Aviation fastener quality control method and system

Abstract

The invention discloses an aviation fastener quality control method and system, belongs to the technical field of production control systems, and aims to solve the problems that multiple tests are needed before the existing aviation fastener is produced, the test steps are tedious, and the test period is long. Based on comprehensive collection, preprocessing, hierarchical analysis and decision support of data and effective storage and management of the data, key data in the production process are collected in real time, and after cleaning and arrangement, the system adopts an analytic hierarchy process to decompose a quality control target into a plurality of hierarchies, including a target layer, a criterion layer and a sub-criterion layer, so that the quality of the product is controlled. A clear quality control framework is formed, through expert consultation and statistical analysis, a pairwise comparison judgment matrix is constructed, the relative importance between elements of all levels is quantified, a weight vector is calculated, consistency check is conducted on the judgment matrix, and a reasonable threshold range is set through a statistical method. Scientific basis is provided for optimization of technological parameters and adjustment of threshold values, and test efficiency and product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production control systems, and particularly to a method and system for quality control of aviation fasteners. Background Art

[0002] Aviation fasteners, such as bolts, nuts, rivets, etc., play an important role in connecting, fastening and supporting in the aircraft structure. Their quality is directly related to the integrity and stability of the aircraft structure. Once the fasteners break, become loose or fail, it may lead to the detachment of aircraft components, structural damage, and even air crashes. Therefore, improving the production quality of fasteners is the primary task to ensure flight safety. And with the development of global competition and the acceleration of the informatization process in the manufacturing industry, enterprises have put forward higher requirements for quality management. While total quality management is increasingly recognized and valued by enterprises, the attention of enterprises to quality management has gradually expanded from traditional quality information management to the comprehensive management of various factors in the entire product life cycle.

[0003] Aviation fasteners adopt different production standards according to their different use positions and environments. However, since various factors during production will directly affect the final product quality, multiple manual experiments are required. Due to the large number of steps in the production of aviation fasteners, the test cycle is relatively long. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for quality control of aviation fasteners, which solves the problem in the background art that multiple tests are required before the production of existing aviation fasteners, and the test steps are cumbersome and the cycle is long.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for quality control of aviation fasteners, comprising the following steps:

[0006] S1. Data import and standard setting: Use a data management system to import historical production data, including raw material inspection data, key parameter records during production, and finished product inspection results. These data will serve as the basis and reference for quality control; Based on industry standards, customer requirements, and internal experience, set clear quality control objectives and standards. The standards cover all aspects of material properties, manufacturing processes, dimensional accuracy, and surface quality, and clarify the specific requirements of each sub-criterion;

[0007] S2. Criterion and Sub-criterion Setting: Clearly define the main aspects of quality control. The criteria include material properties, manufacturing processes, dimensional accuracy, and surface quality to ensure coverage of all key quality control points. Under the criterion level, refine and set specific sub-criteria, including the content rate of non-metallic impurities, metallographic structure, decarburized layer depth, grain size, temperature and pressure parameters during forming, heat treatment process, machining accuracy, length and diameter tolerances, thread accuracy, roughness, rust degree, and oil stain control. Each sub-criterion should have a clear definition, detection method, and acceptance criteria.

[0008] S3. Judgment Matrix Construction and Weight Calculation: Based on the hierarchical analysis model and historical data, pairwise compare the elements in the sub-criterion layer by industry experts and technical personnel to construct a judgment matrix and calculate the relative weights of each element. The weights reflect the importance degree of each element in quality control.

[0009] S4. Weight Inspection and Consistency Evaluation: Conduct a consistency inspection on the calculated weights to ensure the logical consistency in the judgment matrix. If the CR value is less than the set threshold, it is considered that the judgment matrix has consistency; otherwise, the judgment matrix needs to be readjusted until the consistency requirement is met.

[0010] S5. Identification of Influencing Factors and Threshold Adjustment: According to the total hierarchical sorting results, identify the factors that have the greatest impact on product quality, which will become the focus of subsequent quality improvement. For the identified main factors, adjust the corresponding detection thresholds. The threshold adjustment is based on historical data, industry standards, and customer requirements to ensure effective problem identification and cost increase avoidance due to over-detection.

[0011] S6. Monitoring of Quality Control Process: Deploy sensors and monitoring cameras at the production site to collect data from each process in real time. The data covers all aspects of raw material inspection, processing, and finished product inspection. Compare the real-time data with the preset thresholds and analyze whether the data exceeds the normal range. For abnormal data, an alarm should be triggered immediately.

[0012] S7. Quality Improvement and Optimization: For the triggered alarms, organize technical personnel to conduct root cause analysis. Through data analysis and on-site investigation methods, find out the root causes of the anomalies. According to the root cause analysis results, formulate specific improvement measures and track and evaluate the implementation effects. The evaluation results will be used as a reference and basis for subsequent quality control.

[0013] S8. Continuous Optimization of Quality Control System: Regularly review the quality control system to evaluate its effectiveness and adaptability. The review content includes quality control processes, detection standards, and threshold settings. According to the review results and changes in customer requirements, continuously optimize the quality control system. The optimization measures include introducing new technologies and methods, and adjusting detection standards.

[0014] Another technical solution proposed by the present invention:

[0015] A quality control system used in a method for quality control of aviation fasteners, comprising the following modules:

[0016] Data acquisition module: responsible for collecting data of each process in the production process of aviation fasteners in real time, including data on material properties, manufacturing processes, dimensional accuracy, and surface quality. The data sources are sensors, detection instruments, and manual input;

[0017] Data preprocessing module: cleans, organizes, and converts the collected raw data to ensure the quality and consistency of the data, involving data deduplication, outlier handling, and data format conversion;

[0018] Quality control module: used to establish a hierarchical structure, construct a judgment matrix, calculate the weight vector, and perform consistency tests.

[0019] Further, the hierarchical structure established by the quality control module includes:

[0020] Set the goal layer: improve corrosion resistance, fracture resistance, fatigue resistance, and durability;

[0021] Set the criterion layer: material properties, manufacturing processes, dimensional accuracy, and surface quality.

[0022] Set the sub-criterion layer: set according to the provided sub-criterion list.

[0023] Further, the application steps of the quality control module are as follows:

[0024] S41: Construct a pairwise comparison judgment matrix. For each element in the criterion layer and the sub-criterion layer, construct a pairwise comparison judgment matrix;

[0025] S42: Calculate the relative importance of elements under a single criterion, calculate the maximum eigenvalue λ

[0028] ,

[0027] ,

[0026] ,

[0025] ,

[0029] ,

[0024] , , , max , , , and the corresponding eigenvector W. The elements of the eigenvector W are the weights of each element;

[0026] S43: Determine whether the consistency of the judgment matrix is acceptable;

[0027] S44: Calculate layer by layer from top to bottom to obtain the weight of each element with respect to the overall goal;

[0028] S45: Sort the total weights of the elements at each level and check their consistency.

[0029] Further, the judgment matrix is constructed in the quality control module using the 1-9 scale method, where 1 indicates that two elements are equally important, 9 indicates that one element is extremely more important than the other, and the intermediate numbers indicate different degrees of relative importance, to quantify the judgments of experts.

[0030] Further, the quality control module calculates the weight vector using the root method in the analytic hierarchy process, and its calculation formula is:

[0031] Calculate the vector Each element M of vector M i is the product of all elements in the i-th row of A;

[0032] Calculate the vector Each element N of vector N i is the n-th root of M i ;

[0033] Normalize to obtain the eigenvector Each element W of eigenvector W i is the result of N i divided by the sum of all elements of N.

[0034] Further, the quality control module conducts a consistency test through the following formula:

[0035]

[0036] where CR is the ratio of CI to the random consistency index RI, and RI is a constant related to the matrix order n, which can be obtained by looking up the table.

[0037] Further, in S3, the consistency of the judgment matrix is determined by calculating the consistency index CI and the consistency ratio CR. If CR < 0.10, the consistency of the judgment matrix is acceptable; otherwise, the judgment matrix needs to be readjusted.

[0038] Further, the quality control system further includes:

[0039] Decision support module: providing visual quality control results, helping decision-makers quickly understand the problem points and improvement directions in the production process, and making data-driven decisions to optimize process parameters and adjust thresholds;

[0040] Data storage and management module: responsible for storing and managing historical data, quality control results, and decision-making records, and providing data query, report generation, and data export functions.

[0041] Further, the decision support module sets the upper and lower limits according to the standard deviation of historical data, and determines a reasonable threshold range by using statistical methods with reference to industry standards and industry expert opinions.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] A method and system for quality control of aviation fasteners provided by the present invention, based on comprehensive data collection, preprocessing, hierarchical analysis and decision support, as well as effective data storage and management. By collecting key data in the production process in real time, after cleaning and sorting, the system uses the analytic hierarchy process to decompose the quality control objectives into multiple levels, including the target level, the criterion level and the sub-criterion level, forming a clear quality control framework. Through expert consultation and statistical analysis, a pairwise comparison judgment matrix is constructed to quantify the relative importance between elements at each level, and the weight vector is calculated. A consistency test is performed on the judgment matrix to ensure logical rationality and result reliability. By statistical methods, a reasonable threshold range is set to provide a scientific basis for the optimization of process parameters and the adjustment of thresholds, improving the test efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system module of the present invention;

[0045] Figure 2 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] In order to solve the technical problem that multiple tests are required before the production of existing aviation fasteners, and the test steps are cumbersome and the cycle is long, as Figure 1 - Figure 2 shown, the following preferred technical solutions are provided:

[0048] A method for quality control of aviation fasteners includes the following steps:

[0049] S1. Data import and standard setting: Use a data management system to import historical production data, including raw material inspection data, key parameter records in the production process, and finished product inspection results. These data will be used as the basis and reference for quality control; Based on industry standards, customer requirements and internal experience, clear quality control objectives and standards are set. The standards cover all aspects of material properties, manufacturing processes, dimensional accuracy and surface quality, and the specific requirements of each sub-criterion are clarified;

[0050] S2. Criteria and Sub - criteria Setting: Clearly define the main aspects of quality control. The criteria include material properties, manufacturing processes, dimensional accuracy, and surface quality to ensure coverage of all key quality control points. Under the criterion level, refine and set specific sub - criteria, including the content rate of non - metallic impurities, metallographic structure, decarburized layer depth, grain size, temperature and pressure parameters during forming, heat treatment process, machining accuracy, length - diameter tolerance and thread accuracy, roughness, rust degree, and oil stain control. Each sub - criterion should have a clear definition, detection method, and acceptance criteria.

[0051] S3. Judgment Matrix Construction and Weight Calculation: Based on the hierarchical analysis model and historical data, pairwise compare the elements in the sub - criterion layer by industry experts and technical personnel to construct a judgment matrix and calculate the relative weights of each element. The weights reflect the importance degree of each element in quality control.

[0052] S4. Weight Inspection and Consistency Evaluation: Conduct a consistency inspection on the calculated weights to ensure the logical consistency in the judgment matrix. If the CR value is less than the set threshold, it is considered that the judgment matrix has consistency; otherwise, the judgment matrix needs to be readjusted until the consistency requirement is met.

[0053] S5. Influence Factor Identification and Threshold Adjustment: According to the total hierarchical sorting results, identify the factors that have the greatest impact on product quality. These factors will be the focus of subsequent quality improvement. For the identified main factors, adjust the corresponding detection thresholds. The threshold adjustment is based on historical data, industry standards, and customer requirements to ensure effective problem identification while avoiding increased costs caused by over - detection.

[0054] S6. Quality Control Process Monitoring: Deploy sensors and monitoring cameras at the production site to collect data from each process in real - time. The data covers all aspects of raw material inspection, processing, and finished product inspection. Compare the real - time data with the preset thresholds and analyze whether the data exceeds the normal range. For abnormal data, an alarm should be triggered immediately.

[0055] S7. Quality Improvement and Optimization: For the triggered alarms, organize technical personnel to conduct root - cause analysis. Through data analysis and on - site investigation methods, find out the root causes of the anomalies. According to the root - cause analysis results, formulate specific improvement measures, and track and evaluate the implementation effects. The evaluation results will be used as a reference and basis for subsequent quality control.

[0056] S8. Continuous Optimization of the Quality Control System: Regularly review the quality control system to evaluate its effectiveness and adaptability. The review content includes quality control processes, detection standards, and threshold settings. According to the review results and changes in customer requirements, continuously optimize the quality control system. The optimization measures include introducing new technologies and methods, and adjusting detection standards.

[0057] To further better explain the above embodiments, the present invention also provides an implementation scheme, a quality control system used in an aviation fastener quality control method, including the following modules:

[0058] Data acquisition module: responsible for collecting data of each process in the production process of aviation fasteners in real time, including data on material properties, manufacturing processes, dimensional accuracy, and surface quality. The data sources are sensors, detection instruments, and manual input;

[0059] Data preprocessing module: cleans, organizes, and converts the collected raw data to ensure the quality and consistency of the data, involving data deduplication, outlier processing, and data format conversion;

[0060] Quality control module: used to establish a hierarchical structure, construct a judgment matrix, calculate the weight vector, and perform consistency tests.

[0061] The hierarchical structure established by the quality control module includes:

[0062] Set the goal layer: improve corrosion resistance, fracture resistance, fatigue resistance, and durability;

[0063] Set the criterion layer: material properties, manufacturing processes, dimensional accuracy, and surface quality.

[0064] Set the sub-criterion layer: set according to the provided sub-criterion list.

[0065] The application steps of the quality control module are as follows:

[0066] S41: Construct a pairwise comparison judgment matrix. For each element in the criterion layer and sub-criterion layer, construct a pairwise comparison judgment matrix;

[0067] S42: Calculate the relative importance of elements under a single criterion, calculate the maximum eigenvalue λ max and the corresponding eigenvector W. The elements of the eigenvector W are the weights of each element;

[0068] S43: Determine whether the consistency of the judgment matrix is acceptable;

[0069] S44: Calculate layer by layer from top to bottom to obtain the weight of each element for the overall goal;

[0070] S45: Sort the total weights of the elements at each level and check their consistency.

[0071] In the quality control module, the judgment matrix is constructed using the 1-9 scale method, where 1 indicates that two elements are equally important, 9 indicates that one element is extremely more important than the other, and the intermediate numbers indicate different degrees of relative importance to quantify the judgments of experts.

[0072] The quality control module calculates the weight vector using the root method in the analytic hierarchy process, and its calculation formula is as follows:

[0073] Calculate vector Each element M of vector M i is the product of all elements in the i-th row of A;

[0074] Calculate vector Each element N of vector N i is i the n-th root of M;

[0075] Normalize to obtain the eigenvector Each element W of the eigenvector W i is i the result of dividing N by the sum of all elements of N.

[0076] The quality control module conducts a consistency test through the following formula:

[0077]

[0078] Among them, CR is the ratio of CI to the random consistency index RI, and RI is a constant related to the matrix order n, which can be obtained by looking up the table.

[0079] In S3, the consistency of the judgment matrix is judged by calculating the consistency index CI and the consistency ratio CR. If CR < 0.10, the consistency of the judgment matrix is considered acceptable; otherwise, the judgment matrix needs to be readjusted.

[0080] The quality control system further includes:

[0081] Decision support module: Provides visual quality control results, helps decision-makers quickly understand the problem points and improvement directions in the production process, and makes data-based decisions to optimize process parameters and adjust thresholds;

[0082] Data storage and management module: Responsible for storing and managing historical data, quality control results, and decision records, and providing data query, report generation, and data export functions.

[0083] The decision support module sets the upper and lower limits according to the standard deviation of historical data, and determines a reasonable threshold range by using statistical methods with reference to industry standards and the opinions of industry experts.

[0084] Specifically, various methods such as sensors, detection instruments, and manual input are used to collect data on each process in the production of aviation fasteners in real time, including detailed information on material properties, manufacturing processes, dimensional accuracy, and surface quality. The collected raw data is cleaned, sorted, and converted to ensure data quality and consistency. This process involves data deduplication, outlier handling, and unified conversion of data formats, providing an accurate and reliable data foundation for subsequent analysis. Set the target layer, criterion layer, and sub-criterion layer to form a clear quality control hierarchy. Using the 1-9 scale method, based on expert opinions or historical data, construct a pairwise comparison judgment matrix to quantify the relative importance between elements. Use the root method in the analytic hierarchy process to calculate the weight vector of each element, and ensure the logical consistency of the judgment matrix through consistency testing. Calculate layer by layer from top to bottom to obtain the weight of each element for the overall goal and sort them, providing a data basis for subsequent decision-making support. Provide a visual display of quality control results to help decision-makers quickly identify problem points and improvement directions in the production process. Based on the standard deviation of historical data, industry standards, and expert opinions, set a reasonable threshold range through statistical methods to provide decision-making support for the optimization of process parameters and the adjustment of thresholds. Be responsible for storing and managing historical data, quality control results, and decision records to ensure data traceability and analyzability. Provide data query, report generation, and data export functions for further analysis and utilization of data.

[0085] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article, or device.

[0086] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for quality control of aviation fasteners, characterized in that, It includes the following steps: S1. Data Import and Standard Setting: Use a data management system to import historical production data, including raw material inspection data, key parameter records during the production process, and finished product inspection results. These data will serve as the basis and reference for quality control; Based on industry standards, customer requirements, and internal experience, set clear quality control objectives and standards. The standards cover all aspects of material properties, manufacturing processes, dimensional accuracy, and surface quality, and specify the specific requirements for each sub-criterion; S2. Criterion and Sub-criterion Setting: Define the main aspects of quality control. The criteria include material properties, manufacturing processes, dimensional accuracy, and surface quality, ensuring that all key quality control points are covered; Under the criterion level, refine and set specific sub-criteria, including the content rate of non-metallic impurities, metallographic structure, decarburized layer depth, grain size, temperature and pressure parameters during forming, heat treatment process, machining accuracy, length and diameter tolerance, and thread accuracy, roughness, rust degree, and oil stain control. Each sub-criterion should have a clear definition, detection method, and acceptance standard; S3. Judgment Matrix Construction and Weight Calculation: Based on the analytic hierarchy model and historical data, conduct pairwise comparisons of the elements in the sub-criterion layer by industry experts and technical personnel to construct a judgment matrix and calculate the relative weights of each element. The weights reflect the importance degree of each element in quality control; S4. Weight Inspection and Consistency Evaluation: Conduct a consistency inspection on the calculated weights to ensure the logical consistency in the judgment matrix. If the CR value is less than the set threshold, it is considered that the judgment matrix has consistency; Otherwise, the judgment matrix needs to be adjusted until the consistency requirement is met; S5. Influence Factor Identification and Threshold Adjustment: According to the total hierarchy ranking results, identify the factors that have the greatest impact on product quality. These factors will become the focus of subsequent quality improvement; For the identified main factors, adjust the corresponding detection thresholds. The threshold adjustment is based on historical data, industry standards, and customer needs to ensure that problems can be effectively identified while avoiding increased costs caused by over-detection; S6. Quality Control Process Monitoring: Deploy sensors and monitoring camera devices at the production site to collect data from each process in real time. The data covers all aspects of raw material inspection, processing, and finished product inspection; Compare the real-time data with the preset thresholds, analyze whether the data exceeds the normal range, and for abnormal data, an alarm should be triggered immediately; S7. Quality Improvement and Optimization: For the triggered alarms, organize technical personnel to conduct cause analysis. Through data analysis and on-site investigation methods, find out the root causes of the anomalies. According to the cause analysis results, formulate specific improvement measures, and track and evaluate the implementation effects. The evaluation results will serve as the reference and basis for subsequent quality control; S8. Continuous Optimization of the Quality Control System: Regularly review the quality control system to evaluate its effectiveness and adaptability. The review content includes quality control processes, detection standards, and threshold settings; According to the review results and changes in customer needs, continuously optimize the quality control system. The optimization measures include introducing new technologies and methods, and adjusting detection standards.

2. A quality control system used in the quality control method of an aviation fastener as described in claim 1, characterized in that: It includes the following modules: Data acquisition module: Responsible for collecting data of each process in the production of aviation fasteners in real time, including data on material properties, manufacturing processes, dimensional accuracy, and surface quality. The data sources are sensors, testing instruments, and manual input. Data preprocessing module: Cleans, organizes, and transforms the collected raw data to ensure data quality and consistency, involving data deduplication, outlier handling, and data format conversion. Quality control module: Used to establish a hierarchical structure, construct judgment matrices, calculate weight vectors, and perform consistency tests.

3. The quality control system of an aviation fastener quality control method according to claim 2, characterized in that: The hierarchical structure established by the quality control module includes: Set the goal layer: Improve corrosion resistance, fracture resistance, fatigue resistance, and durability. Set the criterion layer: Material properties, manufacturing processes, dimensional accuracy, and surface quality. Set the sub-criterion layer: Set according to the provided sub-criterion list.

4. The quality control system of an aviation fastener quality control method according to claim 2, characterized in that: The application steps of the quality control module are as follows: S41: Construct pairwise comparison judgment matrices. For each element in the criterion layer and sub-criterion layer, construct pairwise comparison judgment matrices. S42: Calculate the relative importance of elements under a single criterion, and calculate the maximum eigenvalue λ of the judgment matrix max and the corresponding eigenvector W. The elements of the eigenvector W are the weights of each element; S43: Determine whether the consistency of the judgment matrix is acceptable. S44: Calculate layer by layer from top to bottom to obtain the weight of each element with respect to the overall goal. S45: Sort the total weights of the elements at each level and check their consistency.

5. The quality control system of an aviation fastener quality control method according to claim 4, characterized in that: In the quality control module, the judgment matrix is constructed using the 1-9 scale method, where 1 means two elements are equally important, 9 means one element is extremely more important than the other, and the intermediate numbers represent different degrees of relative importance to quantify the judgments of experts.

6. The quality control system of an aviation fastener quality control method as described in claim 4, characterized in that: The quality control module uses the root method in the analytic hierarchy process to calculate the weight vector, and its calculation formula is: Calculated vector Each element M of vector M i is the product of all elements in the i-th row of A; Calculation vector Each element N of vector N i is M i the nth root of; Normalize to obtain the eigenvector Each element \(W\) of the eigenvector \(W\) i is \(N\) i divided by the sum of all elements of \(N\).

7. The quality control system of an aviation fastener quality control method as described in claim 4, characterized in that: The quality control module performs consistency tests through the following formula: Where CR is the ratio of CI to the random consistency index RI, and RI is a constant related to the matrix order n, which can be obtained by looking up the table.

8. The quality control system of an aviation fastener quality control method according to claim 4, characterized in that: In S3, the consistency of the judgment matrix is determined by calculating the consistency index CI and the consistency ratio CR. If CR < 0.10, the consistency of the judgment matrix is acceptable; otherwise, the judgment matrix needs to be adjusted again.

9. The quality control system of an aviation fastener quality control method according to claim 2, characterized in that: The quality control system also includes: Decision support module: Provides visual quality control results, helps decision-makers quickly understand the problem points and improvement directions in the production process, and makes data-based decisions to optimize process parameters and adjust thresholds. Data storage and management module: Responsible for storing and managing historical data, quality control results, and decision records, and providing data query, report generation, and data export functions.

10. The quality control system of an aviation fastener quality control method according to claim 9, characterized in that: The decision support module sets the upper and lower limits based on the standard deviation of historical data, and determines a reasonable threshold range by using statistical methods with reference to industry standards and the opinions of industry experts.

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