Intelligent assembly method of precision connector and related system

Abstract

The invention discloses an intelligent assembly method of a precise connector, which comprises the following steps of: adding phase change particles with different melting points into tin-silver-copper-based solder according to the difference of functional areas of welding spots in the connector, generating an area formula index, and obtaining composite solder with partition identification; according to the area formula index, the composite welding flux is deposited to preset welding spot positions according to the functional areas, and particle distribution information is recorded; performing reflow soldering on different functional areas by adopting corresponding temperature curves according to the melting point parameters of the phase change particles, and recording the temperature history of each area; applying pulse thermal excitation to the packaged connector, and collecting a surface temperature change curve of each welding spot to obtain thermal response data; and performing correlation comparison on the thermal response data and process historical data, identifying the welding spot defect type according to curve deviation characteristics, and generating process adjustment parameters. According to the scheme, after the connector is completely packaged, rapid nondestructive detection of internal tiny welding spots can be realized, hidden defects can be effectively identified, and intelligent optimization and adjustment of process parameters can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of precision connector manufacturing and non-destructive testing, and in particular to an intelligent assembly method for precision connectors and a related system. Background Art

[0002] A connector is a device that reversibly couples two or more independent components in electrical energy, signal or fluid channels. It is usually composed of tiny components such as metal terminals, insulating shells and locking mechanisms. In application environments such as high-speed communications, aerospace and high-power fast charging, connectors need to work for a long time under extreme temperatures, vibrations and current shocks. The integrity of their internal solder joints, plating and heat dissipation paths directly determines the reliability of the system. Modern assembly lines use reflow soldering, laser welding or ultrasonic welding to connect terminals to wiring harnesses, PCBs or shielding shells, and complete overmolding packaging in plastic shells, so that the solder joints are shielded by multiple layers of insulators and metal shells, and conventional detection methods cannot directly contact them.

[0003] To balance production line speed and cost control, existing processes typically only indirectly assess weld wetting, porosity, and warpage during the soldering phase through power waveform monitoring or spot X-ray inspections. Solder quality is then confirmed through tensile testing or sample dissection. However, factors such as slight drift in the reflow temperature profile, rheological differences in solder residue, and gas entrapment within critical spaces can cause 5%-10% solder voids or microcracks during the solidification phase. Once encapsulated, these defects escape the physical detection limits of X-ray resolution or ultrasonic coupling media. Due to the high-volume and fast-paced production of precision connectors, comprehensive CT or SAM inspection significantly increases process time and equipment investment. Manufacturers rely on empirically defined process windows, and cannot achieve full inspection within seconds at the end of assembly. Therefore, rapid, non-destructive, and comprehensive quality assessment of hidden defects such as voids, lack of fusion, and cracks in micro-solder joints within fully assembled connectors, once shielded by the plastic housing, is a pressing challenge for those skilled in the art. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that it is difficult to quickly, non-destructively and fully detect tiny internal solder joint defects in existing precision connectors after assembly.

[0005] A first aspect of the present invention provides an intelligent assembly method for a precision connector, the intelligent assembly method for a precision connector comprising: According to the functional area differences of the solder joints in the connector, phase change particles with different melting points are added to the tin-silver-copper based solder to generate a regional formula index and obtain a composite solder with a partition identification. Depositing the composite solder to predetermined solder joint positions according to the functional areas according to the regional formula index, recording particle distribution information, and obtaining deposition coordinate data; According to the melting point parameters of the phase change particles in the regional formula index, reflow soldering is performed on different functional areas using corresponding temperature curves, and the temperature history of each area is recorded to obtain process history data; Apply pulse thermal excitation to the packaged connector, collect the surface temperature change curve of each solder point, and obtain thermal response data; The thermal response data is correlated and compared with the process history data, the solder joint defect type is identified according to the curve deviation characteristics, and process adjustment parameters are generated for temperature curve optimization of subsequent batches.

[0006] Preferably, the process of adding phase change particles with different melting points to the tin-silver-copper based solder according to the functional regional differences of the solder joints in the connector, generating a regional formula index, and obtaining a composite solder with a partition identification includes: Based on the current density threshold and heat dissipation path length of each solder point in the connector, the solder points are evaluated for thermal risk level to obtain functional zoning data including risk coefficients. According to the risk coefficient in the functional zoning data, a phase change particle combination is selected according to the increasing melting point interval rule, and a gradient doping treatment is performed on the tin-silver-copper based solder to obtain a composite base material with a thermal response level; Optimizing the particle size ratio of phase change particles with different melting points in the composite base material to establish a spatial filling pattern of large and small particles, and associating and storing the particle ratio parameters, melting point sequence, spatial distribution characteristics, and risk coefficients with corresponding functional zones to obtain a regional formula index containing a prediction algorithm; According to the parameter configuration in the regional formula index, the composite base material is subjected to partition identification coding processing to obtain a composite solder with partition identification corresponding to the functional area one by one.

[0007] Preferably, the phase change particle combination is selected according to the risk coefficient in the functional zoning data and the melting point interval increasing rule, and the tin-silver-copper based solder is subjected to gradient doping treatment to obtain a composite base material with a thermal response level, including: According to the numerical range of the risk coefficient in the functional zoning data, the phase change particles are subjected to surface activation pretreatment, and by controlling the wetting angle and interfacial tension on the particle surface, a grading standard for the bonding strength between the particles and the tin-silver-copper matrix is ​​established to obtain activated particles containing interface bonding parameters; According to the interface bonding parameters of the activated particles, the tin-silver-copper based solder is melt-mixed in batches, the dispersion depth and aggregation state of particles with different melting points in the matrix are controlled, a three-dimensional concentration gradient distribution pattern is established, and a premix containing a concentration gradient coefficient is obtained; The particle distribution in the premix is ​​homogenized and regulated, and particle agglomeration is eliminated through the synergistic effect of ultrasonic oscillation and temperature gradient field, and a statistical distribution law of the distance between particles is established to obtain a homogenized base material including particle spacing parameters; According to the particle spacing parameters in the homogenized base material, the composite base material is subjected to a thermal response level verification process, and the thermal conduction synergistic effect of particles in different regions is measured to obtain a composite base material with a controllable thermal response level.

[0008] Preferably, the step of depositing the composite solder to predetermined solder joint positions according to functional regions based on the regional formula index, recording particle distribution information, and obtaining deposition coordinate data includes: According to the risk coefficient parameters in the regional recipe index, the solder joints on the connector substrate are sorted in a deposition sequence, a dynamic path for preferential deposition in high-risk areas is established, and deposition trajectory data including timing control is obtained; According to the timing control information in the deposition trajectory data, the composite solder with the partition mark is subjected to a particle density coordinated control deposition process between adjacent solder joints to form a particle distribution pattern with a boundary buffer zone, thereby obtaining a spatial distribution state including a density gradient; The particle flow trajectory in the spatial distribution state is monitored and processed in real time, a predicted correlation between the particle displacement during the deposition process and the final distribution is established, and deposition coordinate data including particle position correction coefficients are obtained.

[0009] Preferably, the reflow soldering is performed on different functional areas using corresponding temperature curves according to the melting point parameters of the phase change particles in the regional formula index, and the temperature history of each area is recorded to obtain process history data, including: According to the melting point sequence and spatial distribution characteristics in the regional formula index, a preheating window calculation process is performed on the phase change timing of the phase change particles to obtain temperature curve parameters including phase change synchronization control; According to the phase change synchronization control information in the temperature curve parameters, differentiated heating rates and holding times are set for different functional areas, a compensation mechanism for thermal effects between areas is established, and a zoned heating strategy including a thermal compensation coefficient is obtained; According to the thermal compensation coefficient in the zoned heating strategy, parallel temperature control processing is performed on each functional area to monitor the solid-liquid conversion state of the phase change particles and obtain regional temperature change data including the phase change time sequence; The phase change timing information in the regional temperature change data is subjected to welding quality correlation analysis, a corresponding relationship between the temperature history and the particle distribution stability is established, and process history data including quality prediction parameters and phase change timing information is obtained.

[0010] Preferably, the method of performing parallel temperature control on each functional area according to the thermal compensation coefficient in the zoned heating strategy, monitoring the solid-liquid conversion state of the phase change particles, and obtaining regional temperature change data including the phase change time sequence includes: According to the thermal compensation coefficients of different functional areas in the zoned heating strategy, the power distribution of the heating elements is optimized, a dynamic balance mechanism of heat flux density between the areas is established, and parallel heating parameters including power distribution ratios are obtained; According to the power allocation ratio in the parallel heating parameters, each functional area is subjected to synchronous temperature control processing, the temperature gradient change of the area boundary is monitored in real time, and a thermal coupling compensation algorithm between adjacent areas is established to obtain regional temperature control data including boundary temperature correction amount; performing phase change particle state identification processing on the boundary temperature correction amount in the regional temperature control data, judging the progress of solid-liquid conversion by monitoring the latent heat release characteristics during the particle melting process, establishing a quantitative evaluation standard for the particle phase change completion, and obtaining particle state monitoring data including a phase change completion index; According to the phase change completion index in the particle state monitoring data, the regional temperature change is time-series calibrated, a time synchronization benchmark for the phase change process of each functional area is established, and regional temperature change data including the phase change time series is obtained.

[0011] Preferably, applying pulse thermal excitation to the packaged connector, collecting the surface temperature change curve of each solder joint, and obtaining thermal response data includes: According to the quality prediction parameters in the process history data, the pulse thermal excitation is subjected to phase change particle response threshold matching processing, an excitation intensity graded sequence based on particle melting point differences is established, and a pulse control strategy including threshold activation parameters is obtained; According to the threshold activation parameters in the pulse control strategy, gradient pulses are applied to the post-package connector in different regions, and the thermal coupling interference effect between adjacent solder joints is monitored to obtain an excitation response state including a regional thermal mutual interference coefficient; Performing solder joint density weight correction processing on the regional thermal mutual interference coefficient in the stimulus response state, establishing a signal amplification mechanism based on particle distribution density, and obtaining a temperature change trajectory including a density enhancement factor; The density enhancement factor in the temperature change trajectory is decoupled from the anisotropic thermal resistance of the packaging material, and the real signal of the solder point in the surface temperature is separated from the propagation distortion to obtain thermal response data containing the intrinsic characteristics of the solder point.

[0012] Preferably, the step of correlating and comparing the thermal response data with the process history data, identifying the solder joint defect type based on the curve deviation characteristics, and generating process adjustment parameters for optimizing the temperature curve of subsequent batches includes: The solder joint intrinsic characteristics in the thermal response data are subjected to hierarchical analysis according to the melting point gradient of the phase change particles, and a temperature-time two-dimensional deviation coordinate is established with the quality prediction parameters and phase change timing information in the process history data to obtain a hierarchical comparison matrix including the particle activation delay coefficient; Based on the particle activation delay coefficient in the layered comparison matrix, the curve deviation is classified into thermal impedance characteristics of pore defects, crack defects, and unfusion defects, and a defect quantification standard based on the degree of heat conduction path blocking is established to obtain a defect classification result including a thermal impedance amplification ratio; Based on the thermal impedance increase ratio in the defect classification result, the process parameters are analyzed for defect root causes, and a causal relationship chain is established, such as excessive heating rate leading to porosity, insufficient peak temperature leading to lack of fusion, and short holding time leading to cracks. A parameter adjustment vector including root cause weight distribution is obtained; According to the root cause weight distribution in the parameter adjustment vector, the temperature curves of subsequent batches are subjected to regional differential correction processing, a local process window narrowing mechanism based on defect density distribution is established, and process adjustment parameters including partition correction amplitude are obtained.

[0013] A second aspect of the present invention provides an intelligent assembly device for a precision connector, the intelligent assembly device for a precision connector comprising: The composite solder preparation module is used to add phase change particles with different melting points to the tin-silver-copper-based solder according to the functional regional differences of the solder joints in the connector, generate regional formula indexes, and obtain composite solder with partition identification; A precision deposition module, for depositing the composite solder to predetermined solder joint positions according to the functional areas based on the regional formula index, recording particle distribution information, and obtaining deposition coordinate data; A partitioned reflow soldering module is used to perform reflow soldering on different functional areas using corresponding temperature curves according to the melting point parameters of the phase change particles in the regional recipe index, record the temperature history of each area, and obtain process history data; The pulse thermal excitation detection module is used to apply pulse thermal excitation to the packaged connector, collect the surface temperature change curve of each solder point, and obtain thermal response data; The intelligent analysis and optimization module is used to correlate and compare the thermal response data with the process history data, identify the solder joint defect type according to the curve deviation characteristics, and generate process adjustment parameters for temperature curve optimization of subsequent batches.

[0014] The third aspect of the present invention provides an intelligent assembly system for precision connectors, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected through lines; the at least one processor calls the instructions in the memory so that the intelligent assembly system of the precision connector executes the steps of the above-mentioned intelligent assembly method of the precision connector.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, causes the computer to execute the steps of the above-mentioned intelligent assembly method for precision connectors.

[0016] By embedding phase-change particles into solder as thermal markers, this method establishes a direct link between soldering process information and post-assembly test results. Specifically, phase-change particles with varying melting points are added to a tin-silver-copper-based solder based on the functional regions of the connector solder joints. This generates a regional recipe index, resulting in a composite solder with zoned identification. The key to this step is that different functional regions (such as the high-power terminal area, signal terminal area, and ground shield area) have distinct thermal load characteristics. By selecting phase-change particles with corresponding melting points for regional marking, each solder joint carries a unique "thermal identification card." Subsequently, based on the regional recipe index, the composite solder is deposited at the predetermined solder joint locations according to the functional region. The particle distribution information is recorded to generate deposition coordinate data, ensuring precise positioning of the marking information. During the reflow soldering stage, reflow soldering is performed using temperature profiles corresponding to the melting point parameters of the phase-change particles in the regional recipe index for each functional region. The temperature history of each region is recorded to generate process history data. During this process, the phase-change particles undergo a complete melting-solidification cycle, freezing their internal crystal structure and thermal properties into a specific state, forming a thermal memory containing process information.

[0017] After assembly, pulsed thermal excitation is applied to the packaged connector, and the surface temperature change curve of each solder joint is collected to obtain thermal response data. Because the phase change particles have been assigned specific thermal characteristics during the welding process, when subjected to external thermal excitation, a thermal response signal corresponding to their internal state is generated. If defects such as pores, cracks, or lack of fusion exist within the solder joint, these defects will change the local heat conduction path and thermal resistance distribution, causing the thermal response characteristics of the phase change particles to shift, which in turn appears as a characteristic abnormal signal in the surface temperature change curve. By correlating and comparing the thermal response data with process history data, the solder joint defect type is identified based on the curve deviation characteristics, and process adjustment parameters are generated for temperature curve optimization of subsequent batches. This not only accurately identifies the defect type and severity, but also traces the specific process root cause, achieving a transition from passive detection to active prevention. This detection method based on phase change particle thermal marking overcomes the application limitations of traditional detection methods such as X-rays in the confined packaging space of precision connectors, achieving rapid, non-destructive, and comprehensive detection of all solder joints. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0019] Figure 1 Schematic diagram of an embodiment of an intelligent assembly method for precision connectors according to an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of an intelligent assembly device for precision connectors according to an embodiment of the present invention; Figure 3 Schematic diagram of an embodiment of an intelligent assembly system for precision connectors in an embodiment of the present invention.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0023] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0024] An embodiment of the present application provides an intelligent assembly method for a precision connector. Figure 1 A flowchart of a method for intelligent assembly of precision connectors provided in one embodiment of the present application. In this embodiment, the method includes: See also Figure 1 ,According to the functional area differences of the solder joints in the connector, phase change particles with different melting points are added to the tin-silver-copper based solder to generate a regional formula index and obtain a composite solder with a partition identification; In one embodiment of the present invention, the method of adding phase change particles with different melting points to the tin-silver-copper-based solder according to the functional regional differences of the solder joints in the connector, generating a regional formula index, and obtaining a composite solder with a partition identification includes: Based on the current density threshold and heat dissipation path length of each solder point in the connector, the solder points are evaluated for thermal risk level to obtain functional zoning data including risk coefficients. According to the risk coefficient in the functional zoning data, a phase change particle combination is selected according to the increasing melting point interval rule, and a gradient doping treatment is performed on the tin-silver-copper based solder to obtain a composite base material with a thermal response level; Optimizing the particle size ratio of phase change particles with different melting points in the composite base material to establish a spatial filling pattern of large and small particles, and associating and storing the particle ratio parameters, melting point sequence, spatial distribution characteristics, and risk coefficients with corresponding functional zones to obtain a regional formula index containing a prediction algorithm; According to the parameter configuration in the regional formula index, the composite base material is subjected to partition identification coding processing to obtain a composite solder with partition identification corresponding to the functional area one by one.

[0025] The following is a detailed description of the steps involved in the above embodiment: During the intelligent assembly of precision connectors, solder joints in different functional areas experience significant differences in current loads and heat dissipation conditions. These differences require a quantitative assessment to guide subsequent phase change particle placement. Specifically, a digital multimeter and current probe are used to measure the current density of each solder joint within the connector under rated operating conditions. The current density value is calculated by dividing the measured current by the cross-sectional area of ​​the solder joint. The heat dissipation path length is determined geometrically, using a vernier caliper to measure the straight-line distance from the center of the solder joint to the nearest heat dissipation surface (such as the connector housing or heat sink). This distance directly determines the ease of heat conduction. For example, in a typical board-to-board connector, solder joints carrying power signals have relatively short heat dissipation paths due to their high current draw and proximity to the connector edge. However, solder joints carrying data signals, located in the center of the connector, have lower current draw but longer heat dissipation paths, resulting in distinct thermal risk characteristics. Based on the measured data, a weighted scoring method is used to calculate the thermal risk level, using current density as the primary risk factor (weight 0.7) and heat dissipation path length as the secondary risk factor (weight 0.3). The weighted summation yields a comprehensive risk score. The risk factor is a numerical value between 0 and 1 derived from the standardized comprehensive risk score. It quantifies the relative probability of thermal failure of a solder joint during operation, with values ​​closer to 1 indicating a higher thermal risk. This quantitative assessment method allows all solder joints within a connector to be divided into functional zones based on their risk factors, providing a scientific basis for the differentiated configuration of phase change particles.

[0026] The selection and doping process of phase-change particles must strictly align with the thermal risk characteristics of each functional zone to ensure a distinguishable thermal response signal during subsequent testing. During implementation, phase-change particles are selected based on the risk coefficient range in the functional zone data. High-risk zones with a risk coefficient above 0.7 are assigned phase-change particles with higher melting points; medium-risk zones with a risk coefficient between 0.3 and 0.7 are assigned phase-change particles with medium melting points; and low-risk zones with a risk coefficient below 0.3 are assigned phase-change particles with lower melting points. Phase-change particles are typically microencapsulated using paraffin or fatty acid-based materials. The melting point differences between these materials provide a basis for temporal differentiation during subsequent thermal testing. Gradient doping is achieved through a batch mixing process. First, a tin-silver-copper-based solder is heated on a constant-temperature heating plate to a temperature slightly above its melting point to render it fluid. Then, phase-change particles with the corresponding melting points are slowly added to the base material at a predetermined volume ratio. A mechanical stirrer is used to thoroughly mix the particles to ensure uniform dispersion. The stirring speed and duration must be controlled to avoid excessive stirring that could damage the particles or oxidize the base material. Thermal response hierarchy refers to the phased response characteristics exhibited by phase-change particles with different melting points when subjected to thermal stimulation. Particles with lower melting points undergo phase change first, releasing latent heat. As the temperature continues to rise, particles with higher melting points undergo phase change in sequence, forming a temporal gradient response pattern. This hierarchical response mechanism provides rich characteristic information for subsequent defect detection, and solder joints in different functional areas will exhibit unique thermal response fingerprints.

[0027] Particle size ratio optimization is a key technical step to ensure that the phase change particles form an ideal distribution state in the solder matrix, which directly affects the formation effect of the heat conduction network and the intensity of the detection signal. In specific implementation, the phase change particles are first classified and screened according to the particle size using a screen grading device. They are usually divided into three levels: coarse particles, medium particles and fine particles. The selection of the particle size range needs to consider the microstructural characteristics of the solder matrix and the preparation process limitations of the phase change particles. The space filling mode refers to the arrangement and distribution of particles of different particle sizes in three-dimensional space. By rationally configuring the number ratio of particles at each level, fine particles can fill the gaps between coarse particles to form a tightly packed network structure. This structure is conducive to improving the overall thermal conductivity of the composite material. The particle size ratio optimization process adopts the volume fraction control method to determine the optimal particle size ratio combination based on the particle stacking theory. At the same time, it is necessary to consider the impact of the differences in phase change characteristics of particles of different particle sizes on the overall thermal response. During the data storage phase, the optimized particle ratio parameters (volume fraction of each particle level), melting point sequence (phase transition point data arranged by temperature), spatial distribution characteristics (particle distribution density and uniformity index in the matrix), and corresponding risk factors are stored in a database system. The prediction algorithm is a calculation method based on the physical properties of phase change particles and the principles of heat conduction. By inputting particle configuration parameters, the corresponding thermal response characteristic curve can be predicted, providing theoretical guidance for formulation optimization and quality control. The regional formulation index is a data set that includes complete technical parameters and prediction algorithms, providing a standardized technical basis for subsequent automated production and quality testing.

[0028] Zone identification coding is a crucial step in converting technical parameters into production instructions, ensuring that each composite solder is accurately mapped to its designated functional area. In specific implementation, each recipe is assigned a unique identification code based on the parameter configuration information stored in the regional recipe index. The coding system uses an alphanumeric combination to improve identification efficiency and error prevention. The coding rules typically include key elements such as risk level identification, phase change characteristic parameters, and formulation information. For example, the letters H, M, and L represent high, medium, and low risk levels, respectively, while the numerals represent the phase change particle characteristic parameters and formulation information. The coding process is performed by an automated batching system. After the operator enters the target code, the system automatically retrieves the corresponding recipe parameters and controls the input ratios of various raw materials, ensuring accuracy and reproducibility during the batching process. The prepared composite base material is then labeled with a zone identification code. This is typically done by affixing a label containing the code or printing a QR code on the packaging container to ensure accurate identification and traceability during subsequent use. Zone-identified composite solders are final products that have been coded and clearly mapped to specific functional areas. Each solder has a unique phase change particle configuration and thermal response characteristics. This identification management method not only ensures the standardization and traceability of the production process, but also provides a reliable product identification basis for subsequent automated deposition operations and quality inspection links.

[0029] In one embodiment of the present invention, the phase change particle combination is selected according to the risk coefficient in the functional zoning data and the melting point interval is increased, and the tin-silver-copper based solder is subjected to a gradient doping treatment to obtain a composite base material with a thermal response level, including: According to the numerical range of the risk coefficient in the functional zoning data, the phase change particles are subjected to surface activation pretreatment, and by controlling the wetting angle and interfacial tension on the particle surface, a grading standard for the bonding strength between the particles and the tin-silver-copper matrix is ​​established to obtain activated particles containing interface bonding parameters; According to the interface bonding parameters of the activated particles, the tin-silver-copper based solder is melt-mixed in batches, the dispersion depth and aggregation state of particles with different melting points in the matrix are controlled, a three-dimensional concentration gradient distribution pattern is established, and a premix containing a concentration gradient coefficient is obtained; The particle distribution in the premix is ​​homogenized and regulated, and particle agglomeration is eliminated through the synergistic effect of ultrasonic oscillation and temperature gradient field, and a statistical distribution law of the distance between particles is established to obtain a homogenized base material including particle spacing parameters; According to the particle spacing parameters in the homogenized base material, the composite base material is subjected to a thermal response level verification process, and the thermal conduction synergistic effect of particles in different regions is measured to obtain a composite base material with a controllable thermal response level.

[0030] The following is a detailed description of the steps involved in the above embodiment: Surface activation pretreatment of phase-change particles is a key process step that enhances their bonding with the Sn, Ag, and Cu matrix by modifying the particle surface's physicochemical properties. In implementation, the intensity level of the activation treatment is first determined based on the range of risk factors within the functional zoning data. Areas with higher risk factors require stronger particle-matrix bonding to ensure stability under high thermal stress. Surface activation pretreatment utilizes plasma surface modification technology. Phase-change particles are placed in the vacuum chamber of a plasma cleaner, where argon or oxygen is introduced as the working gas. Radio frequency power is applied to generate a plasma that bombards the particle surface. The extent of surface modification is controlled by adjusting the plasma power density and treatment time. Particles in high-risk areas receive a higher power density for deep modification, while particles in low-risk areas receive a lower power density for light modification. The wetting angle refers to the contact angle formed by a liquid on a solid surface. A smaller value indicates better wettability and a closer bond between the particle and the matrix. Interfacial tension refers to the surface tension per unit length at the interface between two dissimilar substances and directly influences the dispersion stability of the particles in the matrix. The activated particles were tested for wetting angle using a contact angle meter and interfacial tension using a surface tensiometer. A bonding strength grading system was established based on the test results. This bonding strength grading system categorizes the bonding strength between the particles and the substrate into several levels, such as primary bonding (wetting angle less than 30 degrees), secondary bonding (wetting angle 30-60 degrees), and tertiary bonding (wetting angle greater than 60 degrees). Interfacial bonding parameters are numerical indicators that quantify the interfacial bonding between the particles and the substrate, including the wetting angle, interfacial tension, and bonding strength. The activated particles obtained after surface activation pretreatment exhibit enhanced surface activity and improved interfacial bonding properties, providing a sound physicochemical foundation for the subsequent melt mixing process. This differentiated surface activation treatment ensures that phase change particles in different risk levels form the optimal bonding state with the SnAgCu matrix, avoiding the potential for over- or under-activation associated with a uniform treatment approach.

[0031] Batch melt mixing involves a precisely controlled process of differentiated mixing based on the interfacial bonding properties of the activated particles, aiming to achieve an ideal distribution of phase change particles within the matrix. Specifically, a tin-silver-copper-based solder is first heated to a liquid state in a temperature-controlled melting furnace. The temperature is controlled within a range of 20-30°C above the solder's melting point to ensure complete melting and avoid overheating and oxidation. The mixing process parameters are determined based on the interfacial bonding parameters of the activated particles. Particles with strong interfacial bonding utilize lower mixing temperatures and shorter mixing times, while particles with weaker interfacial bonding utilize higher mixing temperatures and longer mixing times to promote full bonding. Dispersion depth refers to the distance the phase change particles penetrate from the matrix surface into the interior. This depth is adjusted by controlling the stirring intensity and mixing time. Deep dispersion promotes the formation of a three-dimensional heat conduction network. Aggregation refers to the degree of particle agglomeration within the matrix, which can be divided into three basic states: monodisperse, small agglomerates, and large agglomerates. Monodisperse particles exhibit large interparticle distances and are evenly distributed, promoting uniform thermal response. The mixing process adopts a batch feeding method. Particles with strong interface binding ability are added first for preliminary mixing. After the dispersion is stable, particles with weak interface binding ability are added. This serial feeding method avoids mutual interference between different types of particles. The three-dimensional concentration gradient distribution pattern refers to the distribution characteristics of phase change particles in the length, width and height directions of the matrix showing a gradual change in concentration. It is achieved by controlling the stirring direction and intensity. The concentration gradient coefficient is a numerical parameter that quantitatively describes the spatial change rate of particle concentration. The grid sampling method is used to sample at different positions of the premix. The particle density of each area is calculated by microscopic observation and image analysis software, and then the concentration gradient coefficient is calculated. This batch melt mixing treatment method ensures that phase change particles with different melting points can form the most suitable distribution state in the matrix, providing a stable and reliable material basis for subsequent thermal response detection.

[0032] Homogenization treatment is a refined process designed to eliminate uneven particle distribution within a premix and establish an ideal spatial distribution. Specifically, the premix is ​​placed in an ultrasonic cleaning tank while a temperature gradient field is established for synergistic treatment. Ultrasonic oscillation generates cavitation through high-frequency mechanical vibration. The formation and collapse of cavitation bubbles produces intense microjets and shock waves, effectively breaking up particle agglomerates and promoting particle redistribution. A temperature gradient field is an artificially created spatial temperature distribution during the treatment process. This is achieved by placing heating or cooling elements at different temperatures at different locations within the tank. The temperature difference induces convection, promoting uniform particle distribution. The synergistic mechanism of ultrasonic oscillation and temperature gradient field is the superposition and enhancement of two physical effects: ultrasonic oscillation provides mechanical energy to break up agglomerates, while the temperature gradient field provides the driving force to promote redistribution. The combination of these two physical effects can achieve homogenization results that are difficult to achieve with either method alone. Particle agglomeration is the physical phenomenon in which multiple particles aggregate due to van der Waals forces or electrostatic interactions. Agglomeration can lead to localized excessive particle concentrations, which can affect the uniformity of heat transfer. During the treatment process, the treatment effect is controlled by real-time monitoring of ultrasonic power and temperature distribution. If the ultrasonic power is too low, it cannot effectively break up the agglomeration, while if the power is too high, it may damage the particle structure. The statistical distribution law of the distance between particles is a mathematical law obtained by measuring the distance between a large number of particle pairs and performing statistical analysis. The sample cross-section is observed using a metallographic microscope, and the distance between the particle centers is measured using image processing software. The mean, standard deviation and other parameters of the distance distribution are obtained through statistical analysis. The particle spacing parameters are quantitative indicators that describe the spatial distribution characteristics of the particles, including values ​​such as the average spacing, the standard deviation of the spacing distribution and the nearest neighbor distance. A homogenized base material refers to a composite material in which the particle distribution reaches an ideal state after a homogenization control treatment. It is characterized by uniform particle dispersion, spacing distribution that conforms to a preset law, and no obvious agglomeration. This homogenization control treatment ensures that the phase change particles form a regular and orderly distribution network in the matrix, providing a material basis guarantee for the accuracy and reproducibility of subsequent thermal response detection.

[0033] Thermal response hierarchy verification is a key step in verifying whether the phase change particle configuration in a composite matrix achieves the expected thermal performance. In practice, a verification experimental plan is designed based on the particle spacing parameters in the homogenized matrix, and the matrix sample is prepared into a standard-sized test specimen. The verification process utilizes an infrared thermal imager coupled with a controllable heat source. The specimen is placed on a test bench and heated in a stepwise manner using a resistance heater. The heating temperature range covers the melting point range of all phase change particles. Thermal response hierarchy refers to the layered response characteristic exhibited by phase change particles with different melting points undergoing phase transitions in a sequential order of temperature when an external thermal stimulus is applied to the composite matrix. This layered response manifests as distinct temperature distribution patterns and temporal changes in the infrared thermal image. During the measurement, the temperature distribution changes on the sample surface are recorded in real time using an infrared thermal imager. When the heating temperature reaches the melting point of a particular phase change particle, that particle begins to undergo phase transition and absorb latent heat, resulting in a change in the local temperature rise rate. This change manifests as a sudden change in color distribution in the thermal image. The thermal conductivity synergistic effect refers to the combined effects of mutually reinforcing or restraining effects during heat conduction produced by multiple phase-change particles with different melting points. When multiple particles are present simultaneously, their thermal conductivity properties influence each other, resulting in an overall effect different from that of a single particle. The synergistic effect is measured through comparative analysis. Base material samples containing a single particle type and those containing multiple particle types are tested, and the strength and type of the synergistic effect are quantified by comparing the differences in their thermal response curves. The strength of the synergistic effect refers to the magnitude of the change in thermal conductivity produced by the coexistence of multiple particles relative to a single particle system. This is quantified by calculating the ratio of the thermal conductivity coefficients of the mixed system to the single system. Synergistic effects can be classified into positive (the thermal conductivity of the mixed system is better than the arithmetic mean of the individual systems) and negative (the thermal conductivity of the mixed system is worse than the arithmetic mean of the individual systems). This is determined by comparing the direction of deviation between the measured and theoretically calculated values. Verification results are evaluated using characteristic parameters of the thermal response curve, including the temperature at each phase transition point, response time, and peak intensity. These parameters must meet pre-defined design requirements to confirm the performance of the composite base material. Controllable thermal response levels are achieved through precise control of the type, ratio, and distribution of phase-change particles, resulting in predictable and reproducible thermal response characteristics. This controllability ensures the stability and accuracy of subsequent testing. The thermal response level verification process quantitatively confirms that the thermal properties of the composite base material meet design expectations, verifies the correctness of the initial process parameter settings, and provides a reliable verification standard for subsequent production control and quality management.

[0034] Please continue reading Figure 1 , according to the regional formula index, depositing the composite solder to the predetermined solder joint position according to the functional area, recording the particle distribution information, and obtaining deposition coordinate data; In one embodiment of the present invention, the step of depositing the composite solder to predetermined solder joint positions according to functional regions based on the regional recipe index, recording particle distribution information, and obtaining deposition coordinate data includes: According to the risk coefficient parameters in the regional recipe index, the solder joints on the connector substrate are sorted in a deposition sequence, a dynamic path for preferential deposition in high-risk areas is established, and deposition trajectory data including timing control is obtained; According to the timing control information in the deposition trajectory data, the composite solder with the partition mark is subjected to a particle density coordinated control deposition process between adjacent solder joints to form a particle distribution pattern with a boundary buffer zone, thereby obtaining a spatial distribution state including a density gradient; The particle flow trajectory in the spatial distribution state is monitored and processed in real time, a predicted correlation between the particle displacement during the deposition process and the final distribution is established, and deposition coordinate data including particle position correction coefficients are obtained.

[0035] The following is a detailed description of the steps involved in the above embodiment: Deposition sequence sorting is an optimization control process that determines the order in which composite solders are applied based on the thermal risk level of each solder joint. In practice, risk coefficient parameters for each solder joint are first extracted from a regional recipe index database. Risk coefficients range from 0 to 1, with higher values ​​indicating greater thermal risk. The sorting process utilizes a priority queue algorithm, ranking all solder joints from highest to lowest risk. Solder joints with the same risk coefficient are then sorted based on their importance within the connector. For a 64-pin precision connector, for example, the risk coefficient for the power pin is 0.9, the risk coefficient for the signal pin is 0.5, and the risk coefficient for the ground pin is 0.3. The resulting deposition order is power pins first, signal pins second, and ground pins last. A dynamic path is an optimal trajectory planned based on the deposition equipment's movement efficiency and the spatial location of the solder joints. Unlike a static, linear path, a dynamic path adjusts in real time based on actual deposition progress and equipment status. Path planning utilizes a traveling salesman algorithm to find the path with the shortest total distance while satisfying priority constraints. Timing control refers to a mechanism that precisely manages the temporal sequence and intervals of deposition operations, including parameters such as the deposition start time, duration, and interval time for each solder joint. Deposition trajectory data is a collection of data that records the path and timing of the deposition equipment throughout the entire operation. The data format includes fields such as coordinate position, arrival time, and deposition parameters. This strategy of prioritizing deposition in high-risk areas ensures a more precise phase change particle configuration in solder joints with higher thermal risk, avoiding the potential for improper critical solder joint configurations caused by random deposition methods.

[0036] Coordinated particle density control between adjacent solder joints is a refined control technology designed to prevent interference between the phase change particle distributions of adjacent solder joints. In specific implementation, the spatial relationship and time interval between the currently deposited solder joint and adjacent solder joints are determined based on timing control information from the deposition trajectory data. Coordinated particle density control utilizes a gradient adjustment method. As the deposition equipment approaches a previously deposited solder joint, the particle density at the edge of the current solder joint is automatically reduced to prevent overlap of high-density areas. For example, for a 0.5mm spacing between two adjacent solder joints, the particle density is gradually reduced from 100% of the standard density to 70% within a 0.1mm radius of the boundary, forming a transition buffer zone. This boundary buffer zone is a transitional region of lower particle density between adjacent solder joints. It prevents mixing of phase change particles from different functional areas, which could affect subsequent inspection accuracy. The width of the buffer zone is determined by the size of the solder joint and the diffusion characteristics of the phase change particles. Smaller solder joints correspond to narrower buffer zones, while more diffusive particles correspond to wider buffer zones. A particle distribution pattern refers to the spatial arrangement of phase change particles on a two-dimensional plane, and includes basic types such as uniform, gradient, and annular distributions. The density gradient refers to the spatial rate of change of particle density. This gradient is adjusted by controlling the deposition equipment's movement speed and discharge rate. Spatial distribution describes the distribution characteristics of particles within a specified area, encompassing metrics such as density distribution, gradient variation, and boundary clarity. This coordinated control mechanism ensures clear functional boundaries between adjacent solder joints, avoids cross-contamination of phase change particles, and ensures the independence and accuracy of signals from each region during subsequent thermal response testing.

[0037] Real-time monitoring and processing of particle flow trajectories is a dynamic monitoring technology that tracks the motion of phase-change particles during deposition and predicts their final distribution. In practice, a high-speed camera system captures the deposition area in real time at a frame rate of 1000 frames per second to capture the rapid movement of particles. Image processing employs a particle tracking algorithm, which calculates the trajectory and velocity vector by comparing particle position changes in consecutive frames. A particle flow trajectory is the complete path of a single phase-change particle from leaving the deposition nozzle to its final resting position. Its shape is influenced by factors such as gravity, surface tension, and airflow resistance. For example, a 30-micron-diameter phase-change particle has a flight time of approximately 50 milliseconds under standard deposition conditions, and its horizontal displacement ranges from 0.1 to 0.3 mm. Predictive correlation is a computational method that predicts future particle distribution based on monitored particle motion data. This prediction is performed using kinematic equations combined with empirical correction coefficients. Particle displacement refers to the distance a particle deviates from its intended landing point to its actual landing point. This displacement is caused by factors such as airflow disturbances, surface irregularities, and electrostatic effects. The position correction coefficient is a numerical parameter that quantifies the degree of particle displacement. It is calculated by statistically analyzing the actual displacement data of a large number of particles and is used to correct process parameters in the subsequent deposition process. Deposition coordinate data, a collection of coordinate information that records the final resting position of each particle, is accurate to the micron level and provides foundational data for subsequent quality assessment and process optimization. This real-time monitoring and prediction mechanism promptly detects anomalies during the deposition process and enables process adjustments, ensuring that phase change particles are precisely distributed to the designated locations according to design requirements, thereby improving deposition accuracy and yield.

[0038] Please continue reading Figure 1 , according to the melting point parameters of the phase change particles in the regional formula index, reflow soldering is performed on different functional areas using corresponding temperature curves, and the temperature history of each area is recorded to obtain process history data; In one embodiment of the present invention, according to the melting point parameters of the phase change particles in the regional recipe index, reflow soldering is performed on different functional areas using corresponding temperature curves, and the temperature history of each area is recorded to obtain process history data, including: According to the melting point sequence and spatial distribution characteristics in the regional formula index, a preheating window calculation process is performed on the phase change timing of the phase change particles to obtain temperature curve parameters including phase change synchronization control; According to the phase change synchronization control information in the temperature curve parameters, differentiated heating rates and holding times are set for different functional areas, a compensation mechanism for thermal effects between areas is established, and a zoned heating strategy including a thermal compensation coefficient is obtained; According to the thermal compensation coefficient in the zoned heating strategy, parallel temperature control processing is performed on each functional area to monitor the solid-liquid conversion state of the phase change particles and obtain regional temperature change data including the phase change time sequence; The phase change timing information in the regional temperature change data is subjected to welding quality correlation analysis, a corresponding relationship between the temperature history and the particle distribution stability is established, and process history data including quality prediction parameters and phase change timing information is obtained.

[0039] The following is a detailed description of the steps involved in the above embodiment: The phase transition timing preheating window calculation process precisely determines the temperature conditions and timing required for each type of phase change particle to initiate phase transition. In specific implementation, melting point sequences and spatial distribution characteristics are first extracted from the regional recipe index database. The melting point sequence is a sequence of melting temperature data for each phase change particle, arranged in order of temperature. The spatial distribution characteristics are geometric parameters describing the three-dimensional positional distribution of the particles within the solder. For example, three phase change particles with different melting points—low melting point particles at 183°C, medium melting point particles at 192°C, and high melting point particles at 201°C—are distributed in layers within the solder. The phase transition timing refers to the specific point in time when each phase change particle begins to undergo a solid-liquid transition, which is influenced by factors such as heating rate, particle size, and ambient temperature. The preheating window refers to the temperature preheating period before the phase change particle reaches its melting point. This period ensures a uniform temperature distribution within the particle and provides energy for the phase transition process. The calculation process utilizes the heat conduction equation combined with phase transition kinetics theory to numerically determine the preheating starting temperature and preheating duration for each particle. Taking phase change particles with a diameter of 50 microns as an example, the preheating window needs to start 15°C before the melting point and last for about 30 seconds. Phase change synchronization control refers to a control technology that adjusts the heating parameters to make phase change particles with different melting points undergo phase changes in sequence within a predetermined time sequence, avoiding the concentrated release of heat caused by simultaneous phase changes. The temperature curve parameters are a set of numerical parameters that describe the temperature change pattern over time during the heating process, including key parameters such as heating rate, holding temperature, holding time, and cooling rate. This precise timing control ensures that the phase change particles in different functional areas can be activated in sequence according to the preset order, avoiding the problems of thermal stress concentration and unstable welding quality that may be caused by disordered phase change.

[0040] Differentiated heating rate and hold times are optimized control processes that develop personalized heating strategies based on the thermal characteristics of different functional areas. In practice, the heating requirements of each functional area are determined based on the phase change synchronization control information in the temperature curve parameters. High-risk areas require faster heating rates to shorten high-temperature exposure time, medium-risk areas use moderate heating rates to balance efficiency and quality, and low-risk areas use slower heating rates to ensure sufficient preheating. The heating rate refers to the rate of temperature increase per unit time. High-risk areas are set at 2°C per second, medium-risk areas at 1.5°C per second, and low-risk areas at 1°C per second. The hold time is the duration required to maintain the target temperature, which must ensure complete melting of phase change particles and sufficient solder flow. Inter-regional thermal effects refer to the heat transfer caused by temperature differences between adjacent functional areas, which affects the actual temperature distribution in each area. The thermal effect compensation mechanism adjusts the heating power and timing of boundary areas to offset the heat transfer effect of adjacent areas. The compensation amount is calculated based on thermal conduction theory and experimental calibration data. Taking adjacent high-risk and low-risk areas as an example, the heating power at the boundary of the low-risk area needs to be increased by 10% to compensate for heat loss. The thermal compensation coefficient is a numerical parameter that quantifies the degree of compensation, indicating the adjustment ratio relative to the standard heating power. A positive value indicates an increase in power, while a negative value indicates a decrease in power. A zoned heating strategy is a personalized heating plan tailored to the different functional areas within the connector, including specific heating parameters and compensation measures for each area. This differentiated approach fully considers the complex thermal environment within precision connectors and avoids the localized overheating or insufficient heating that can result from a uniform heating approach.

[0041] Parallel temperature control is a coordinated control technology that simultaneously and independently adjusts the temperature of multiple functional areas while monitoring phase transition states in real time. In specific implementation, multiple heating controllers are configured based on the thermal compensation coefficients in the zoned heating strategy, with each functional area assigned an independent heating circuit and temperature sensor. Parallel control utilizes a distributed control architecture, with data exchange and coordinated operation between the heating circuits via a communication bus. Monitoring the solid-liquid transition state of phase-change particles utilizes the principle of differential scanning calorimetry, detecting changes in heat flow during heating to determine the onset and progress of phase transition. Solid-liquid transition states include solid, transition, and liquid. The transition state refers to the intermediate state during the phase transition where solid and liquid coexist. State determination is based on characteristic changes in the heat flow curve: an endothermic peak in the heat flow indicates the onset of phase transition, and the end of the peak indicates the completion of the phase transition. For a phase-change particle with a melting point of 192°C, for example, the solid-liquid transition process lasts approximately 45 seconds, with the heat flow exhibiting a bell-shaped curve that first increases and then decreases. The phase transition timing refers to the temporal sequence and intervals of solid-liquid transitions among various phase-change particles. This timing information is valuable for subsequent thermal response testing. Regional temperature change data is a time series recording of the temperature changes in each functional area throughout the heating process. The data is sampled 10 times per second to ensure sufficient temporal resolution. The advantage of parallel control is that it can simultaneously handle the complex thermal processes of multiple areas, avoiding the time delays and cross-region interference associated with serial control, ensuring optimal heating performance in each functional area.

[0042] Welding quality correlation analysis is a data mining technique that predicts welding quality by analyzing the intrinsic relationship between temperature history data and the distribution of phase-change particles. Specifically, the phase-change timing information is first extracted from the regional temperature change data, including key parameters such as the time coordinates, temperature values, and duration of each phase change point. Correlation analysis utilizes multiple regression analysis, using phase-change timing parameters as independent variables and welding quality indicators as dependent variables. Predictive relationships are established through training with historical data. The temperature history refers to the complete temperature change process from heating to cooling, which contains a wealth of process information and quality characteristics. Particle distribution stability refers to the ability of phase-change particles to maintain their predetermined distribution after undergoing heating and cooling cycles. A stable particle distribution enhances the accuracy of subsequent testing. Stability evaluation quantitatively analyzes the particle distribution images before and after heating, calculating the particle position offset and distribution uniformity change. The correspondence relationship is a mathematical mapping between temperature history parameters and particle distribution stability indicators, derived through statistical analysis of experimental data. Taking the heating rate as an example, a too rapid heating rate can lead to excessive thermal stress in the particles, affecting distribution stability, while a too slow heating rate prolongs high-temperature exposure and may cause particle aggregation. Quality prediction parameters are key indicators that can predict welding quality, extracted based on correlation analysis results. These include parameters such as phase change completeness, timing deviation, and temperature uniformity. Process history data is a comprehensive data set containing complete heating process information and quality prediction results, providing a traceable data foundation for subsequent quality testing and process optimization. This correlation analysis method enables quantitative prediction from process to quality outcome, providing a scientific quality control method for the intelligent manufacturing of precision connectors.

[0043] In one embodiment of the present invention, the temperature control process is performed on each functional area in parallel according to the thermal compensation coefficient in the zoned heating strategy, the solid-liquid conversion state of the phase change particles is monitored, and the regional temperature change data including the phase change time sequence is obtained, including: According to the thermal compensation coefficients of different functional areas in the zoned heating strategy, the power distribution of the heating elements is optimized, a dynamic balance mechanism of heat flux density between the areas is established, and parallel heating parameters including power distribution ratios are obtained; According to the power allocation ratio in the parallel heating parameters, each functional area is subjected to synchronous temperature control processing, the temperature gradient change of the area boundary is monitored in real time, and a thermal coupling compensation algorithm between adjacent areas is established to obtain regional temperature control data including boundary temperature correction amount; performing phase change particle state identification processing on the boundary temperature correction amount in the regional temperature control data, judging the progress of solid-liquid conversion by monitoring the latent heat release characteristics during the particle melting process, establishing a quantitative evaluation standard for the particle phase change completion, and obtaining particle state monitoring data including a phase change completion index; According to the phase change completion index in the particle state monitoring data, the regional temperature change is time-series calibrated, a time synchronization benchmark for the phase change process of each functional area is established, and regional temperature change data including the phase change time series is obtained.

[0044] The following is a detailed description of the steps involved in the above embodiment: Power allocation optimization is a control technology that precisely adjusts the power of heating elements based on the thermal characteristics and compensation requirements of each functional zone. In implementation, thermal compensation coefficients for different functional zones are first extracted from a zone heating strategy database. These coefficients reflect the power adjustment requirements for each zone relative to standard heating conditions. The heating elements utilize an array of independently controlled resistive heaters, with each functional zone equipped with a dedicated heating unit. Power allocation is calculated using a proportional allocation algorithm, using the baseline power as a reference value and calculating the actual power requirements for each zone based on the thermal compensation coefficients. For a three-zone connector, for example, the high-risk zone has a thermal compensation coefficient of 1.2, requiring 120% of the baseline power; the medium-risk zone has a thermal compensation coefficient of 1.0, requiring 100% of the baseline power; and the low-risk zone has a thermal compensation coefficient of 0.8, requiring 80% of the baseline power. Inter-zone heat flux refers to the rate of heat flow per unit area. Different heating power levels between functional zones create heat flux gradients. The dynamic balancing mechanism is an automatic control system that maintains a stable heat flux by adjusting the power distribution between adjacent zones in real time. When the heat flux in a zone deviates from the set value, the system automatically adjusts the heating power for that zone and adjacent zones. The power allocation ratio is the percentage of total power allocated to each functional area. This ratio provides a power baseline for subsequent synchronized heating control. Parallel heating parameters are a set of parameters that encompass power allocation information and control logic for all functional areas, ensuring coordinated heating of each area according to the predetermined power ratio. This optimized power allocation method avoids thermal stress concentration caused by large power differences between adjacent areas, ensuring stability and uniformity of the overall heating process.

[0045] Synchronous heating control is a precision control technology that achieves coordinated heating across multiple zones and dynamically compensates for boundary heat loss based on power allocation. In specific implementation, the output power of each zone's heater is set according to the power allocation ratio in the parallel heating parameters, and a PID controller is used to independently control the temperature of each zone in a closed-loop manner. Synchronous heating requires that each functional zone's temperature rise simultaneously according to a preset heating curve, with the synchronization error of the heating rate controlled within ±0.1°C / second. Zone boundaries refer to the intersections between adjacent functional zones, where temperature transitions occur due to heat conduction. The temperature gradient, defined as the rate of change of temperature per unit distance within the boundary region, is monitored in real time using a thermocouple array, with a sensor spacing of 0.2mm to ensure sufficient spatial resolution. Thermal coupling refers to the mutual influence of heat conduction between adjacent zones, causing heat to flow from high-temperature to low-temperature areas, affecting the actual temperature distribution in each zone. The thermal coupling compensation algorithm calculates compensation power based on the boundary temperature gradient and adjusts heating parameters in real time. The algorithm utilizes a combination of feedforward control and feedback correction. Taking adjacent high-temperature and low-temperature areas as an example, when the boundary temperature gradient exceeds the set threshold, the algorithm automatically increases the heating power of the low-temperature area and appropriately reduces the power of the high-temperature area. The compensation amount is calculated based on the gradient size and the regional heat capacity. The boundary temperature correction refers to the adjustment required to the heating power of each area to maintain the stability of the regional boundary temperature. The unit of the correction amount is power percentage. The regional temperature control data is a data set that records the temperature change trajectory and control parameters of each functional area during the synchronous heating process. It contains real-time temperature values, power output, correction amounts and other information. This synchronous control mechanism ensures that each functional area can coordinate the heating according to the predetermined temperature curve, avoiding thermal stress and phase change timing confusion caused by temperature differences between areas.

[0046] Phase-change particle state identification is a real-time monitoring technology that determines the progress of a phase-change particle's solid-liquid transition by analyzing boundary temperature variation characteristics. In specific implementation, phase-change state analysis is performed based on the changing trends of boundary temperature corrections in regional temperature control data. Abnormal fluctuations in boundary temperature reflect changes in the thermodynamic state of the phase-change particle. Phase-change particles absorb or release latent heat during solid-liquid transitions. This latent heat change causes local temperature field disturbances that propagate to the regional boundaries. The latent heat release characteristic refers to the temporal curve of heat release during melting, manifested as a temporary slowdown or stagnation in the rate of temperature rise. State identification utilizes signal processing techniques to detect the latent heat release signal by performing differential operations and feature extraction on the temperature curve. The solid-liquid transition progress refers to the percentage of completion of the phase-change particle's transition from fully solid to fully liquid. Progress is determined by comparing the integrated latent heat release with the theoretical value. The particle phase transition completion degree is a quantitative indicator that describes the degree of completion of the phase transition process. The value ranges from 0 to 100%, with 0% indicating a fully solid state and 100% indicating a fully liquid state. The quantitative evaluation standard is a calculation method for phase change completeness based on thermodynamic theory and experimental data. It takes into account factors such as particle size, melting point, and heating rate. For a 30-micron diameter phase change particle, for example, the phase change completeness is determined to be 95% when a latent heat release signal is detected for 20 seconds and the release reaches 95% of the theoretical value. The phase change completeness index is a numerical parameter describing the transition state of each phase change particle, providing a quantitative basis for subsequent time series analysis. Particle state monitoring data is a real-time data set containing state information and completion indicators for all phase change particles. The data is updated five times per second to ensure timely monitoring. This state recognition technology enables precise monitoring of the phase change process, providing a reliable state benchmark for subsequent time series calibration and quality control.

[0047] Time series calibration is a time calibration technique that uses a phase change completion index to determine the time base for each functional area's phase change process and synchronize the process. In specific implementation, the key time points for each region's phase change, including the start, peak, and end times of the phase change, are determined based on the trajectory of the phase change completion index in the particle state monitoring data. Time series calibration utilizes a multi-point calibration method, selecting the time points of 25%, 50%, 75%, and 100% phase change completion as calibration benchmarks. Time series processing of regional temperature changes is achieved through a time axis alignment algorithm, which aligns the temperature data of each functional area with the phase change time base. The time synchronization base is a unified time reference system shared by all functional areas, with the base time starting at the moment the first region begins phase change. Time synchronization of phase change processes refers to the process of unifying the phase change time series data of different regions into the same time coordinate system for comparative analysis. Taking a three-zone system as an example, the high-risk zone begins phase change at a reference time of 0 seconds, the medium-risk zone begins phase change at a reference time of 15 seconds, and the low-risk zone begins phase change at a reference time of 30 seconds. Through time series calibration, the phase change time differences between zones can be accurately recorded. Phase change timing is time series data that describes the temporal order and intervals of the phase change process in each functional zone. This data provides an important reference for subsequent quality analysis and process optimization. After time series calibration, the regional temperature change data can accurately reflect the temperature evolution pattern and phase change characteristics of each functional zone under a unified time reference. This time series calibration process ensures the temporal consistency and comparability of the multi-zone phase change process, laying the time foundation for establishing a standardized phase change timing database and realizing quality comparison analysis between batches.

[0048] Please continue reading Figure 1 , apply pulse thermal excitation to the packaged connector, collect the surface temperature change curve of each solder point, and obtain thermal response data; In one embodiment of the present invention, applying pulse thermal excitation to the packaged connector, collecting surface temperature change curves of each solder joint, and obtaining thermal response data includes: According to the quality prediction parameters in the process history data, the pulse thermal excitation is subjected to phase change particle response threshold matching processing, an excitation intensity graded sequence based on particle melting point differences is established, and a pulse control strategy including threshold activation parameters is obtained; According to the threshold activation parameters in the pulse control strategy, gradient pulses are applied to the post-package connector in different regions, and the thermal coupling interference effect between adjacent solder joints is monitored to obtain an excitation response state including a regional thermal mutual interference coefficient; Performing solder joint density weight correction processing on the regional thermal mutual interference coefficient in the stimulus response state, establishing a signal amplification mechanism based on particle distribution density, and obtaining a temperature change trajectory including a density enhancement factor; The density enhancement factor in the temperature change trajectory is decoupled from the anisotropic thermal resistance of the packaging material, and the real signal of the solder point in the surface temperature is separated from the propagation distortion to obtain thermal response data containing the intrinsic characteristics of the solder point.

[0049] The following is a detailed description of the steps involved in the above embodiment: Phase-change particle response threshold matching is a technique that precisely configures pulse excitation parameters based on quality characteristics from historical process data. In practice, quality prediction parameters are first extracted from the process history database, including key indicators such as phase change completeness, temperature uniformity, and particle distribution stability. The phase-change particle response threshold refers to the minimum excitation energy required to trigger a particle thermal response. This threshold is related to factors such as the particle's melting point, size, and packaging depth. Threshold matching uses a table lookup method to establish an excitation threshold database based on the physical properties of particles with different melting points. For example, for three phase-change particles with different melting points, the response threshold for a particle with a melting point of 183°C is 2.5 J / cm², for a particle with a melting point of 192°C it is 3.2 J / cm², and for a particle with a melting point of 201°C it is 4.0 J / cm². A graded excitation intensity sequence is a sequence of pulse power levels arranged according to the melting point of the particles. The purpose of this grading is to ensure that particles with different melting points are activated sequentially and do not respond simultaneously. The graded sequence adopts an incremental gradient design, with low-melting-point particles using low-intensity excitation and high-melting-point particles using high-intensity excitation. The intensity interval is set to 0.5 joules / square centimeter to ensure differentiation. The threshold activation parameters are a complete set of parameters that include the excitation intensity, pulse width, and excitation interval at each level. The parameter configuration ensures that each type of particle can be activated step by step in a predetermined order. The pulse control strategy is a step-by-step excitation scheme based on the threshold activation parameters. The strategy specifies the timing arrangement and intensity control logic of the excitation. This threshold matching process ensures that the pulse excitation can accurately correspond to the response characteristics of different types of phase change particles, avoiding particle damage caused by excessively strong excitation or insufficient response caused by excessively weak excitation.

[0050] Regional gradient pulse application is a precision control technology that uses a pulse control strategy to provide differentiated thermal stimulation to post-package connectors. In specific implementation, the pulse generator's output parameters are set according to the threshold activation parameters in the pulse control strategy, and an adjustable-power laser or infrared heater is used as the stimulation source. Regional application involves independent pulse stimulation of different functional zones within the connector, with each zone using corresponding stimulation intensity and timing parameters. Gradient pulses are an excitation method that gradually increases the pulse intensity during the same stimulation process. The gradient design ensures that low-melting-point particles respond first, and as the intensity increases, high-melting-point particles are activated in turn. For example, in a three-zone connector, the high-risk zone first receives a pulse stimulation of 4.0 J / cm² for 50 milliseconds, followed by a medium-risk zone at 3.2 J / cm² for 50 milliseconds, and finally a low-risk zone at 2.5 J / cm² for 50 milliseconds. Thermal coupling interference between adjacent solder joints is a physical phenomenon whereby heat generated by one solder joint during stimulation transfers to adjacent solder joints, affecting their thermal response characteristics. Interference effects are monitored using a high-precision thermocouple array, with sensors placed in the boundary areas between solder joints to detect temperature changes caused by heat conduction. The regional thermal mutual interference coefficient is a numerical parameter that quantifies the degree of thermal influence between adjacent regions. The coefficient calculation is based on the temperature conduction model and measured data, and the value range is 0-1, with 0 indicating no influence and 1 indicating complete influence. The excitation response state is a set of state parameters that describe the thermal response characteristics and mutual influence relationships of each functional region under pulse excitation, and includes information such as response intensity, response time, and degree of mutual interference. This regional gradient excitation method achieves the selective activation of phase change particles in different functional regions, and at the same time provides an interference correction basis for subsequent signal processing by monitoring the thermal coupling effect.

[0051] Solder density weighting is a signal enhancement technique that compensates for thermal response signals based on differences in solder point density. In practice, the density weighting coefficient for each solder point region is calculated based on the regional thermal interference coefficient in the stimulus-response state. Solder point density refers to the number of solder points per unit area. High-density areas, due to the concentration of solder points, generate a thermal stacking effect, while low-density areas are relatively isolated. Weighting correction uses a normalization method, using a standard density as a benchmark. Regions with density above the standard have a weighting coefficient greater than 1, while regions with density below the standard have a weighting coefficient less than 1. For a 64-pin connector, for example, the central signal area has a solder point density of 25 per square centimeter, and the weighting coefficient is set to 1.3; the peripheral power area has a solder point density of 15 per square centimeter, and the weighting coefficient is set to 0.8. Particle density refers to the spatial distribution density of phase change particles in the solder and is related to, but not identical to, solder point density. The signal amplification mechanism uses the density weighting coefficient to adjust the gain of the thermal response signal. Signals in high-density areas are appropriately attenuated to compensate for the stacking effect, while signals in low-density areas are amplified to improve detection sensitivity. The density enhancement factor is a numerical parameter that characterizes the degree of signal amplification. Its value is equal to the ratio of the corrected signal strength to the original signal strength. The temperature trajectory is a curve that records the temperature change of each solder joint over time during the stimulation process. Density weighting is applied to the trajectory to more accurately reflect the true thermal response characteristics of each solder joint. This density weighting process eliminates the impact of uneven solder joint distribution on test results, ensuring comparability and consistency of test signals for solder joints in different density areas.

[0052] Decoupling the anisotropic thermal resistance of packaging materials is a signal recovery technique that separates the true information in surface inspection signals from propagation distortion. In practice, the signal's spectral characteristics and propagation properties are analyzed based on the density enhancement factor in the temperature trajectory. Anisotropy in packaging materials refers to the material property of the packaging plastic exhibiting varying thermal conductivity in different directions. The longitudinal thermal resistance is typically higher than the lateral thermal resistance. This anisotropic thermal resistance causes directional attenuation and spectral distortion during thermal signal propagation, affecting surface inspection accuracy. Decoupling uses a deconvolution algorithm to restore the original thermal response signal of the solder joint by establishing a transfer function for the packaging material. This transfer function incorporates physical parameters such as the thermal diffusivity, thickness, and anisotropy ratio, and is obtained through calibration experiments using standard samples. For example, for a 2 mm thick epoxy resin package, the longitudinal thermal diffusivity is 0.8 mm² / s, the lateral thermal diffusivity is 1.2 mm² / s, and the anisotropy ratio is 0.67. The true solder joint signal refers to the original thermal response signal generated by the phase change particles within the solder joint, unaffected by the propagation of the packaging material. Propagation distortion refers to the distortion phenomena such as amplitude attenuation, phase delay, and spectral deformation that occur when a thermal signal propagates through the packaging material to the surface. Signal separation is achieved through a combination of frequency-domain filtering and time-domain deconvolution. The separated true signal accurately reflects the actual state within the solder joint. The intrinsic characteristics of a solder joint refer to characteristic parameters extracted after decoupling that characterize the internal defect state of the solder joint. These include key indicators such as response peak, response time, and spectral distribution. Thermal response data is the final test result containing all information about the intrinsic characteristics of the solder joint. The quality of this data directly impacts the accuracy of defect identification. This decoupling processing technology effectively eliminates the interference of packaging materials on the test signal, significantly improving the accuracy and reliability of internal defect detection.

[0053] Please continue reading Figure 1 , the thermal response data is correlated and compared with the process history data, the solder joint defect type is identified according to the curve deviation characteristics, and the process adjustment parameters are generated for temperature curve optimization of subsequent batches.

[0054] In one embodiment of the present invention, correlating and comparing the thermal response data with the process history data, identifying the solder joint defect type based on the curve deviation characteristics, and generating process adjustment parameters for optimizing the temperature curve of subsequent batches includes: The solder joint intrinsic characteristics in the thermal response data are subjected to hierarchical analysis according to the melting point gradient of the phase change particles, and a temperature-time two-dimensional deviation coordinate is established with the quality prediction parameters and phase change timing information in the process history data to obtain a hierarchical comparison matrix including the particle activation delay coefficient; Based on the particle activation delay coefficient in the layered comparison matrix, the curve deviation is classified into thermal impedance characteristics of pore defects, crack defects, and unfusion defects, and a defect quantification standard based on the degree of heat conduction path blocking is established to obtain a defect classification result including a thermal impedance amplification ratio; Based on the thermal impedance increase ratio in the defect classification result, the process parameters are analyzed for defect root causes, and a causal relationship chain is established, such as excessive heating rate leading to porosity, insufficient peak temperature leading to lack of fusion, and short holding time leading to cracks. A parameter adjustment vector including root cause weight distribution is obtained; According to the root cause weight distribution in the parameter adjustment vector, the temperature curves of subsequent batches are subjected to regional differential correction processing, a local process window narrowing mechanism based on defect density distribution is established, and process adjustment parameters including partition correction amplitude are obtained.

[0055] The following is a detailed description of the steps involved in the above embodiment: Hierarchical analysis is a data processing technique that analyzes thermal response data hierarchically based on the melting point differences of phase change particles and compares them with historical data. In specific implementation, intrinsic solder joint characteristics are first extracted from the thermal response data, including key parameters such as response peak, response time, and decay rate. The melting point gradient of phase change particles refers to the layered structure formed by phase change particles with different melting points according to their temperature. Particles with melting points of 183°C, 192°C, and 201°C form three temperature strata. Hierarchical analysis uses a temperature threshold segmentation method to divide the thermal response signal into three independent data layers based on the particle melting point range. Each layer contains the response characteristics of the corresponding melting point particle. The temperature-time two-dimensional deviation coordinate system is a two-dimensional coordinate system established with temperature as the horizontal axis and time as the vertical axis. It is used to quantify the deviation between measured and theoretical data. During the coordinate establishment process, the quality prediction parameters and phase change timing information in the process history data are used as the theoretical benchmark, and the measured thermal response data is used as the comparison object. Taking particles with a melting point of 183°C as an example, the theoretical response time is 20 seconds, and the measured response time is 23 seconds, so the time deviation is +3 seconds; the theoretical response temperature is 185°C, and the measured response temperature is 188°C, so the temperature deviation is +3°C. The particle activation delay coefficient is a numerical parameter that quantifies the degree of response lag of phase change particles. The coefficient is equal to the ratio of the measured activation time to the theoretical activation time. The hierarchical comparison matrix is ​​a data structure that arranges the deviation data of three temperature layers in a matrix form. The rows of the matrix represent different melting point layers, and the columns represent different types of deviation parameters. This hierarchical analysis method can accurately identify response anomalies of phase change particles with different melting points, avoiding signal interference and feature ambiguity that may be caused by mixed analysis.

[0056] Thermal impedance feature classification is a technology that identifies internal solder joint defect types based on particle activation delay characteristics. In specific implementation, the characteristic patterns of curve deviations are analyzed based on the particle activation delay coefficients in the hierarchical comparison matrix. The thermal impedance characteristics of a pore defect are characterized by a prolonged response time but minimal peak temperature change, as the air inside the pore has poor thermal conductivity, hindering heat transfer. The thermal impedance characteristics of a crack defect are characterized by a reduced response peak and multiple peaks in the waveform. Cracks block the continuous heat conduction path, resulting in dispersed heat flow. The thermal impedance characteristics of a lack of fusion defect are characterized by a significantly prolonged response time and significantly reduced peak temperature. The lack of fusion creates a thermal barrier that hinders heat transfer. The degree of thermal path blockage refers to the degree to which the defect affects heat conduction within the solder joint; a higher degree of blockage indicates a more severe defect. Defect quantification uses a blockage degree grading system: mild blockage corresponds to a delay coefficient of 1.1-1.3, moderate blockage corresponds to a delay coefficient of 1.3-1.6, and severe blockage corresponds to a delay coefficient greater than 1.6. Taking a typical porosity defect as an example, a 0.1 mm diameter porosity corresponds to a delay coefficient of approximately 1.2, while a 0.2 mm diameter porosity corresponds to a delay coefficient of approximately 1.5. The thermal impedance amplification ratio (TAR) refers to the increase in thermal impedance relative to the normal state when a defect is present. A larger amplification ratio indicates a more severe defect. The defect classification result is a comprehensive assessment that includes information on defect type, severity, and location, providing accurate defect signature data for subsequent root cause analysis. This thermal impedance signature-based classification method leverages the differences in the impact of different defect types on heat conduction, enabling accurate identification and quantitative assessment of multiple defects within solder joints.

[0057] Defect root cause analysis is a causal analysis technique that identifies the cause of defects by analyzing the correlation between defect characteristics and process parameters. In specific implementation, the correlation between defects and process parameters is established based on the thermal impedance amplification ratio in the defect classification results. The mechanism by which porosity defects result from excessively rapid heating rates is that the solder melts rapidly, preventing internal gases from escaping quickly, resulting in bubbles. These bubbles are retained during solidification, forming pores. The mechanism by which incomplete fusion defects result from insufficient peak temperature is that the solder fails to fully melt, leading to insufficient bonding between particles and the formation of weak interfaces. The mechanism by which crack defects result from short holding times is that internal stress is insufficiently released during solder solidification, forming microcracks in weak areas. A causal chain is a relationship network that connects process parameters and defect types according to causal logic. The chain includes three links: parameter deviation, physical mechanism, and defect manifestation. Root cause weighting is a statistical analysis that determines the impact of each process parameter on a specific defect. The weight value represents the contribution of each parameter to the defect. Taking porosity defects as an example, the heating rate is weighted 0.6, the peak temperature is weighted 0.3, and the holding time is weighted 0.1. This weight distribution reflects that the heating rate is the primary factor in porosity formation. The parameter adjustment vector is a vector data containing the adjustment direction and adjustment range of each process parameter. The direction of the vector indicates whether the parameter should be increased or decreased, and the magnitude of the vector indicates the adjustment range. This root cause tracing analysis enables reverse tracing from defect symptoms to process causes, providing scientific theoretical guidance for precise process adjustments.

[0058] Regional differential correction is a refined control technology that adjusts process parameters individually for different functional areas based on defect distribution characteristics. In specific implementation, the adjustment strategy for each functional area is determined based on the root cause weight distribution in the parameter adjustment vector. Regional correction involves applying different parameter adjustment schemes to different functional areas within the connector to avoid the negative impact of uniform adjustments on normal areas. Differential correction employs a relative adjustment method, using the current process parameters as a benchmark and calculating the adjustment amount based on defect severity. Defect density distribution refers to the spatial distribution of defects within each functional area, with areas with higher density requiring more significant process adjustments. For example, in a three-area connector, three porosity defects were detected in the high-risk area, resulting in a 15% reduction in the heating rate; one crack defect was detected in the medium-risk area, resulting in a 10% increase in the holding time; and no defects were detected in the low-risk area, leaving the parameters unchanged. The local process window refers to the allowable range of process parameter variation within a specific functional area. This window range is determined based on the process stability and quality requirements of that area. The process window narrowing mechanism is an adaptive control method that narrows the process parameter variation range based on defect detection results, with the degree of narrowing proportional to the defect density. The zone correction range refers to the specific adjustment amount for process parameters in each functional area. The calculation of the correction range takes into account multiple factors, including defect type, severity, and regional characteristics. The process adjustment parameters are a complete set of parameters encompassing the correction plan for all functional areas. The parameter format includes information such as area identification, parameter type, adjustment range, and priority. This regional differential correction method enables precise adjustment of process parameters, avoiding the potential for over- or under-correction caused by global adjustments, significantly improving the stability of product quality in subsequent batches.

[0059] The above describes the intelligent assembly method of the precision connector according to the embodiment of the present invention. The following describes the intelligent assembly device of the precision connector according to the embodiment of the present invention. Figure 2 An embodiment of the intelligent assembly device of a precision connector according to an embodiment of the present invention includes: The composite solder preparation module 101 is used to add phase change particles with different melting points to the tin-silver-copper based solder according to the functional regional differences of the solder joints in the connector, generate a regional formula index, and obtain a composite solder with a partition identification; A precision deposition module 102 is configured to deposit the composite solder to predetermined solder joint positions according to the functional regions based on the regional recipe index, record particle distribution information, and obtain deposition coordinate data; A zoned reflow soldering module 103 is configured to perform reflow soldering on different functional areas using corresponding temperature curves according to the melting point parameters of the phase change particles in the zone recipe index, record the temperature history of each area, and obtain process history data; The pulse thermal excitation detection module 104 is used to apply pulse thermal excitation to the packaged connector, collect the surface temperature change curve of each solder point, and obtain thermal response data; The intelligent analysis and optimization module 105 is used to correlate and compare the thermal response data with the process history data, identify the solder joint defect type according to the curve deviation characteristics, and generate process adjustment parameters for temperature curve optimization of subsequent batches.

[0060] above Figure 2 The intelligent assembly device of the precision connector in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The intelligent assembly system of the precision connector in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0061] Figure 3 This is a structural diagram of an intelligent assembly system for precision connectors provided by an embodiment of the present invention. The intelligent assembly system 200 for precision connectors may have relatively large differences due to different configurations or performances, and may include one or more processors 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 (for example, one or more mass storage devices) for storing application programs 233 or data 232. The memory 220 and the storage medium 230 may be temporary storage or permanent storage. The program stored in the storage medium 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the intelligent assembly system 200 for precision connectors. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, and execute a series of instruction operations in the storage medium 230 on the intelligent assembly system 200 for precision connectors to implement the steps of the above-mentioned intelligent assembly method for precision connectors.

[0062] The intelligent assembly system 200 for precision connectors may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the intelligent assembly system for precision connectors shown does not constitute a limitation on the intelligent assembly system for precision connectors provided by the present invention, and may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.

[0063] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer executes the steps of the intelligent assembly method of the precision connector.

[0064] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0066] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An intelligent assembly method for precision connectors, characterized in that: include: According to the functional area differences of the solder joints in the connector, phase change particles with different melting points are added to the tin-silver-copper based solder to generate a regional formula index and obtain a composite solder with a partition identification. Depositing the composite solder to predetermined solder joint positions according to the functional areas according to the regional formula index, recording particle distribution information, and obtaining deposition coordinate data; According to the melting point parameters of the phase change particles in the regional formula index, reflow soldering is performed on different functional areas using corresponding temperature curves, and the temperature history of each area is recorded to obtain process history data; Apply pulse thermal excitation to the packaged connector, collect the surface temperature change curve of each solder point, and obtain thermal response data; The thermal response data is correlated and compared with the process history data, the solder joint defect type is identified according to the curve deviation characteristics, and process adjustment parameters are generated for temperature curve optimization of subsequent batches.

2. The intelligent assembly method of a precision connector according to claim 1, characterized in that: The method includes adding phase change particles with different melting points to the tin-silver-copper based solder according to the functional regional differences of the solder joints in the connector, generating a regional formula index, and obtaining a composite solder with a partition identification, including: Based on the current density threshold and heat dissipation path length of each solder point in the connector, the solder points are evaluated for thermal risk level to obtain functional zoning data including risk coefficients. According to the risk coefficient in the functional zoning data, a phase change particle combination is selected according to the increasing melting point interval rule, and a gradient doping treatment is performed on the tin-silver-copper based solder to obtain a composite base material with a thermal response level; Optimizing the particle size ratio of phase change particles with different melting points in the composite base material to establish a spatial filling pattern of large and small particles, and associating and storing the particle ratio parameters, melting point sequence, spatial distribution characteristics, and risk coefficients with corresponding functional zones to obtain a regional formula index containing a prediction algorithm; According to the parameter configuration in the regional formula index, the composite base material is subjected to partition identification coding processing to obtain a composite solder with partition identification corresponding to the functional area one by one.

3. The intelligent assembly method of a precision connector according to claim 2, characterized in that: According to the risk coefficient in the functional zoning data, a phase change particle combination is selected according to the increasing melting point interval rule, and the tin-silver-copper based solder is subjected to a gradient doping treatment to obtain a composite base material with a thermal response level, including: According to the numerical range of the risk coefficient in the functional zoning data, the phase change particles are subjected to surface activation pretreatment, and by controlling the wetting angle and interfacial tension on the particle surface, a grading standard for the bonding strength between the particles and the tin-silver-copper matrix is ​​established to obtain activated particles containing interface bonding parameters; According to the interface bonding parameters of the activated particles, the tin-silver-copper based solder is melt-mixed in batches, the dispersion depth and aggregation state of particles with different melting points in the matrix are controlled, a three-dimensional concentration gradient distribution pattern is established, and a premix containing a concentration gradient coefficient is obtained; The particle distribution in the premix is ​​homogenized and regulated, and particle agglomeration is eliminated through the synergistic effect of ultrasonic oscillation and temperature gradient field, and a statistical distribution law of the distance between particles is established to obtain a homogenized base material including particle spacing parameters; According to the particle spacing parameters in the homogenized base material, the composite base material is subjected to a thermal response level verification process, and the thermal conduction synergistic effect of particles in different regions is measured to obtain a composite base material with a controllable thermal response level.

4. The intelligent assembly method of a precision connector according to claim 1, characterized in that: The method of depositing the composite solder to predetermined solder joint positions according to the functional areas based on the regional formula index, recording particle distribution information, and obtaining deposition coordinate data includes: According to the risk coefficient parameters in the regional recipe index, the solder joints on the connector substrate are sorted in a deposition sequence, a dynamic path for preferential deposition in high-risk areas is established, and deposition trajectory data including timing control is obtained; According to the timing control information in the deposition trajectory data, the composite solder with the partition mark is subjected to a particle density coordinated control deposition process between adjacent solder joints to form a particle distribution pattern with a boundary buffer zone, thereby obtaining a spatial distribution state including a density gradient; The particle flow trajectory in the spatial distribution state is monitored and processed in real time, a predicted correlation between the particle displacement during the deposition process and the final distribution is established, and deposition coordinate data including particle position correction coefficients are obtained.

5. The intelligent assembly method of a precision connector according to claim 1, characterized in that: According to the melting point parameters of the phase change particles in the regional formula index, reflow soldering is performed on different functional areas using corresponding temperature curves, and the temperature history of each area is recorded to obtain process history data, including: According to the melting point sequence and spatial distribution characteristics in the regional formula index, a preheating window calculation process is performed on the phase change timing of the phase change particles to obtain temperature curve parameters including phase change synchronization control; According to the phase change synchronization control information in the temperature curve parameters, differentiated heating rates and holding times are set for different functional areas, a compensation mechanism for thermal effects between areas is established, and a zoned heating strategy including a thermal compensation coefficient is obtained; According to the thermal compensation coefficient in the zoned heating strategy, parallel temperature control processing is performed on each functional area to monitor the solid-liquid conversion state of the phase change particles and obtain regional temperature change data including the phase change time sequence; The phase change timing information in the regional temperature change data is subjected to welding quality correlation analysis, a corresponding relationship between the temperature history and the particle distribution stability is established, and process history data including quality prediction parameters and phase change timing information is obtained.

6. The intelligent assembly method of a precision connector according to claim 5, characterized in that: According to the thermal compensation coefficient in the zoned heating strategy, parallel temperature control processing is performed on each functional area, the solid-liquid conversion state of the phase change particles is monitored, and regional temperature change data including the phase change time sequence is obtained, including: According to the thermal compensation coefficients of different functional areas in the zoned heating strategy, the power distribution of the heating elements is optimized, a dynamic balance mechanism of heat flux density between the areas is established, and parallel heating parameters including power distribution ratios are obtained; According to the power allocation ratio in the parallel heating parameters, each functional area is subjected to synchronous temperature control processing, the temperature gradient change of the area boundary is monitored in real time, and a thermal coupling compensation algorithm between adjacent areas is established to obtain regional temperature control data including boundary temperature correction amount; performing phase change particle state identification processing on the boundary temperature correction amount in the regional temperature control data, judging the progress of solid-liquid conversion by monitoring the latent heat release characteristics during the particle melting process, establishing a quantitative evaluation standard for the particle phase change completion, and obtaining particle state monitoring data including a phase change completion index; According to the phase change completion index in the particle state monitoring data, the regional temperature change is time-series calibrated, a time synchronization benchmark for the phase change process of each functional area is established, and regional temperature change data including the phase change time series is obtained.

7. The intelligent assembly method of a precision connector according to claim 1, characterized in that: The method of applying pulse thermal excitation to the packaged connector and collecting the surface temperature change curve of each solder joint to obtain thermal response data includes: According to the quality prediction parameters in the process history data, the pulse thermal excitation is subjected to phase change particle response threshold matching processing, an excitation intensity graded sequence based on particle melting point differences is established, and a pulse control strategy including threshold activation parameters is obtained; According to the threshold activation parameters in the pulse control strategy, gradient pulses are applied to the post-package connector in different regions, and the thermal coupling interference effect between adjacent solder joints is monitored to obtain an excitation response state including a regional thermal mutual interference coefficient; Performing solder joint density weight correction processing on the regional thermal mutual interference coefficient in the stimulus response state, establishing a signal amplification mechanism based on particle distribution density, and obtaining a temperature change trajectory including a density enhancement factor; The density enhancement factor in the temperature change trajectory is decoupled from the anisotropic thermal resistance of the packaging material, and the real signal of the solder point in the surface temperature is separated from the propagation distortion to obtain thermal response data containing the intrinsic characteristics of the solder point.

8. The intelligent assembly method of a precision connector according to claim 1, characterized in that: The step of correlating and comparing the thermal response data with the process history data, identifying the solder joint defect type according to the curve deviation characteristics, and generating process adjustment parameters for optimizing the temperature curve of subsequent batches includes: The solder joint intrinsic characteristics in the thermal response data are subjected to hierarchical analysis according to the melting point gradient of the phase change particles, and a temperature-time two-dimensional deviation coordinate is established with the quality prediction parameters and phase change timing information in the process history data to obtain a hierarchical comparison matrix including the particle activation delay coefficient; Based on the particle activation delay coefficient in the layered comparison matrix, the curve deviation is classified into thermal impedance characteristics of pore defects, crack defects, and unfusion defects, and a defect quantification standard based on the degree of heat conduction path blocking is established to obtain a defect classification result including a thermal impedance amplification ratio; Based on the thermal impedance increase ratio in the defect classification result, the process parameters are analyzed for defect root causes, and a causal relationship chain is established, such as excessive heating rate leading to porosity, insufficient peak temperature leading to lack of fusion, and short holding time leading to cracks. A parameter adjustment vector including root cause weight distribution is obtained; According to the root cause weight distribution in the parameter adjustment vector, the temperature curves of subsequent batches are subjected to regional differential correction processing, a local process window narrowing mechanism based on defect density distribution is established, and process adjustment parameters including partition correction amplitude are obtained.

9. An intelligent assembly device for precision connectors, characterized in that: The intelligent assembly device for the precision connector adopts the intelligent assembly method for the precision connector according to any one of claims 1 to 8, and the intelligent assembly device for the precision connector comprises: The composite solder preparation module is used to add phase change particles with different melting points to the tin-silver-copper-based solder according to the functional regional differences of the solder joints in the connector, generate regional formula indexes, and obtain composite solder with partition identification; A precision deposition module, for depositing the composite solder to predetermined solder joint positions according to the functional areas based on the regional formula index, recording particle distribution information, and obtaining deposition coordinate data; A partitioned reflow soldering module is used to perform reflow soldering on different functional areas using corresponding temperature curves according to the melting point parameters of the phase change particles in the regional recipe index, record the temperature history of each area, and obtain process history data; The pulse thermal excitation detection module is used to apply pulse thermal excitation to the packaged connector, collect the surface temperature change curve of each solder point, and obtain thermal response data; The intelligent analysis and optimization module is used to correlate and compare the thermal response data with the process history data, identify the solder joint defect type according to the curve deviation characteristics, and generate process adjustment parameters for temperature curve optimization of subsequent batches.

10. An intelligent assembly system for precision connectors, characterized in that: The intelligent assembly system of the precision connector includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the intelligent assembly system of the precision connector to execute the steps of the intelligent assembly method of the precision connector according to any one of claims 1 to 8.

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