server 9012, and / or any device that enables the computer system / server 9012 to communicate with one or more other computing devices (e.g., a network card, modem, etc.). This communication can occur via input / output (I / O) interface 22. Furthermore, the computer system / server 9012 can communicate with one or more networks (such as a local area network (LAN), a general area network (WAN), and / or a public network (e.g., the Internet)) via network adapter 20. As depicted, network adapter 20 communicates with other components of the computer system / server 9012 via bus 9018. It should be understood that, although not shown, other hardware and / or software components can be used in conjunction with the computer system / server 9012. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archiving storage systems.

[0114] This disclosure can be implemented as a system, method, and / or computer program product. The computer program product may include one or more computer-readable storage media having computer-readable program instructions thereon for causing a processor to perform aspects of this disclosure.

[0115] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example (but not limited to), electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital universal disc (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punch cards or recessed protrusions storing instructions thereon), and any suitable combination of the foregoing. As used herein, computer-readable storage media is not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0116] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network (e.g., the Internet, local area network, wide area network, and / or wireless network) to an external computer or external storage device. The network may include copper cables, fiber optic cables, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media within the respective computing / processing device.

[0117] Computer-readable program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may be personalized by utilizing state information from the computer-readable program instructions to perform aspects of this disclosure.

[0118] This document describes aspects of the present disclosure with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0119] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that, when executed by the processor of the computer or other programmable data processing apparatus, these instructions create means for implementing the functions / behaviors specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to operate in a particular manner, thereby including an article of manufacture comprising instructions for implementing aspects of the functions / behaviors specified in one or more blocks of the flowchart and / or block diagram.

[0120] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, thereby causing the instructions to be executed on the computer, other programmable apparatus, or other device to perform the functions / behaviors specified in one or more boxes of a flowchart and / or block diagram.

[0121] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a portion of a module, segment, or instruction containing one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the figures. For example, depending on the functions involved, two consecutive blocks may actually be executed substantially in parallel, or these blocks may sometimes be executed in reverse order. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or behavior or executes a combination of dedicated hardware and computer instructions.

[0122] Those skilled in the art should also understand that the various operations illustrated in sequence in the embodiments of this disclosure do not necessarily have to be performed in the illustrated order. Those skilled in the art can adjust the order of operations as needed. They can also add more operations or omit some operations as needed.

[0123] Various embodiments of this disclosure have been described for illustrative purposes, but are not intended to be exhaustive or limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles of the embodiments, their practical application, or technical improvements to technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A global digital quality monitoring and management system for product manufacturing processes, comprising: A data acquisition system includes multiple data acquisition subsystems, wherein the data acquisition system is configured to acquire source data related to the production process, the source data including real-time data from at least some of the multiple data acquisition subsystems; A centralized data interface coupled to the data acquisition system; as well as The cloud data processing center is configured as follows: Receive source data related to the production process from the data acquisition system via a centralized data interface; Processing source data to obtain target data includes structuring the source data to obtain structured data assets. These data assets include a common data model and a data concept model. The common data model is a logical combination of data models where common relationships between domains are grouped together. Data from each domain is combined based on the purpose of the analysis. The data concept model categorizes data according to the principles of mutual exclusion and collective exhaustiveness. Each piece of data belongs to only one data domain, and the data concept model is a framework with appropriate dimensions and levels to which all data can be mapped. Global services are provided based on the access and analysis of target data. These global services refer to services that consider all related data throughout the entire production process. The cloud data processing center is configured as follows: Analyzing historical target data derived from source data related to the entire production process to determine the correlation between production equipment failures and monitoring parameters; and Based on the analysis of real-time target data and the correlation, it is possible to predict whether a production equipment failure will occur.

2. The system of claim 1, wherein, The plurality of data acquisition subsystems include at least one of the following: One or more sensors; and One or more process and quality management subsystems.

3. The system as recited in claim 1, wherein, The source data includes at least one of the following: Environmental data related to the environment involved in the production process; Machine data related to machining; Coolant data; Data related to cutting tools; Data related to the tools list; Data related to cutting parameters; and Data related to process and quality management.

4. The system of claim 1, wherein the cloud data processing center is configured to perform at least one of the following based on the invocation and analysis of target data: Provide daily quality reports based on target data; Track and locate defective parts; Predictive production equipment maintenance; Analysis of the causes of production equipment failure; supply chain management; Consumable lifespan management; Supplier performance management; and Beat timing analysis.

5. The system as described in claim 1, wherein the cloud data processing center is configured as follows: Based on quality problem data and target data, identify suspicious parts in each production subsystem that are associated with quality problems; Instruct each production subsystem to lock down the corresponding suspicious parts.

6. The system of claim 1, wherein the cloud data processing center is configured as follows: In response to a prediction of a production equipment failure based on analysis of real-time target data, a production equipment maintenance prompt is output.

7. The system of claim 1, wherein the cloud data processing center is configured as follows: Analyze historical target data derived from source data related to the entire production process to determine the set of production equipment failures and their causes; and The causes of production equipment failures are determined based on production equipment failure data and a set of identified production equipment failures and their causes.

8. The system of any one of claims 1-7, wherein the target data comprises at least one of the following: Cutting tool lifespan data; Discard data; Equipment operation data; Actual data from the tool; Material data; Equipment data; Process data; and Quality data.

9. A method for global digital quality monitoring and management of product manufacturing processes, comprising: The data acquisition system collects source data related to the production process in real time, and the data acquisition system includes multiple data acquisition subsystems. and The cloud data processing center receives source data related to the production process from the data acquisition system via a centralized data interface coupled with the data acquisition system. It then structures the source data to obtain structured target data and provides global services based on the access and analysis of the target data. The structured target data includes a common data model and a data concept model. The common data model is a logical combination of data models in which common relationships between domains are combined. Data from each domain is combined based on the purpose of the analysis. The data concept model classifies data according to the principles of mutual exclusion and collective exhaustiveness. Each piece of data belongs to only one data domain. The data concept model is a framework with appropriate dimensions and levels to which all data can be mapped. Global services refer to services that consider all related data throughout the entire production process. The cloud data processing center will perform the following operations: Analyze historical target data to determine the correlation between production equipment failures and monitoring parameters; and Based on the analysis of real-time target data and the correlation, it is possible to predict whether a production equipment failure will occur.

10. A computer-readable storage medium storing computer-readable program instructions thereon, said instructions, when executed by a processor, causing the processor to perform the method of claim 9.

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