An assembly path planning device and method for flexible sensor chips used in textile accessories
By acquiring textile path and motion data and performing composite index analysis, the problem of inaccurate yarn damage prediction by sensing technology was solved, and quantitative assessment of damage and selection of optimal path were achieved, thereby improving the safety and efficiency of the textile process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGXI INST OF FASHION TECH
- Filing Date
- 2026-04-10
- Publication Date
- 2026-06-30
AI Technical Summary
Existing technologies lack a systematic quantitative assessment of the complex stresses on sensing yarns during dynamic weaving processes. This makes it impossible to predict and differentiate damage risks caused by excessive bending, abnormal stretching, and motion interference under different path planning schemes, resulting in inaccurate damage prediction.
By acquiring data on the curvature of the textile path, the movement of the chip sensing yarn, and the movement of the textile needle, we conduct path curvature change impact analysis and chip sensing yarn damage analysis, and combine this with optimization index analysis to select the optimal assembly path.
It enables quantitative assessment of damage to sensing yarns, improves the accuracy and objectivity of damage prediction, provides decision support for path planning, and avoids damage.
Smart Images
Figure CN121998220B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of path planning, specifically an assembly path planning device and method for flexible sensing chips used in textile accessories. Background Technology
[0002] With the rapid development of smart textiles and wearable devices, integrating functional units such as chips and sensors into the fabric in the form of sensing yarns has become a key technology path to realize cutting-edge applications such as continuous monitoring of human physiological signals, human-computer interaction, and environmental perception. However, in the manufacturing process of actually assembling or weaving such chip sensing yarns into textiles, how to plan the weaving path to protect them from damage is a problem currently facing the industry.
[0003] Currently, in the field of textile engineering, path planning for ordinary yarns mainly considers production efficiency, fabric texture aesthetics, and mechanical strength. Its optimization objectives are singular. When the object is upgraded to sensor yarns that integrate precision chips and circuits, existing technical solutions reveal the following serious shortcomings:
[0004] Existing technologies lack a systematic quantitative assessment of the complex stresses experienced by sensing yarns during dynamic weaving. Relying solely on experience to set conservative process parameters (such as uniformly reducing weaving speed and increasing the radius of curvature of the path) or conducting offline detection after damage occurs makes it impossible to predict and distinguish the damage risks caused by excessive bending (curvature exceeding limits), abnormal stretching (strain exceeding limits), and motion interference (needle entanglement) under different path planning schemes in advance.
[0005] Existing technologies often consider a single factor in isolation, such as only checking whether the geometric curvature of the path exceeds a certain empirical threshold, ignoring the coupling characteristics of damage. For example, a path with acceptable geometric curvature may be damaged during high-speed textile spinning due to dynamic stretching or complex relative motion with the needle. To address the problems raised in this background technology, this application designs an assembly path planning device and method for flexible sensing chips used in textile accessories. Summary of the Invention
[0006] To address the aforementioned technical shortcomings, this application proposes an assembly path planning device and method for flexible sensing chips used in textile accessories.
[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This application provides an assembly path planning method for a flexible sensing chip for textile accessories, which includes the following specific steps:
[0008] S1. Obtain data on the bending of the textile path, the movement of the yarn sensed by the chip, and the movement of the textile needle in each path planning scheme.
[0009] S2. Based on the data on the curvature of the textile path in each path planning scheme, conduct an impact analysis on the path curvature change in each path planning scheme.
[0010] S3. Analyze the damage of the chip sensing yarn based on the data of the movement of the chip sensing yarn and the movement of the spinning needle in each path planning scheme.
[0011] S4. Based on the analysis results of the influence of path curvature change in each path planning scheme and the analysis results of chip sensing yarn damage, conduct an optimization index analysis of each path planning scheme.
[0012] S5. Based on the results of the optimization index analysis of each path planning scheme, select the optimal assembly path for the chip sensing yarn.
[0013] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S1 are as follows:
[0014] S11. Obtain textile path curvature data through path planning scheme and chip sensing yarn manufacturing manual. The textile path curvature data includes textile path curvature data, length data and maximum allowable curvature data of chip sensing yarn.
[0015] S12. Obtain motion data of chip sensing yarn through high-speed camera, laser rangefinder and chip sensing yarn manufacturing manual. The motion data of chip sensing yarn includes relative angular velocity data of chip sensing yarn, original length data of chip sensing yarn, instantaneous tensile length data of chip sensing yarn during weaving and critical strain data of chip sensing yarn.
[0016] S13. Obtain data on the motion of textile needles through inertial measurement units and experimental statistics, including instantaneous yaw angle data, critical angle data, and curvature data of the textile needle trajectory;
[0017] S14. Store the acquired data in the storage component for use in the analysis process.
[0018] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, S2 includes the following specific steps: Based on the curvature data, length data, and maximum allowable curvature data of the chip sensing yarn in each path planning scheme, an impact analysis of the path curvature change in each path planning scheme is performed. The process of analyzing the impact of path curvature change in each path planning scheme is as follows: The difference between the maximum curvature value on the textile path and the maximum allowable curvature data of the chip sensing yarn in each path planning scheme is compared and then processed into absolute values, recorded as the difference comparison result for each path planning scheme. The difference comparison result for each path planning scheme is used to determine the excess curvature value of the textile path in each path planning scheme through a Heaviside step function. The excess curvature value of the textile path in each path planning scheme is divided by the maximum allowable curvature data of the chip sensing yarn to obtain the result for each path planning scheme. The scheme includes a textile path curvature influence index; integrating the absolute value of the rate of change of textile path curvature along the path length in each path planning scheme yields the overall smoothness index of the textile path in each path planning scheme; weighting and summing the textile path curvature influence index and the overall smoothness index in each path planning scheme yields the analysis results of the influence of path curvature change in each path planning scheme; specifically, the Heaviside step function is used to determine the excess value of textile path curvature in each path planning scheme as follows: when the maximum curvature on the textile path in each path planning scheme is greater than the maximum allowable curvature data of the chip sensing yarn, the excess value of textile path curvature in each path planning scheme is the result of the difference comparison between each path planning scheme; when the maximum curvature on the textile path in each path planning scheme is less than or equal to the maximum allowable curvature data of the chip sensing yarn, the excess value of textile path curvature in each path planning scheme is zero.
[0019] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S3 are as follows:
[0020] S31. The tensile damage index analysis results of the chip sensing yarn in each path planning scheme are obtained from the instantaneous tensile length data, original length data and critical strain data of the chip sensing yarn during the weaving process in each path planning scheme.
[0021] S32. The analysis results of the chip sensing yarn entanglement risk index in each path planning scheme are obtained from the instantaneous yaw angle data, critical angle data, curvature data of the textile needle motion trajectory, and relative angular velocity data of the chip sensing yarn in each path planning scheme.
[0022] S33. The analysis results of chip sensing yarn damage in each path planning scheme are obtained from the analysis results of the tensile damage index and the entanglement risk index of the chip sensing yarn in each path planning scheme.
[0023] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S31 are as follows: Based on the instantaneous tensile length data, original length data, and critical strain data of the chip sensing yarn during weaving in each path planning scheme, the tensile damage index analysis of the chip sensing yarn in each path planning scheme is performed. Specifically, the process of analyzing the tensile damage index of the chip sensing yarn in each path planning scheme is as follows: The difference between the instantaneous tensile length data and the original length data of the chip sensing yarn during weaving in each path planning scheme is compared. The difference comparison result is divided by the original length data to obtain the instantaneous tensile strain of the chip sensing yarn during weaving in each path planning scheme. The instantaneous tensile strain of the chip sensing yarn is compared with the critical strain to obtain the instantaneous tensile damage rate of the chip sensing yarn. The instantaneous tensile damage rate of the chip sensing yarn is integrated over the weaving time and then divided by the weaving time to obtain the tensile damage index analysis results of the chip sensing yarn in each path planning scheme. The specific process of comparing the instantaneous tensile strain of the chip sensing yarn with the critical strain is as follows: when the instantaneous tensile strain of the chip sensing yarn is greater than the critical strain, the instantaneous tensile damage rate of the chip sensing yarn is the instantaneous tensile strain of the chip sensing yarn minus the critical strain and divided by the critical strain; when the instantaneous tensile strain of the chip sensing yarn is less than or equal to the critical strain, the instantaneous tensile damage rate of the chip sensing yarn is zero.
[0024] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S32 are as follows: Based on the instantaneous yaw angle data, critical angle data, curvature data of the textile needle trajectory, and relative angular velocity data of the chip sensing yarn in each path planning scheme, an analysis of the chip sensing yarn entanglement risk index is performed. Specifically, the analysis process for the chip sensing yarn entanglement risk index in each path planning scheme is as follows: During the textile time, for the time period when the instantaneous yaw angle data of the textile needle exceeds the critical angle data in each path planning scheme, the product of the curvature of the textile needle trajectory and the relative angular velocity of the chip sensing yarn is integrated, and the integration result is divided by the textile time to obtain the chip sensing yarn entanglement risk index analysis result for each path planning scheme.
[0025] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S33 are as follows: obtaining the analysis results of the tensile damage index of the chip sensing yarn and the entanglement risk index in each path planning scheme, and weighting and adding the analysis results of the tensile damage index of the chip sensing yarn and the entanglement risk index in each path planning scheme to obtain the analysis results of the damage of the chip sensing yarn in each path planning scheme.
[0026] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S4 are as follows: obtaining the analysis results of the influence of path curvature change and the analysis results of chip sensing yarn damage in each path planning scheme, multiplying the analysis results of the influence of path curvature change and the analysis results of chip sensing yarn damage in each path planning scheme to obtain the analysis results of the preferred index of each path planning scheme.
[0027] It should be noted that, as a preferred technical solution for the assembly path planning method of a flexible sensing chip for textile accessories, the specific steps of S5 are as follows: obtain the optimization index analysis results of each path planning scheme, arrange the optimization index analysis results of each path planning scheme in ascending order, and take the path planning scheme corresponding to the path planning scheme with the smallest optimization index analysis result as the optimal assembly path of the chip sensing yarn, thereby selecting the optimal assembly path for the chip sensing yarn.
[0028] An assembly path planning device for a flexible sensing chip used in textile accessories is disclosed. Based on the aforementioned assembly path planning method for a flexible sensing chip used in textile accessories, the device specifically includes an assembly path planning data acquisition module, a path curvature change impact analysis module, a chip sensing yarn damage analysis module, a path planning scheme optimization analysis module, and an optimal assembly path determination module.
[0029] The assembly path planning data acquisition module is used to acquire data on the bending of the textile path, the movement of the chip sensing yarn, and the movement of the textile needle in each path planning scheme.
[0030] The path curvature change impact analysis module is used to analyze the impact of path curvature change in each path planning scheme based on the data on the bending of the textile path in each path planning scheme.
[0031] The chip sensing yarn damage analysis module is used to analyze the chip sensing yarn damage based on the chip sensing yarn movement data and textile needle movement data during weaving in each path planning scheme.
[0032] The path planning scheme optimization analysis module is used to perform optimization index analysis of each path planning scheme based on the analysis results of the influence of path curvature change in each path planning scheme and the analysis results of chip sensing yarn damage.
[0033] The optimal assembly path determination module is used to select the optimal assembly path for the chip sensing yarn based on the optimization index analysis results of each path planning scheme.
[0034] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention acquires data on the bending of the textile path, the movement of the chip sensing yarn, and the movement of the textile needle in each path planning scheme; it performs an impact analysis on the path curvature change in each path planning scheme based on the data on the bending of the textile path; it performs an analysis on the damage of the chip sensing yarn based on the data on the movement of the chip sensing yarn and the movement of the textile needle in each path planning scheme; it performs an optimization index analysis on each path planning scheme based on the results of the analysis on the impact of path curvature change and the analysis on the damage of the chip sensing yarn; it selects the optimal assembly path for the chip sensing yarn based on the results of the optimization index analysis on each path planning scheme; by integrating data such as path geometry, yarn movement, and needle posture, it achieves a quantitative assessment of the three major damage mechanisms: bending over-limit, tensile damage, and entanglement risk, transforming process data into composite indicators such as curvature influence index, smoothness index, tensile damage index, and entanglement risk index, thereby improving the objectivity and accuracy of damage prediction; at the same time, it introduces an optimization index to balance the inherent smoothness of the path with the safety of the dynamic process, providing decision support for automatic path optimization. Attached Figure Description
[0035] Figure 1 This is a schematic diagram of the overall process of the assembly path planning method for a flexible sensor chip for textile accessories according to this application.
[0036] Figure 2 This is a schematic diagram of step S3 of the assembly path planning method for a flexible sensor chip for textile accessories according to this application.
[0037] Figure 3 This is a schematic diagram of the overall framework of an assembly path planning device for a flexible sensor chip for textile accessories according to this application.
[0038] Figure 4 This is a schematic diagram illustrating the process of obtaining the results of the chip path curvature change influence analysis in the assembly path planning method for a flexible sensing chip for textile accessories according to this application.
[0039] Figure 5 This is a schematic diagram illustrating the process of obtaining the analysis results of yarn damage in the chip sensing of the flexible sensing chip for textile accessories, which is part of the assembly path planning method of the flexible sensing chip for textile accessories described in this application.
[0040] Figure 6 This is a schematic diagram illustrating the process of obtaining the optimization index analysis results for the assembly path planning method of a flexible sensing chip for textile accessories according to this application. Detailed Implementation
[0041] To better understand this application, various aspects of this application will be described in more detail with reference to the accompanying drawings.
[0042] To address the technical problems raised in the background art, this application provides a preferred embodiment:
[0043] The specific content of this embodiment is as follows:
[0044] like Figure 1 As shown, an assembly path planning method for a flexible sensing chip used in textile accessories includes the following specific steps:
[0045] S1. Obtain data on the bending of the textile path, the movement of the yarn sensed by the chip, and the movement of the textile needle in each path planning scheme.
[0046] In this embodiment, the specific steps of S1 are as follows:
[0047] S11. Textile path curvature data includes textile path curvature data, length data, and maximum allowable curvature data of the chip sensing yarn; textile path curvature data and length data are obtained through path planning schemes; maximum allowable curvature data of the chip sensing yarn is obtained through the chip sensing yarn manufacturing manual.
[0048] S12. The chip sensing yarn motion data includes the chip sensing yarn relative angular velocity data, the chip sensing yarn original length data, the chip sensing yarn instantaneous tensile length data during weaving, and the chip sensing yarn critical strain data. The chip sensing yarn relative angular velocity data is the angular velocity of the chip sensing yarn rotating around the weaving needle. It is obtained by tracking the position of the chip sensing yarn relative to the weaving needle with a high-speed camera, calculating the azimuth angle of the chip sensing yarn relative to the weaving needle, and differentiating the azimuth angle. The chip sensing yarn original length data is the initial length of the chip sensing yarn in a tension-free and unstretched state, obtained through a laser rangefinder. The chip sensing yarn instantaneous tensile length data during weaving is obtained by tracking feature points on the chip sensing yarn with a high-speed camera and then calculating the instantaneous tensile length of the chip sensing yarn during weaving through image processing technology. The chip sensing yarn critical strain data is the maximum strain that the chip sensing yarn can withstand without permanent damage, obtained from the chip sensing yarn manufacturing manual.
[0049] S13. The data on the movement of the textile needle includes instantaneous yaw angle data, critical angle data, and curvature data of the textile needle's trajectory. The instantaneous yaw angle data is the angle at which the needle rotates in the horizontal plane around the vertical direction. It is obtained by using a sensor fusion algorithm (such as Kalman filtering) to calculate the yaw angle data of the textile needle in real time through an inertial measurement unit installed on the textile needle. The critical angle data is the minimum yaw angle threshold that triggers the risk of entanglement. It is obtained by experimental observation and recording, gradually increasing the needle yaw angle until entanglement occurs, repeating the process multiple times, taking the average value, and statistically analyzing the critical angle data. The curvature data of the textile needle's trajectory is defined as follows: the absolute value of the yaw angle change rate is divided by the linear velocity of the textile needle in the textile direction. The yaw angle change rate is obtained by numerically differentiating the yaw angle. The linear velocity of the textile needle in the textile direction is obtained by obtaining the position of the textile needle through an inertial measurement unit installed on the textile needle, and then differentiating the position of the textile needle.
[0050] S14. Store the acquired data in the storage component for use in the analysis process;
[0051] S2. Based on the data on the curvature of the textile path in each path planning scheme, conduct an impact analysis on the path curvature change in each path planning scheme.
[0052] like Figure 4As shown, in this embodiment, S2 includes the following specific steps: Based on the curvature data, length data, and maximum allowable curvature data of the chip sensing yarn in each path planning scheme, an impact analysis of the path curvature change in each path planning scheme is performed. The process of analyzing the impact of path curvature change in each path planning scheme is as follows: The difference between the maximum curvature value on the textile path and the maximum allowable curvature data of the chip sensing yarn in each path planning scheme is compared and then processed into absolute values, recorded as the difference comparison result for each path planning scheme. The difference comparison result for each path planning scheme is used to determine the excessive curvature value of the textile path in each path planning scheme through the Heaviside step function. The path planning scheme is then... The curvature exceeding the limit value of the textile path in each planning scheme is divided by the maximum allowable curvature data of the chip sensing yarn to obtain the textile path curvature influence index of each path planning scheme; the absolute value of the rate of change of the textile path curvature along the path length in each path planning scheme is integrated to obtain the overall smoothness index of the textile path in each path planning scheme; the textile path curvature influence index and the overall smoothness index of each path planning scheme are weighted and summed to obtain the path curvature change impact analysis results of each path planning scheme; specifically, the Heaviside step function is used to determine the textile path curvature exceeding the limit value in each path planning scheme: when the maximum curvature value on the textile path in each path planning scheme is greater than the chip sensing... When considering the maximum allowable curvature data for the yarn, the excess curvature value of the textile path in each path planning scheme is the result of comparing the differences between the path planning schemes. When the maximum curvature value on the textile path in each path planning scheme is less than or equal to the maximum allowable curvature data for the chip sensing yarn, the excess curvature value of the textile path in each path planning scheme is zero. It should be noted that chip sensing yarn refers to yarn that utilizes new materials and processing techniques to directly fabricate complete electronic functions such as transistors, memory, sensors, and even energy units onto a single fiber substrate. It is naturally compatible with textiles, can be invisible within the fabric, and maintains the original appearance of the textile. The manufacturing method is the thermal stretching method: combining multiple functional materials (e.g., semiconductors, insulating...) Insulators, conductors, etc. are prefabricated in a macroscopic preform and heated and stretched at high temperature into micro / nano fibers hundreds of meters long. During this stretching process, the internal structure of the material is maintained and proportionally reduced to form electrodes, semiconductor channels, etc., continuously distributed along the fiber direction. When the curvature on the path exceeds the critical value of the chip sensing yarn, the fibers inside the chip sensing yarn begin to undergo plastic deformation, which will affect the sensing accuracy. The curvature effect index uses the Heaviside step function to identify and quantify the severity of exceeding the limit. The overall smoothness index analyzes the overall smoothness of the path by integrating the rate of change of curvature, because frequent sharp turns (even if not exceeding the limit) will increase mechanical stress and assembly difficulty.Weighting method: Collect sample paths corresponding to the weaving path of the chip sensor yarn and their corresponding curvature influence index and overall smoothness index. Obtain the finished product qualification rate of the chip sensor yarn along the sample paths. Use multiple regression analysis to obtain the weighted combination of the curvature influence index and the overall smoothness index, maximizing the correlation coefficient between the weighted analysis result and the finished product qualification rate. The weights of the curvature influence index and the overall smoothness index are determined in this way.
[0053] S3. Analyze the damage of the chip sensing yarn based on the data of the movement of the chip sensing yarn and the movement of the spinning needle in each path planning scheme.
[0054] like Figure 2 As shown, in this embodiment, the specific steps of S3 are as follows:
[0055] S31. The tensile damage index analysis results of the chip sensing yarn in each path planning scheme are obtained from the instantaneous tensile length data, original length data and critical strain data of the chip sensing yarn during the weaving process in each path planning scheme.
[0056] In this embodiment, the specific steps of S31 are as follows: Based on the instantaneous tensile length data, original length data, and critical strain data of the chip sensing yarn during weaving in each path planning scheme, the tensile damage index analysis of the chip sensing yarn in each path planning scheme is performed. The analysis process involves: comparing the difference between the instantaneous tensile length data and the original length data of the chip sensing yarn during weaving in each path planning scheme; dividing the difference comparison result by the original length data to obtain the instantaneous tensile strain of the chip sensing yarn during weaving in each path planning scheme; comparing the instantaneous tensile strain of the chip sensing yarn with the critical strain to obtain the instantaneous tensile damage rate of the chip sensing yarn; and integrating the instantaneous tensile damage rate of the chip sensing yarn over the weaving time and dividing it by the weaving time to obtain the tensile damage index of the chip sensing yarn in each path planning scheme. The numerical analysis results show that the specific process of comparing the instantaneous tensile strain of the chip sensing yarn with the critical strain is as follows: when the instantaneous tensile strain of the chip sensing yarn is greater than the critical strain, the instantaneous tensile damage rate of the chip sensing yarn is the instantaneous tensile strain minus the critical strain divided by the critical strain; when the instantaneous tensile strain of the chip sensing yarn is less than or equal to the critical strain, the instantaneous tensile damage rate of the chip sensing yarn is zero. It should be noted that the meaning of "instantaneous tensile strain minus critical strain divided by critical strain" is: the relative degree to which the strain exceeds the critical value of the chip sensing yarn. Strain is the direct mechanical quantity that causes damage to the chip sensing yarn. The tensile damage index analysis judges the damage state of the chip sensing yarn by comparing the instantaneous strain and the critical strain, and integrates the chip sensing yarn damage rate over the weaving time to reflect the cumulative damage degree throughout the entire weaving process.
[0057] S32. The analysis results of the chip sensing yarn entanglement risk index in each path planning scheme are obtained from the instantaneous yaw angle data, critical angle data, curvature data of the textile needle motion trajectory, and relative angular velocity data of the chip sensing yarn in each path planning scheme.
[0058] In this embodiment, the specific steps of S32 are as follows: Based on the instantaneous yaw angle data, critical angle data, curvature data of the textile needle trajectory, and relative angular velocity data of the chip sensing yarn in each path planning scheme, an analysis of the chip sensing yarn entanglement risk index is performed. The analysis process is as follows: During the textile time, for the time interval when the instantaneous yaw angle data of the textile needle exceeds the critical angle data in each path planning scheme, the product of the textile needle trajectory curvature and the relative angular velocity of the chip sensing yarn is integrated, and the integration result is divided by the textile time to obtain the chip sensing yarn entanglement risk index analysis result for each path planning scheme. It should be noted that by selecting time periods with excessively large yaw angles (i.e., calculating entanglement risk only when the needle yaw angle exceeds the critical angle), the entanglement risk is focused on high-risk periods. The product of the curvature of the textile needle's motion trajectory and the relative angular velocity of the chip sensing yarn is integrated: the product of trajectory curvature and relative angular velocity combines the sharp changes in the needle's motion trajectory (easily snagging the chip sensing yarn) and the rotational speed of the chip sensing yarn relative to the needle (the faster the rotation, the easier it is for the chip sensing yarn to entangle), and the integration operation accumulates the total entanglement risk. By identifying textile needle motion patterns that easily lead to entanglement of the chip sensing yarn, the accuracy of the chip sensing yarn entanglement risk index analysis results is improved.
[0059] S33. The analysis results of chip sensing yarn damage in each path planning scheme are obtained from the analysis results of the tensile damage index and the entanglement risk index of the chip sensing yarn in each path planning scheme.
[0060] like Figure 5As shown, in this embodiment, the specific steps of S33 are as follows: Obtain the tensile damage index analysis results and entanglement risk index analysis results of the chip sensing yarn in each path planning scheme; weight the tensile damage index analysis results and entanglement risk index analysis results of the chip sensing yarn in each path planning scheme and then sum them to obtain the chip sensing yarn damage analysis results in each path planning scheme; it should be noted that the weighted summation of the tensile damage index and the entanglement risk index yields a comprehensive damage situation, providing a comprehensive view of chip sensing yarn damage and unifying the damage analysis standards for different damage situations of chip sensing yarn; weight setting method: based on historical production data of chip sensing yarn, statistics are performed to obtain the frequency of failures and the severity of damage consequences caused by tensile damage and entanglement risk, respectively. For example, the statistical results show that serious scrap events caused by tensile damage account for 70% of the total process risk, while downtime adjustment events caused by entanglement risk account for 30%. The weight allocation for tensile damage and entanglement risk can be: tensile damage weight is 0.7, and entanglement risk weight is 0.3;
[0061] S4. Based on the analysis results of the influence of path curvature change in each path planning scheme and the analysis results of chip sensing yarn damage, conduct an optimization index analysis of each path planning scheme.
[0062] like Figure 6 As shown, in this embodiment, the specific steps of S4 are as follows: Obtain the analysis results of the impact of path curvature changes and the analysis results of chip sensing yarn damage in each path planning scheme; multiply the analysis results of the impact of path curvature changes and the analysis results of chip sensing yarn damage in each path planning scheme to obtain the optimization index analysis results of each path planning scheme. It should be noted that multiplying the static geometric influence of the textile path with the dynamic damage risk of the textile process is because, in the process of weaving chip sensing yarn, it is necessary to avoid the limitations of unilateral optimization and ensure that the selected path scheme achieves the best balance between geometric feasibility and process safety. The optimization index analysis results intuitively rank all candidate paths, laying the foundation for subsequent optimal path selection.
[0063] S5. Based on the results of the optimization index analysis of each path planning scheme, select the optimal assembly path for the chip sensing yarn.
[0064] In this embodiment, the specific steps of S5 are as follows: obtain the optimization index analysis results of each path planning scheme, sort the optimization index analysis results of each path planning scheme in ascending order, and take the path planning scheme corresponding to the path planning scheme with the smallest optimization index analysis result as the optimal assembly path of the chip sensing yarn. In this way, the optimal assembly path is selected for the chip sensing yarn. It should be noted that sorting the optimization index analysis results in ascending order and selecting the scheme corresponding to the smallest optimization index analysis result means selecting the path with the lowest comprehensive risk and the best overall performance (the lower the optimization index analysis result, the better the comprehensive performance of the path, that is, the smaller the curvature influence and the lower the damage risk).
[0065] Based on the above implementation details, this embodiment has the following advantages over the prior art: This embodiment acquires data on the bending of the textile path, the movement of the chip sensing yarn, and the movement of the textile needle in each path planning scheme; it performs an impact analysis on the path curvature change in each path planning scheme based on the textile path bending data; it performs an analysis on the chip sensing yarn damage based on the chip sensing yarn movement data and textile needle movement data during weaving in each path planning scheme; and it analyzes the impact of path curvature change and chip sensing yarn damage based on the results of the path curvature change impact analysis in each path planning scheme. The analysis results are used to perform optimization index analysis on each path planning scheme. Based on the optimization index analysis results of each path planning scheme, the optimal assembly path is selected for the chip sensing yarn. By integrating data such as path geometry, yarn motion and needle posture, a quantitative assessment of the three major damage mechanisms of bending over-limit, tensile damage and entanglement risk is achieved. The process data is transformed into composite indicators such as curvature influence index, smoothness index, tensile damage index and entanglement risk index, which improves the objectivity and accuracy of damage prediction. At the same time, the optimization index is introduced to balance the inherent smoothness of the path and the safety of the dynamic process, providing decision support for automatic path optimization.
[0066] like Figure 3As shown, this embodiment also provides an assembly path planning device for a flexible sensing chip used in textile accessories. It is implemented based on the aforementioned assembly path planning method for a flexible sensing chip used in textile accessories. Specifically, it includes an assembly path planning data acquisition module, a path curvature change impact analysis module, a chip sensing yarn damage analysis module, a path planning scheme optimization analysis module, and an optimal assembly path judgment module. The assembly path planning data acquisition module is used to acquire data on the bending of the textile path, the movement of the chip sensing yarn, and the movement of the textile needle in each path planning scheme. The path curvature change impact analysis module is used to analyze the impact of each path planning scheme... The solution includes: analyzing the impact of path curvature changes on the data of textile path bending; analyzing chip sensing yarn damage based on the data of chip sensing yarn movement and textile needle movement during weaving in each path planning scheme; performing a path planning scheme optimization analysis based on the results of the path curvature change impact analysis and chip sensing yarn damage analysis; and selecting the optimal assembly path for the chip sensing yarn based on the results of the optimization index analysis for each path planning scheme.
[0067] The specific steps for each unit module in the assembly path planning device for a flexible sensor chip for textile accessories described above to achieve its corresponding function can be found in the steps of the embodiment of the assembly path planning method for a flexible sensor chip for textile accessories described above, and will not be repeated here.
[0068] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.
Claims
1. A method for assembling a flexible sensing chip for textile accessories, characterized in that, include: S1. Obtain data on the bending of the textile path, the movement of the yarn sensed by the chip, and the movement of the textile needle in each path planning scheme. S2. Based on the data on the curvature of the textile path in each path planning scheme, conduct an impact analysis on the changes in path curvature in each path planning scheme. This includes: analyzing the impact of changes in path curvature in each path planning scheme based on the curvature data, length data, and maximum allowable curvature data of the chip sensing yarn in each path planning scheme. The analysis process is as follows: compare the difference between the maximum curvature value on the textile path and the maximum allowable curvature data of the chip sensing yarn in each path planning scheme, and then process the absolute value, recording it as the difference comparison result of each path planning scheme. The difference comparison result of each path planning scheme is then processed through He... The aviside step function is used to determine the value of the textile path curvature exceeding the limit in each path planning scheme. The value of the textile path curvature exceeding the limit in each path planning scheme is divided by the maximum allowable curvature data of the chip sensing yarn to obtain the textile path curvature influence index in each path planning scheme. The absolute value of the rate of change of the textile path curvature along the path length in each path planning scheme is integrated to obtain the overall smoothness index of the textile path in each path planning scheme. The textile path curvature influence index and the overall smoothness index of each path planning scheme are weighted and summed to obtain the path curvature change influence analysis result of each path planning scheme. S3. Based on the data of the movement of the chip-sensing yarn and the movement of the textile needles during weaving in each path planning scheme, analyze the damage of the chip-sensing yarn. The specific steps are as follows: S31. Compare the difference between the instantaneous tensile length data of the chip sensing yarn during weaving and the original length data in each path planning scheme. Divide the difference comparison result by the original length data to obtain the instantaneous tensile strain of the chip sensing yarn during weaving in each path planning scheme. Compare the instantaneous tensile strain of the chip sensing yarn with the critical strain to obtain the instantaneous tensile damage rate of the chip sensing yarn. Integrate the instantaneous tensile damage rate of the chip sensing yarn over the weaving time and divide it by the weaving time to obtain the tensile damage index analysis result of the chip sensing yarn in each path planning scheme. S32. During the weaving time, for the time period when the instantaneous yaw angle data of the needle exceeds the critical angle data in each path planning scheme, calculate the product of the curvature of the weaving needle trajectory and the relative angular velocity of the chip sensing yarn, integrate it, divide the integration result by the weaving time, and obtain the chip sensing yarn entanglement risk index analysis result in each path planning scheme. S33. The analysis results of chip sensing yarn damage in each path planning scheme are obtained from the analysis results of the tensile damage index and the entanglement risk index of the chip sensing yarn in each path planning scheme. S4. Multiply the results of the path curvature change impact analysis and the chip sensing yarn damage analysis in each path planning scheme to obtain the optimization index analysis results of each path planning scheme. S5. Based on the results of the optimization index analysis of each path planning scheme, select the optimal assembly path for the chip sensing yarn.
2. The assembly path planning method for a flexible sensing chip for textile accessories as described in claim 1, characterized in that, The specific steps of S33 are as follows: obtain the analysis results of the tensile damage index of the chip sensing yarn and the entanglement risk index in each path planning scheme, and add the weighted analysis results of the tensile damage index of the chip sensing yarn and the entanglement risk index in each path planning scheme to obtain the analysis results of the chip sensing yarn damage in each path planning scheme.
3. The assembly path planning method for a flexible sensing chip for textile accessories as described in claim 2, characterized in that, The specific steps of S5 are as follows: obtain the optimization index analysis results of each path planning scheme, sort the optimization index analysis results of each path planning scheme in ascending order, and take the path planning scheme corresponding to the smallest optimization index analysis result as the optimal assembly path of the chip sensing yarn, and select the optimal assembly path for the chip sensing yarn in this way.
4. An assembly path planning device for a flexible sensing chip for textile accessories, implemented based on the assembly path planning method for a flexible sensing chip for textile accessories according to any one of claims 1-3, characterized in that, Specifically, it includes an assembly path planning data acquisition module, a path curvature change impact analysis module, a chip sensing yarn damage analysis module, a path planning scheme optimization analysis module, and an optimal assembly path determination module. The assembly path planning data acquisition module is used to acquire data on the bending of the textile path, the movement of the chip sensing yarn, and the movement of the textile needle in each path planning scheme. The path curvature change impact analysis module is used to analyze the impact of path curvature change in each path planning scheme based on the data on the bending of the textile path in each path planning scheme. The chip sensing yarn damage analysis module is used to analyze the chip sensing yarn damage based on the chip sensing yarn movement data and textile needle movement data during weaving in each path planning scheme. The path planning scheme optimization analysis module is used to perform optimization index analysis of each path planning scheme based on the analysis results of the influence of path curvature change in each path planning scheme and the analysis results of chip sensing yarn damage. The optimal assembly path determination module is used to select the optimal assembly path for the chip sensing yarn based on the optimization index analysis results of each path planning scheme.