Wire harness production online monitoring system based on artificial intelligence

Abstract

The invention discloses a wire harness production online monitoring system based on artificial intelligence, and particularly relates to the technical field of wire harness production. Comprising a wire harness production monitoring data acquisition module, a wire harness production monitoring data analysis module, a wire harness production intelligent monitoring module, a wire harness production intelligent monitoring comprehensive analysis module and a wire harness production intelligent monitoring man-machine interaction module. The wire harness production monitoring data acquisition module is used for acquiring data in a wire harness production process to obtain various parameters of wire harness production online monitoring and transmitting the various parameters to the wire harness production monitoring data analysis module; through the artificial intelligence technology, the advanced sensor technology and the data processing algorithm, the system can accurately measure and analyze various parameters, and the detection efficiency is improved, so that each link of the wire harness production line can be monitored more comprehensively, the reliability and accuracy of the detection result are improved, and the production quality of the wire harness is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of wire harness production, and in particular to an online monitoring system for wire harness production based on artificial intelligence. Background Art

[0002] With the rapid development of the automotive, electronics and other industries, the demand for wire harnesses is also increasing. Therefore, improving production efficiency and quality has become an urgent need for wire harness manufacturers. Artificial intelligence technology has powerful data processing and analysis capabilities. It can process massive production data, extract key information, and provide strong support for production monitoring, thereby realizing automated and intelligent production monitoring and improving production efficiency.

[0003] In modern industrial manufacturing, wire harnesses, as key components for electrical connection and signal transmission, cover multiple key links such as twisting, crimping, injection molding, and assembly. In the twisting link, parameters such as the tension of the wire core and the uniformity of the twist pitch are crucial to the electrical conductivity and anti-interference ability of the wire harness. Traditional monitoring methods rely on manual sampling inspections, which make it difficult to fully and timely detect minor defects in the twisting process; in the crimping link, the firmness of the crimping and the size of the contact resistance are directly related to the reliability of the wire harness connection, but existing monitoring methods are difficult to accurately measure the mechanical properties and electrical parameters of the crimping; the injection molding link plays a decisive role in the protection and insulation performance of the wire harness, but traditional monitoring is prone to missed inspections; the assembly link needs to ensure the accurate installation and good coordination of each component. Manual inspection of assembly quality is not only inefficient, but also prone to errors caused by human negligence.

[0004] With the advancement of science and technology and the increasing market demand for high-quality wire harness products, traditional monitoring methods can no longer meet the high-precision, high-efficiency and high-stability requirements of modern wire harness production. Therefore, there is an urgent need for an artificial intelligence-based online monitoring system for wire harness production that can monitor key links such as twisting, crimping, injection molding and assembly in real time and accurately, detect problems in the production process in a timely manner, improve the quality and efficiency of wire harness production, reduce production costs, and enhance the competitiveness of enterprises in the market. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an online monitoring system for wire harness production based on artificial intelligence to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an online monitoring system for wire harness production based on artificial intelligence, comprising: Wire harness production monitoring data acquisition module: used to collect data during the wire harness production process, obtain various parameters for online monitoring of wire harness production, and transmit various parameters to the wire harness production monitoring data analysis module; Wire harness production monitoring data analysis module: used to analyze the various parameters obtained by the data acquisition module, obtain the various monitoring indicators of the online monitoring of the wire harness production, and transmit the various monitoring indicators to the wire harness production intelligent monitoring module. The wire harness production monitoring data analysis module includes a stranding quality monitoring unit, a crimping quality monitoring unit, an injection molding quality monitoring unit, and an assembly quality monitoring unit; Wire harness production intelligent monitoring module: used to compare the various monitoring indicators obtained by the data analysis module with the threshold value, transmit the good comparison results to the wire harness production intelligent monitoring comprehensive analysis module, and send warning information to the wire harness production intelligent monitoring human-computer interaction module according to the abnormal comparison results; Wire harness production intelligent monitoring comprehensive analysis module: used to comprehensively analyze the good comparison results obtained by the intelligent monitoring module, obtain the comprehensive indicators of online monitoring of wire harness production, and transmit them to the wire harness production intelligent monitoring human-computer interaction module; Wire harness production intelligent monitoring human-computer interaction module: used to receive the early warning information of the intelligent monitoring module and the comprehensive indicators of wire harness production online monitoring of the comprehensive analysis module for human-computer interaction.

[0007] Preferably, the parameters in the wire harness production monitoring data acquisition module include administrator identity authentication information, stranded wire quality monitoring parameters, crimping quality monitoring parameters, injection molding quality monitoring parameters and assembly quality monitoring parameters, wherein the stranded wire quality monitoring parameters include the length, pitch, tension and diameter of the stranded wire; the crimping quality monitoring parameters include the crimping pressure peak, the crimping terminal height, the terminal pull-out force after crimping, the conductor resistance before crimping and the conductor resistance after crimping; the injection molding quality monitoring parameters include the actual pressure of the injection molding cycle, the size of the injection molded part, the weight of the injection molded part, the number of surface defects of the injection molded part and the number of injection molded part measurement samples; the assembly quality monitoring parameters include the assembly step data set, the actual execution order of each assembly step, the number of steps that meet the requirements of the actual order, the total number of assembly steps, the number of terminals whose insertion depth meets the requirements, the total number of terminals, the actual assembly time of each step and the corresponding standard assembly time.

[0008] Preferably, the strand quality monitoring unit in the harness production monitoring data analysis module: firstly monitors the length L of the strand in real time through a high-precision length sensor act and with the design length L des By comparison, the strand length deviation index LDR is obtained, LDR=|L act -L des | / L des; Then, the pitch of the stranded wire is monitored in real time by the pitch sensor, n pitch values ​​are recorded, the average value μ(p) and standard deviation σ(p) of the pitch are obtained, and the pitch difference index PCI is obtained, PCI=σ(p) / μ(p); Secondly, the tension of the stranded wire during processing is monitored in real time by the tension sensor, m tension values ​​are recorded, the average value μ(f) and standard deviation σ(p) of the tension are obtained, and the tension fluctuation index TFR is obtained, TFR=σ(p) / μ(f); Then, the diameter values ​​of the stranded wire at k time points are recorded by the laser rangefinder, and the diameter change rate DCR of the stranded wire is obtained, , j∈k, j is at least 1 and j≤k, R j and R j+1 Represent the diameter values ​​at the jth and j+1th time points, t j and t j+1 They represent the time values ​​of the jth and j+1th time points respectively, and max() represents the maximum value. Finally, the strand quality monitoring index SQMI is obtained through big data analysis technology. , DCR0 represents the maximum allowable diameter change rate.

[0009] Preferably, the crimping quality monitoring unit in the harness production monitoring data analysis module: firstly monitors the pressure during the crimping process in real time through a pressure sensor, and records the peak pressure P of each crimping. peak , get the crimping pressure deviation index PDR, PDR=|P peak -P peak,0 | / P peak , P peak,0 Indicates the set crimping pressure peak value; then the height of the wire harness terminal after each crimping is measured by measuring equipment, N height values ​​are recorded, the average value μ(h) and standard deviation σ(h) of the height of the wire harness crimped terminal are obtained, and the height difference index HCI of the wire harness crimped terminal is obtained, HCI=σ(h) / μ(h); secondly, the pull-out force test is performed on the terminal after each crimping, N test results are recorded, the average value μ(F) and standard deviation σ(F) of the terminal pull-out force test are obtained, and the terminal pull-out force difference index SCI is obtained, SCI=σ(F) / μ(F); finally, the crimping quality monitoring index CQMI is obtained through big data analysis technology, , F hou and F pre They represent the conductor resistance after crimping and the conductor resistance before crimping respectively.

[0010] Preferably, the injection molding quality monitoring unit in the wiring harness production monitoring data analysis module: firstly, the pressure data is recorded in real time by the pressure sensor on the injection molding machine, the actual pressure of L injection molding cycles is obtained, and the injection molding pressure fluctuation index PFR is obtained, , l∈L, IP act,lIndicates the actual pressure of the first injection cycle, IP set Indicates the set pressure; then, through the measuring tool, according to the size production process parameters of the injection molded part, the size measurement value D of the injection molded part is obtained. mea , get the injection molded part dimensional deviation index DDC, , i∈M,D mea，i represents the dimensional measurement value of the i-th injection molded part, D des represents the design size, M is the number of injection molded parts measurement samples; secondly, the injection molded parts are weighed by a weight sensor, and the weight of each injection molded part is recorded to obtain the injection molded part weight difference index WCI. , W act，i represents the actual weight of the i-th injection molded part, W des Indicates the design weight; again, the number of injection molded parts with surface defects n_d is identified through visual recognition technology, and the surface defect index SDR of injection molded parts is obtained, SDR=n_d / M; finally, the injection molding quality monitoring index IQMI is obtained through big data analysis technology, , add 0.01 to avoid the denominator being 0.

[0011] Preferably, the assembly quality monitoring unit in the wire harness production monitoring data analysis module: firstly obtains an assembly step data set ASD according to the wire harness production assembly design parameters, , K represents the number of steps required for the assembly process, AS J represents the Jth assembly step, a sensor is set in each assembly step, the actual execution order is recorded, and compared with the design order, the assembly order qualification index CSR is obtained, CSR=n_as / Tn_as, n_as represents the number of steps that meet the requirements of the actual order, Tn_as represents the total number of assembly steps; then the wiring harness terminal insertion depth is measured by the depth sensor, and compared with the design parameters, the wiring harness terminal insertion depth qualification index TDR is obtained, TDR=n_t / Tn_t, n_t represents the number of terminals whose insertion depth meets the requirements, Tn_t represents the total number of terminals; at the same time, a timer is set in each assembly step to record the actual assembly time T of each step act,J , and the corresponding standard assembly time T set,J By comparison, we can get the assembly time fluctuation index TTR. Finally, the assembly quality monitoring index AQMI is obtained through big data analysis technology. .

[0012] Preferably, the intelligent monitoring module in the wiring harness production intelligent monitoring module comprises the following steps: Step 1: Compare the strand quality monitoring index SQMI with the threshold SQMI0 to obtain the early warning factor ζ(SQMI) for strand quality monitoring, ζ(SQMI)=exp(SQMI-SQMI0). If ζ(SQMI)≥1, it means that the strand quality monitoring is good; otherwise, it means that the strand quality monitoring is abnormal, and a strand quality early warning message is issued; Step 2: Compare the crimping quality monitoring index CQMI with the threshold CQMI0 to obtain the early warning factor ζ(CQMI) for crimping quality monitoring, ζ(CQMI)=exp(CQMI-CQMI0). If ζ(CQMI)≥1, it means that the crimping quality monitoring is good; otherwise, it means that the crimping quality monitoring is abnormal, and a crimping quality early warning information is issued; Step 3: Compare the injection molding quality monitoring index IQMI with the threshold value IQMI0 to obtain the early warning factor ζ(IQMI) for injection molding quality monitoring, ζ(IQMI)=exp(IQMI-IQMI0). If ζ(IQMI)≥1, it means that the injection molding quality monitoring is good; otherwise, it means that the injection molding quality monitoring is abnormal, and an injection molding quality early warning information is issued; Step 4: Compare the assembly quality monitoring index AQMI with the threshold AQMI0 to obtain the early warning factor ζ(AQMI) for assembly quality monitoring, ζ(AQMI)=exp(AQMI-AQMI0). If ζ(AQMI)≥1, it means that the assembly quality monitoring is good; otherwise, it means that the assembly quality monitoring is abnormal and an assembly quality early warning message is issued.

[0013] Preferably, the comprehensive analysis model in the comprehensive analysis module for intelligent monitoring of wire harness production is: WPCI=w1×ζ(SQMI)+w2×ζ(CQMI)+w3×ζ(IQMI)+w4×ζ(AQMI), WPCI represents the comprehensive index of online monitoring of wire harness production, w1 represents the weight of the early warning factor ζ(SQMI) for stranded wire quality monitoring, w2 represents the weight of the early warning factor ζ(CQMI) for crimping quality monitoring, w3 represents the weight of the early warning factor ζ(IQMI) for injection molding quality monitoring, and w4 represents the weight of the early warning factor ζ(AQMI) for assembly quality monitoring.

[0014] Technical effects and advantages of the present invention: 1. The present invention uses artificial intelligence technology, advanced sensor technology and data processing algorithms. The system can accurately measure and analyze various parameters, can more comprehensively monitor all aspects of the wire harness production line, improve the reliability and accuracy of the test results, thereby improving the test efficiency and helping to ensure the production quality of the wire harness; 2. The present invention uses artificial intelligence technology to optimize and adjust the production process according to real-time monitoring data. For example, by adjusting parameters such as stranding tension, crimping pressure and injection temperature, production accidents and product quality degradation can be avoided, thereby ensuring product quality, reducing manual intervention and reducing labor costs; 3. The present invention uses artificial intelligence technology, and the online monitoring system can realize automatic detection. When quality problems are detected, the system will automatically send out an alarm signal to promptly discover and handle abnormal conditions in the production process, avoid production stagnation and waste, and prompt operators to take timely measures to correct them, thereby improving the accuracy and efficiency of detection and reducing the errors and missed detection rates of manual detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention provides an artificial intelligence-based online monitoring system for wire harness production, including a wire harness production monitoring data acquisition module, a wire harness production monitoring data analysis module, a wire harness production intelligent monitoring module, a wire harness production intelligent monitoring comprehensive analysis module and a wire harness production intelligent monitoring human-computer interaction module.

[0018] What needs to be specifically explained in this embodiment is that the production process of the wire harness studied by the present invention includes twisting, crimping, injection molding and assembly. The production process of the wire harness requires multiple crimping processes and multiple injection molding processes. The twisting process is to twist multiple single wires together according to certain rules and directions to form a twisted wire or a bundle of wires; the crimping process is to press the terminal or connector with the core wire in the wire harness through a crimping mold to form a firm mechanical and electrical connection; the injection molding process is to inject thermoplastic plastic into a pre-designed mold cavity, and after cooling and curing, a plastic product that meets the required shape is obtained. In the production of wire harnesses, the injection molding process is usually used to produce key components such as connectors, plugs, and sockets, as well as to achieve a close combination of cables and connectors or other components; the assembly process is to assemble components such as twisted wires, crimping parts, injection molding parts, etc. according to design requirements to form a complete wire harness product.

[0019] The wire harness production monitoring data acquisition module is connected to the wire harness production monitoring data analysis module, the wire harness production intelligent monitoring module is respectively connected to the wire harness production monitoring data analysis module and the wire harness production intelligent monitoring comprehensive analysis module, and the wire harness production intelligent monitoring human-computer interaction module is respectively connected to the wire harness production intelligent monitoring module and the wire harness production intelligent monitoring comprehensive analysis module.

[0020] Wire harness production monitoring data acquisition module: used to collect data during the wire harness production process, obtain various parameters for online monitoring of wire harness production, and transmit various parameters to the wire harness production monitoring data analysis module; In this embodiment, it is necessary to specifically explain that the various parameters include administrator identity authentication information, stranded wire quality monitoring parameters, crimping quality monitoring parameters, injection molding quality monitoring parameters and assembly quality monitoring parameters, wherein the stranded wire quality monitoring parameters include the length, pitch, tension and diameter of the stranded wire; the crimping quality monitoring parameters include the crimping pressure peak, the crimping terminal height, the terminal pull-out force after crimping, the conductor resistance before crimping and the conductor resistance after crimping; the injection molding quality monitoring parameters include the actual pressure of the injection molding cycle, the size of the injection molded part, the weight of the injection molded part, the number of surface defects of the injection molded part and the number of injection molded part measurement samples; the assembly quality monitoring parameters include the assembly step data set, the actual execution order of each assembly step, the number of steps whose actual order meets the requirements, the total number of assembly steps, the number of terminals whose insertion depth meets the requirements, the total number of terminals, the actual assembly time of each step and the corresponding standard assembly time.

[0021] Wire harness production monitoring data analysis module: used to analyze the various parameters obtained by the data acquisition module, obtain the various monitoring indicators of the online monitoring of the wire harness production, and transmit the various monitoring indicators to the wire harness production intelligent monitoring module. The wire harness production monitoring data analysis module includes a stranding quality monitoring unit, a crimping quality monitoring unit, an injection molding quality monitoring unit, and an assembly quality monitoring unit. Obtaining various monitoring indicators includes the following steps: What needs to be specifically explained in this embodiment is that the stranded wire quality monitoring unit is used to analyze the stranded wire quality monitoring parameters, the crimping quality monitoring unit is used to analyze the crimping quality monitoring parameters, the injection molding quality monitoring unit is used to analyze the injection molding quality monitoring parameters, and the assembly quality monitoring unit is used to analyze the assembly quality monitoring parameters.

[0022] Step 1: Stranding wire quality monitoring unit: First, the length L of the stranded wire is monitored in real time by a high-precision length sensor act and with the design length L des By comparison, the strand length deviation index LDR is obtained, LDR=|L act -L des | / L des; Then, the pitch of the stranded wire is monitored in real time by the pitch sensor, n pitch values ​​are recorded, the average value μ(p) and standard deviation σ(p) of the pitch are obtained, and the pitch difference index PCI is obtained, PCI=σ(p) / μ(p); Secondly, the tension of the stranded wire during processing is monitored in real time by the tension sensor, m tension values ​​are recorded, the average value μ(f) and standard deviation σ(p) of the tension are obtained, and the tension fluctuation index TFR is obtained, TFR=σ(p) / μ(f); Then, the diameter values ​​of the stranded wire at k time points are recorded by the laser rangefinder, and the diameter change rate DCR of the stranded wire is obtained, , j∈k, j is at least 1 and j≤k, R j and R j+1 Represent the diameter values ​​at the jth and j+1th time points, t j and t j+1 They represent the time values ​​of the jth and j+1th time points respectively, and max() represents the maximum value. Finally, the strand quality monitoring index SQMI is obtained through big data analysis technology. , DCR0 represents the maximum allowed diameter change rate; What needs to be specifically explained in this embodiment is that with the help of advanced artificial intelligence algorithms, the system can perform high-precision analysis on the collected data. Based on the wire quality monitoring indicators, the artificial intelligence system can automatically adjust the parameters of the production equipment, such as tension control, pitch adjustment, etc., thereby optimizing the production process and improving production efficiency.

[0023] Step 2: Crimping quality monitoring unit: First, the pressure during the crimping process is monitored in real time through the pressure sensor, and the peak pressure P of each crimping is recorded. peak , get the crimping pressure deviation index PDR, PDR=|P peak -P peak,0 | / P peak , P peak,0 Indicates the set crimping pressure peak value; then the height of the wire harness terminal after each crimping is measured by measuring equipment, N height values ​​are recorded, the average value μ(h) and standard deviation σ(h) of the height of the wire harness crimped terminal are obtained, and the height difference index HCI of the wire harness crimped terminal is obtained, HCI=σ(h) / μ(h); secondly, the pull-out force test is performed on the terminal after each crimping, N test results are recorded, the average value μ(F) and standard deviation σ(F) of the terminal pull-out force test are obtained, and the terminal pull-out force difference index SCI is obtained, SCI=σ(F) / μ(F); finally, the crimping quality monitoring index CQMI is obtained through big data analysis technology, , F hou and F pre They represent the conductor resistance after crimping and the conductor resistance before crimping respectively; What needs to be specifically explained in this embodiment is that by real-time monitoring of key parameters such as the pressure peak deviation rate and the height difference index during the crimping process, subtle changes in the crimping quality such as insufficient pressure and inconsistent height can be accurately captured, thereby avoiding the production of defective products; by monitoring the resistance value before and after crimping, the resistance change rate is calculated. If the resistance change rate is too large, it may mean that poor crimping directly affects the conductivity and stability of the electrical connection.

[0024] Step 3: Injection quality monitoring unit: First, the pressure data is recorded in real time through the pressure sensor on the injection molding machine, the actual pressure of L injection cycles is obtained, and the injection pressure fluctuation index PFR is obtained. , l∈L, IP act,l Indicates the actual pressure of the first injection cycle, IP set Indicates the set pressure; then, through measuring tools (such as vernier calipers, three-coordinate measuring machines, etc.), according to the size production process parameters of the injection molded part, the size measurement value D of the injection molded part is obtained. mea , get the injection molded part dimensional deviation index DDC, , i∈M,D mea，i represents the dimensional measurement value of the i-th injection molded part, D des represents the design size, M is the number of injection molded parts measurement samples; secondly, the injection molded parts are weighed by a weight sensor, and the weight of each injection molded part is recorded to obtain the injection molded part weight difference index WCI. , W act，i represents the actual weight of the i-th injection molded part, W des Indicates the design weight; again, through visual recognition technology, the number of injection molded parts with surface defects n_d is identified, and the surface defects of injection molded parts such as bubbles, cracks, shrinkage, etc. are obtained to obtain the surface defect index SDR of injection molded parts, SDR=n_d / M; finally, the injection molding quality monitoring index IQMI is obtained through big data analysis technology, , add 0.01 to avoid the denominator being 0; What needs to be specifically explained in this embodiment is that artificial intelligence technology can be used to accurately analyze pressure fluctuation data to predict potential failures of the injection molding machine; high-precision sensors and algorithms can be used to monitor dimensional deviations of injection molded parts in real time to ensure that the products meet design requirements; by analyzing weight difference data, production process parameters can be optimized to improve the overall quality of the product; the AI ​​visual inspection system can automatically identify defects on the surface of injection molded parts, such as scratches, pits, protrusions, foreign matter, etc., to ensure product quality consistency.

[0025] Step 4: Assembly quality monitoring unit: First, obtain the assembly step data set ASD according to the wiring harness production assembly design parameters. , K represents the number of steps required for the assembly process, AS Jrepresents the Jth assembly step, a sensor is set in each assembly step, the actual execution order is recorded, and compared with the design order, the assembly order qualification index CSR is obtained, CSR=n_as / Tn_as, n_as represents the number of steps that meet the requirements of the actual order, Tn_as represents the total number of assembly steps; then the wiring harness terminal insertion depth is measured by the depth sensor, and compared with the design parameters, the wiring harness terminal insertion depth qualification index TDR is obtained, TDR=n_t / Tn_t, n_t represents the number of terminals whose insertion depth meets the requirements, Tn_t represents the total number of terminals; at the same time, a timer is set in each assembly step to record the actual assembly time T of each step act,J , and the corresponding standard assembly time T set,J By comparison, we can get the assembly time fluctuation index TTR. Finally, the assembly quality monitoring index AQMI is obtained through big data analysis technology. ; What needs to be specifically explained in this embodiment is that the correct assembly sequence is crucial to ensuring the performance and safety of the wiring harness. If the assembly sequence is wrong, it may cause internal short circuit, open circuit or other faults in the wiring harness; if the terminal insertion depth is insufficient, it may cause poor electrical connection, thereby affecting the performance of the entire wiring harness; if the assembly time fluctuates greatly, it may mean that there are unstable factors in the assembly process, such as equipment failure, improper operation of employees, etc.

[0026] Wire harness production intelligent monitoring module: used to compare the various monitoring indicators obtained by the data analysis module with the threshold value, transmit the good comparison results to the wire harness production intelligent monitoring comprehensive analysis module, and send warning information to the wire harness production intelligent monitoring human-computer interaction module according to the abnormal comparison results. The intelligent monitoring module includes the following steps: Step 1: Compare the strand quality monitoring index SQMI with the threshold SQMI0 to obtain the early warning factor ζ(SQMI) for strand quality monitoring, ζ(SQMI)=exp(SQMI-SQMI0). If ζ(SQMI)≥1, it means that the strand quality monitoring is good; otherwise, it means that the strand quality monitoring is abnormal, and a strand quality early warning message is issued; Step 2: Compare the crimping quality monitoring index CQMI with the threshold CQMI0 to obtain the early warning factor ζ(CQMI) for crimping quality monitoring, ζ(CQMI)=exp(CQMI-CQMI0). If ζ(CQMI)≥1, it means that the crimping quality monitoring is good; otherwise, it means that the crimping quality monitoring is abnormal, and a crimping quality early warning information is issued; Step 3: Compare the injection molding quality monitoring index IQMI with the threshold value IQMI0 to obtain the early warning factor ζ(IQMI) for injection molding quality monitoring, ζ(IQMI)=exp(IQMI-IQMI0). If ζ(IQMI)≥1, it means that the injection molding quality monitoring is good; otherwise, it means that the injection molding quality monitoring is abnormal, and an injection molding quality early warning information is issued; Step 4: Compare the assembly quality monitoring index AQMI with the threshold value AQMI0 to obtain the early warning factor ζ(AQMI) for assembly quality monitoring, ζ(AQMI)=exp(AQMI-AQMI0). If ζ(AQMI)≥1, it means that the assembly quality monitoring is good; otherwise, it means that the assembly quality monitoring is abnormal, and an assembly quality early warning message is issued; Wire harness production intelligent monitoring comprehensive analysis module: used to comprehensively analyze the good comparison results obtained by the intelligent monitoring module, obtain the comprehensive index WPCI of wire harness production online monitoring, and transmit it to the wire harness production intelligent monitoring human-computer interaction module. The comprehensive analysis model is: WPCI=w1×ζ(SQMI)+w2×ζ(CQMI)+w3×ζ(IQMI)+w4×ζ(AQMI), w1 represents the weight of the early warning factor ζ(SQMI) of stranded wire quality monitoring, w2 represents the weight of the early warning factor ζ(CQMI) of crimping quality monitoring, w3 represents the weight of the early warning factor ζ(IQMI) of injection molding quality monitoring, and w4 represents the weight of the early warning factor ζ(AQMI) of assembly quality monitoring, for example, w1=0.25, w2=0.25, w3=0.25 and w4=0.25; Wire harness production intelligent monitoring human-computer interaction module: used to receive the early warning information of the intelligent monitoring module and the comprehensive indicators of wire harness production online monitoring of the comprehensive analysis module for human-computer interaction. According to different early warning information, the administrator is prompted to take corresponding management measures; according to the comprehensive indicators of wire harness production online monitoring, it is judged whether it is within the set allowable range. If so, it means that the wire harness production online monitoring is good. Otherwise, the administrator is prompted to take timely measures, such as tension control, pitch adjustment, etc.; optimize crimping parameters, improve mold design, etc.; find the optimal combination of process parameters through machine learning, so as to optimize the production process and improve product quality and production efficiency.

[0027] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An artificial intelligence-based online monitoring system for wire harness production, characterized by: include: Wire harness production monitoring data acquisition module: used to collect data during the wire harness production process, obtain various parameters for online monitoring of wire harness production, and transmit various parameters to the wire harness production monitoring data analysis module; Wire harness production monitoring data analysis module: used to analyze the various parameters obtained by the data acquisition module, obtain the various monitoring indicators of the online monitoring of the wire harness production, and transmit the various monitoring indicators to the wire harness production intelligent monitoring module. The wire harness production monitoring data analysis module includes a stranding quality monitoring unit, a crimping quality monitoring unit, an injection molding quality monitoring unit, and an assembly quality monitoring unit; Wire harness production intelligent monitoring module: used to compare the various monitoring indicators obtained by the data analysis module with the threshold value, transmit the good comparison results to the wire harness production intelligent monitoring comprehensive analysis module, and send warning information to the wire harness production intelligent monitoring human-computer interaction module according to the abnormal comparison results; Wire harness production intelligent monitoring comprehensive analysis module: used to comprehensively analyze the good comparison results obtained by the intelligent monitoring module, obtain the comprehensive indicators of online monitoring of wire harness production, and transmit them to the wire harness production intelligent monitoring human-computer interaction module; Wire harness production intelligent monitoring human-computer interaction module: used to receive the early warning information of the intelligent monitoring module and the comprehensive indicators of wire harness production online monitoring of the comprehensive analysis module for human-computer interaction.

2. According to claim 1, an artificial intelligence-based online monitoring system for wire harness production is characterized in that: The parameters in the wire harness production monitoring data acquisition module include administrator identity authentication information, stranded wire quality monitoring parameters, crimping quality monitoring parameters, injection molding quality monitoring parameters and assembly quality monitoring parameters, wherein the stranded wire quality monitoring parameters include the length, pitch, tension and diameter of the stranded wire; the crimping quality monitoring parameters include the crimping pressure peak, crimping terminal height, terminal pull-out force after crimping, conductor resistance before crimping and conductor resistance after crimping; the injection molding quality monitoring parameters include the actual pressure of the injection molding cycle, the size of the injection molded part, the weight of the injection molded part, the number of surface defects of the injection molded part and the number of injection molded part measurement samples; the assembly quality monitoring parameters include the assembly step data set, the actual execution order of each assembly step, the number of steps that meet the requirements of the actual order, the total number of assembly steps, the number of terminals whose insertion depth meets the requirements, the total number of terminals, the actual assembly time of each step and the corresponding standard assembly time.

3. The artificial intelligence-based online monitoring system for wire harness production according to claim 1 is characterized in that: The strand quality monitoring unit in the harness production monitoring data analysis module first monitors the length L of the strand in real time through a high-precision length sensor. act and with the design length L des By comparison, the strand length deviation index LDR is obtained, LDR=|L act -L des | / L des ; Then, the pitch of the stranded wire is monitored in real time by the pitch sensor, n pitch values ​​are recorded, the average value μ(p) and standard deviation σ(p) of the pitch are obtained, and the pitch difference index PCI is obtained, PCI=σ(p) / μ(p); Secondly, the tension of the stranded wire during processing is monitored in real time by the tension sensor, m tension values ​​are recorded, the average value μ(f) and standard deviation σ(p) of the tension are obtained, and the tension fluctuation index TFR is obtained, TFR=σ(p) / μ(f); Then, the diameter values ​​of the stranded wire at k time points are recorded by the laser rangefinder, and the diameter change rate DCR of the stranded wire is obtained, , j is at least 1 and j≤k, R j and R j+1 Represent the diameter values ​​at the jth and j+1th time points, t j and t j+1 They represent the time values ​​of the jth and j+1th time points respectively, and max() represents the maximum value. Finally, the strand quality monitoring index SQMI is obtained through big data analysis technology. , DCR0 represents the maximum allowable diameter change rate.

4. The artificial intelligence-based online monitoring system for wire harness production according to claim 1 is characterized in that: The crimping quality monitoring unit in the harness production monitoring data analysis module first monitors the pressure during the crimping process in real time through a pressure sensor, and records the peak pressure P of each crimping. peak , get the crimping pressure deviation index PDR, PDR=|P peak -P peak,0 | / P peak , P peak,0 Indicates the set crimping pressure peak value; then the height of the wire harness terminal after each crimping is measured by measuring equipment, N height values ​​are recorded, the average value μ(h) and standard deviation σ(h) of the height of the wire harness crimped terminal are obtained, and the height difference index HCI of the wire harness crimped terminal is obtained, HCI=σ(h) / μ(h); secondly, the pull-out force test is performed on the terminal after each crimping, N test results are recorded, the average value μ(F) and standard deviation σ(F) of the terminal pull-out force test are obtained, and the terminal pull-out force difference index SCI is obtained, SCI=σ(F) / μ(F); finally, the crimping quality monitoring index CQMI is obtained through big data analysis technology, , F hou and F pre They represent the conductor resistance after crimping and the conductor resistance before crimping respectively.

5. The artificial intelligence-based online monitoring system for wire harness production according to claim 1 is characterized in that: The injection molding quality monitoring unit in the wiring harness production monitoring data analysis module first records the pressure data in real time through the pressure sensor on the injection molding machine, obtains the actual pressure of L injection molding cycles, and obtains the injection molding pressure fluctuation index PFR. , l∈L, IP act,l Indicates the actual pressure of the first injection cycle, IP set Indicates the set pressure; then, through the measuring tool, according to the size production process parameters of the injection molded part, the size measurement value D of the injection molded part is obtained. mea , get the injection molded part dimensional deviation index DDC, , i∈M,D mea，i represents the dimensional measurement value of the i-th injection molded part, D des represents the design size, M is the number of injection molded parts measurement samples; secondly, the injection molded parts are weighed by a weight sensor, and the weight of each injection molded part is recorded to obtain the injection molded part weight difference index WCI. , W act，i represents the actual weight of the i-th injection molded part, W des Indicates the design weight; again, the number of injection molded parts with surface defects n_d is identified through visual recognition technology, and the surface defect index SDR of injection molded parts is obtained, SDR=n_d / M; finally, the injection molding quality monitoring index IQMI is obtained through big data analysis technology, , add 0.01 to avoid the denominator being 0.

6. The artificial intelligence-based online monitoring system for wire harness production according to claim 1 is characterized in that: The assembly quality monitoring unit in the wire harness production monitoring data analysis module first obtains the assembly step data set ASD according to the wire harness production assembly design parameters, , K represents the number of steps required for the assembly process, AS J represents the Jth assembly step, a sensor is set in each assembly step, the actual execution order is recorded, and compared with the design order, the assembly order qualification index CSR is obtained, CSR=n_as / Tn_as, n_as represents the number of steps that meet the requirements of the actual order, Tn_as represents the total number of assembly steps; then the wiring harness terminal insertion depth is measured by the depth sensor, and compared with the design parameters, the wiring harness terminal insertion depth qualification index TDR is obtained, TDR=n_t / Tn_t, n_t represents the number of terminals whose insertion depth meets the requirements, Tn_t represents the total number of terminals; at the same time, a timer is set in each assembly step to record the actual assembly time T of each step act,J , and the corresponding standard assembly time T set,J By comparison, we can get the assembly time fluctuation index TTR. Finally, the assembly quality monitoring index AQMI is obtained through big data analysis technology. .

7. The artificial intelligence-based online monitoring system for wire harness production according to claim 1 is characterized in that: The intelligent monitoring module in the wiring harness production intelligent monitoring module includes the following steps: Step 1: Compare the strand quality monitoring index SQMI with the threshold SQMI0 to obtain the early warning factor ζ(SQMI) for strand quality monitoring, ζ(SQMI)=exp(SQMI-SQMI0). If ζ(SQMI)≥1, it means that the strand quality monitoring is good; otherwise, it means that the strand quality monitoring is abnormal, and a strand quality early warning message is issued; Step 2: Compare the crimping quality monitoring index CQMI with the threshold CQMI0 to obtain the early warning factor ζ(CQMI) for crimping quality monitoring, ζ(CQMI)=exp(CQMI-CQMI0). If ζ(CQMI)≥1, it means that the crimping quality monitoring is good; otherwise, it means that the crimping quality monitoring is abnormal, and a crimping quality early warning information is issued; Step 3: Compare the injection molding quality monitoring index IQMI with the threshold value IQMI0 to obtain the early warning factor ζ(IQMI) for injection molding quality monitoring, ζ(IQMI)=exp(IQMI-IQMI0). If ζ(IQMI)≥1, it means that the injection molding quality monitoring is good; otherwise, it means that the injection molding quality monitoring is abnormal, and an injection molding quality early warning information is issued; Step 4: Compare the assembly quality monitoring index AQMI with the threshold AQMI0 to obtain the early warning factor ζ(AQMI) for assembly quality monitoring, ζ(AQMI)=exp(AQMI-AQMI0). If ζ(AQMI)≥1, it means that the assembly quality monitoring is good; otherwise, it means that the assembly quality monitoring is abnormal and an assembly quality early warning message is issued.

8. The artificial intelligence-based online monitoring system for wire harness production according to claim 1 is characterized by: The comprehensive analysis model in the wire harness production intelligent monitoring comprehensive analysis module is: WPCI=w1×ζ(SQMI)+w2×ζ(CQMI)+w3×ζ(IQMI)+w4×ζ(AQMI), WPCI represents the comprehensive index of online monitoring of wire harness production, w1 represents the weight of the early warning factor ζ(SQMI) of stranded wire quality monitoring, w2 represents the weight of the early warning factor ζ(CQMI) of crimping quality monitoring, w3 represents the weight of the early warning factor ζ(IQMI) of injection molding quality monitoring, and w4 represents the weight of the early warning factor ζ(AQMI) of assembly quality monitoring.

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