Chip dehydration method

Abstract

The invention relates to the technical field of chip production, in particular to a chip dehydration method which comprises the following steps: S1, providing a jig; the shape of the jig is cylindrical; a plurality of limiting grooves are formed in the jig in the circumferential direction at equal intervals. S2, a plurality of chip extraction discs loaded with the degolded chips are placed in the limiting grooves correspondingly; s3, putting the jig into a washing machine; and S4, starting the washing machine, and carrying out dehydration treatment on the degolded chip. The jig is manufactured according to the mode of the inner container of the washing machine and is directly placed in the washing machine, the chip extraction disc is directly clamped in the limiting groove of the jig during dehydration each time, materials are directly taken from the limiting groove during material collection, and convenience, rapidness, high efficiency and simplicity are achieved; in addition, dehydration treatment of the chip is achieved through centrifugal force of the washing machine, the washing machine is used as a centrifugal power source, and the effects of low cost and convenient operation are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of chip production, and in particular relates to a chip dehydration method. Background Art

[0002] Due to ball planting defects, the chip often needs to be re-planted. Before re-planting, the residual metal on the original pins of the chip needs to be removed, so it needs to be de-metallized. After the de-metallization, the surface of the chip is completely wet. Usually, it is directly dried in an oven after cleaning. However, the drying effect of a large number of chip trays stacked in the oven is poor, so it needs to be dehydrated before drying.

[0003] Currently, specialized dehydration equipment on the market is typically designed for specific products, resulting in poor applicability. Furthermore, the equipment is expensive, complex to operate, and requires frequent manual loading and unloading, resulting in low overall dehydration efficiency. While some alternative solutions can reduce costs to a certain extent, these solutions often fail to meet the demands of mass production and are prone to damage to chips due to vibration or stress concentration during the high-speed dehydration process. Summary of the Invention

[0004] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art and provide a chip dehydration method. The purpose of the present invention is achieved through the following technical solution: A chip dehydration method comprises the following steps:

[0005] S1. Provide a jig; the jig is cylindrical in shape; the jig is provided with a plurality of limiting grooves at equal intervals along the circumference;

[0006] S2, placing multiple chip trays containing gold-de-goldened chips into the limiting slots respectively;

[0007] S3. Place the fixture into the washing machine;

[0008] S4. Start the washing machine to dehydrate the chip after the gold is removed.

[0009] The present invention is further configured to include the following steps in step S4:

[0010] A1. Obtain chip extraction tray specifications and dehydration requirements, match the appropriate dehydration model with the database, and determine the initial centrifugation speed and time parameters.

[0011] A2. Based on the standardized dehydration configuration plan, real-time collection of vibration and stress distribution information during high-speed centrifugation is performed to determine whether dynamic adjustment of centrifugation parameters is required.

[0012] A3. Analyze the correlation between vibration and stress distribution, optimize centrifugal speed and time parameters, and form an optimized dehydration operation plan;

[0013] A4: Combine the chip plate's geometric characteristics and material properties to simulate stress concentration areas and predict potential damage risks;

[0014] A5: Train the damage suppression model based on historical data, determine the vibration reduction parameter adjustment plan, and generate a damage-optimized operating configuration;

[0015] A6: Real-time monitoring of temperature and humidity, adjustment of centrifugal speed or extension of dehydration time, and obtaining dehydration parameters suitable for the environment;

[0016] A7: Use digital twin technology to build a virtual model, analyze dehydration efficiency and quality change trends, and generate the final batch dehydration plan;

[0017] A8: Extract the washing machine's operation log, analyze the washing machine's status, and determine the technical configuration for long-term optimization.

[0018] A9: Integrate data from multiple batches, analyze the adaptability trends of extraction trays for chips of different specifications, and form a universal dehydration process optimization model.

[0019] The present invention is further configured such that obtaining chip tray specification data and dehydration requirement parameters in step A1 includes establishing a chip tray specification database covering geometric dimensions, material properties and dehydration requirement parameters, and generating a standardized dehydration configuration plan through an intelligent matching algorithm.

[0020] The present invention is further configured such that the real-time collection of vibration and stress distribution information during high-speed centrifugation in step A2 includes deploying a high-sensitivity sensor network to dynamically capture the vibration amplitude and stress distribution during the centrifugation process, and determine whether the operating parameters need to be adjusted in combination with preset thresholds.

[0021] The present invention is further configured such that the analysis of the correlation between vibration and stress distribution in step A3 includes analyzing the spatial relationship between the vibration source and the stress distribution, and optimizing the centrifugal speed and time parameters to reduce stress concentration.

[0022] The present invention is further configured such that the simulation of stress concentration areas in step A4 includes utilizing finite element analysis technology, combined with the chip disc geometric characteristics and material properties, to evaluate the impact of stress concentration on the chip structure and identify potential damage risk areas.

[0023] The present invention is further configured such that the training of the damage suppression model based on historical data in step A5 includes extracting operating data of chips of similar specifications from historical dehydration cases, constructing the damage suppression model and generating a vibration reduction parameter adjustment scheme.

[0024] The present invention is further configured such that the real-time monitoring of temperature and humidity in step A6 includes deploying temperature and humidity sensors, dynamically adjusting the centrifugal speed or extending the dehydration time to ensure that the environmental conditions match the dehydration requirements.

[0025] The present invention is further configured such that the construction of the virtual model by digital twin technology in step A7 includes simulating the entire chip dehydration process, analyzing the dehydration efficiency and quality change trends, and verifying the impact of parameter adjustment on batch production.

[0026] The present invention is further configured such that extracting the equipment operation log in step A8 includes analyzing maintenance cycles and fault data, evaluating equipment operation stability, and formulating a long-term operation optimization strategy;

[0027] The integration of multiple batches of data in step A9 includes analyzing the adaptability change trend of chip extraction trays of different specifications through a cloud computing platform, optimizing equipment flexibility and forming a highly universal and efficient dehydration process model.

[0028] The beneficial effects of the present invention are as follows: the present invention makes the jig in the manner of the inner tank of a washing machine and directly places it in the washing machine. Each time dehydration is performed, the chip extraction plate is directly clamped in the limiting groove of the jig. When collecting materials, the materials are directly taken out from the limiting groove, which is convenient, fast, efficient and simple. In addition, the dehydration of the chips is achieved through the centrifugal force of the washing machine, and the washing machine is used as the centrifugal power source, which has the effects of low cost and easy operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The invention is further described with reference to the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without making any creative effort.

[0030] Figure 1 This is a schematic diagram of the structure of the fixture of the present invention in cooperation with a washing machine;

[0031] Figure 2 is a flow chart of the present invention;

[0032] Among them: 1. Washing machine; 2. Fixture; 21. Limiting groove; 3. Chip extraction tray. DETAILED DESCRIPTION

[0033] The present invention is further described with reference to the following examples.

[0034] Depend on Figures 1 to 2 It can be seen that an embodiment of the present invention provides a chip dehydration method, comprising the following steps: S1, providing a jig 2; the shape of the jig 2 is cylindrical; the jig 2 is provided with a plurality of limiting grooves 21 at equal intervals along the circumferential direction; S2, placing a plurality of chip collection trays 3 containing de-goldened chips into the limiting grooves 21 respectively; S3, placing the jig 2 into the washing machine 1; S4, starting the washing machine 1 to dehydrate the de-goldened chips.

[0035] Specifically, in the chip dehydration method described in this embodiment, the jig 2 is made in the manner of the inner tank of the washing machine 1 and is directly placed in the washing machine 1. Each time dehydration is performed, the chip extraction plate 3 is directly clamped in the limiting groove 21 of the jig 2. When collecting the material, the material is directly taken from the limiting groove 21, which is convenient, fast, efficient and simple. In addition, the dehydration of the chip is achieved by the centrifugal force of the washing machine 1, and the washing machine 1 is used as the centrifugal power source, which has the effects of low cost and easy operation.

[0036] An embodiment of the present invention provides a chip dehydration method. First, in step A1, obtaining the chip tray 3 specification data and dehydration requirement parameters is the basis of the entire method. In order to achieve high-precision parameter initialization, it is necessary to establish a chip tray 3 specification database. The database covers information such as the geometric dimensions, material properties and dehydration requirement parameters of the chip tray 3. For example, for a specific model of chip tray 3, its geometric dimensions may include diameter D, thickness T, pore size distribution R, while the material properties include elastic modulus E, Poisson's ratio ν and density ρ. The dehydration requirement parameters involve target humidity Ht and maximum allowable stress σmax. On this basis, a standardized dehydration configuration scheme is generated through an intelligent matching algorithm. The core of the intelligent matching algorithm is to use fuzzy matching technology and a weight calculation model to determine the best-fitting dehydration model. Taking a certain example, it is assumed that there are multiple known dehydration models M1 to Mn in the database, and each model corresponds to a set of initial centrifugal speed V0 and time parameter T0. The intelligent matching algorithm calculates the similarity score S and selects the model with the highest score as the initial configuration scheme. The similarity score S is calculated as: S = ∑(wi × |Xi - Yi|), where wi represents the weight coefficients of each parameter, Xi represents the actual parameter value of the current chip extraction tray 3, and Yi represents the model parameter value. This formula accurately matches the most suitable dehydration model and determines the initial centrifugal speed V0 and time parameter T0.

[0037] In step A2, real-time collection of vibration and stress distribution information during high-speed centrifugation is a key step in ensuring the dynamic adjustment mechanism. This process is achieved by deploying a highly sensitive sensor network. The sensor network includes accelerometers, strain gauges, and fiber optic sensors, which are used to dynamically capture the vibration amplitude A and stress distribution σ during the centrifugation process. For example, in a certain experimental scenario, the accelerometer is installed at the shaft position of the washing machine 1 to monitor the vibration frequency f and amplitude A during operation; the strain gauge is attached to the surface of the chip extraction plate 3 to measure the local stress value σlocal. The data from these sensors is uploaded to the central control system via a wireless transmission module and compared with the preset threshold. If it is detected that the vibration amplitude A exceeds the safety threshold Ath or the stress value σ exceeds the allowable range σmax, the system will trigger the dynamic adjustment mechanism, indicating that the centrifugal parameters need to be corrected. This real-time response capability significantly improves the safety and stability of the dehydration process.

[0038] Step A3 further analyzes the correlation between vibration and stress distribution, and optimizes the centrifugal speed and time parameters. In this process, by analyzing the spatial relationship between the vibration source and the stress distribution, the area that may cause stress concentration is identified. For example, when the washing machine 1 is running, due to the imbalance of the shaft or the geometric asymmetry of the chip extraction tray 3, the point of action of the centrifugal force F may deviate from the center, thereby causing local stress concentration. In order to reduce this stress concentration, the finite difference method is used to optimize the centrifugal parameters. Specifically, the relationship between the centrifugal speed V and the time parameter T can be expressed by the following formula: F=ma=m(V2 / r), where m is the mass of the chip extraction tray 3, a is the centripetal acceleration, and r is the rotation radius. By adjusting V and T, the distribution of F can be made more uniform, thereby reducing the risk of stress concentration. In addition, the system will train the damage suppression model based on historical data, generate a vibration reduction parameter adjustment plan, and finally form an optimized dehydration operation plan.

[0039] Step A4 combines the geometric characteristics and material properties of the chip tray 3 to simulate stress concentration areas and predict potential damage risks. This process mainly relies on finite element analysis technology. In practical applications, the grid units are first divided according to the three-dimensional geometric model of the chip tray 3, and the corresponding material properties are assigned to each unit. Then, based on the direction and magnitude of the centrifugal force F, the stress value σelement of each unit is calculated. For example, for a specific unit, its stress value can be calculated by the following formula: σelement = F / A, where A is the cross-sectional area of ​​the unit. Through stress analysis of each unit, a complete stress distribution map can be generated and stress concentration areas can be identified. For example, in a certain experimental case, it was found that the stress value of the edge area of ​​the chip tray 3 increased significantly due to the sudden change in the geometric shape, making it a potential damage risk area. For such problems, the probability of damage can be reduced by adjusting the design of the chip tray 3 or optimizing the centrifugal parameters.

[0040] Step A5 trains a damage suppression model based on historical data and determines a vibration damping parameter adjustment plan. The core of this process lies in extracting operational data for chips of similar specifications from historical dehydration cases and using machine learning algorithms to construct a damage suppression model. For example, suppose a historical database stores operational records of multiple batches of chip dehydration, including centrifugal speed V, time parameter T, vibration amplitude A, stress value σ, and the final damage condition D. By training this data using a support vector machine (SVM) algorithm, a prediction model can be generated to estimate the damage probability P under different operating parameter combinations. The specific training process is as follows: First, the historical data is divided into training and test sets and normalized. Then, the SVM algorithm is used to calculate the hyperplane equation w·x+b=0, where w is the weight vector, x is the input feature vector, and b is the bias term. The model parameters are optimized through cross-validation, ultimately generating a vibration damping parameter adjustment plan. For example, if the prediction model indicates a high damage probability for a certain operating parameter combination, the system will automatically adjust the centrifugal speed or extend the dehydration time to reduce the damage risk.

[0041] Step A6 monitors temperature and humidity in real time, adjusting the centrifugal speed or extending the dehydration time to adapt to changing environmental conditions. In practice, temperature and humidity sensors are deployed to dynamically acquire ambient temperature Tenv and humidity Henv. For example, when the ambient temperature exceeds the set threshold Tth or the humidity falls below the set threshold Hth, the system determines that the current environmental conditions are unfavorable for stable operation of the dehydration process. In this case, adjusting the centrifugal speed V or extending the dehydration time T can improve the dehydration effect. For example, when the ambient humidity is low, appropriately increasing the centrifugal speed can accelerate water evaporation; when the ambient temperature is too high, extending the dehydration time can help prevent chip damage caused by overheating. Furthermore, the system establishes an environmental adaptation model based on historical data to guide parameter adjustment strategies. For example, through linear regression analysis, the relationship between centrifugal speed V and ambient humidity Henv can be derived: V = kHenv + b, where k is the slope and b is the intercept. This model can quickly generate environmentally adapted dehydration parameters.

[0042] Step A7 uses digital twin technology to construct a virtual model, analyze dehydration efficiency and quality trends, and generate a final batch dehydration plan. During this process, a virtual model is first constructed based on the actual equipment and process parameters. For example, the virtual model includes the geometry of the washing machine 1, the physical properties of the chip extraction tray 3, and the kinetic behavior during the dehydration process. By simulating the entire chip dehydration process, the impact of different parameter combinations on dehydration efficiency η and quality Q can be analyzed. For example, dehydration efficiency η can be calculated using the following formula: η = (Mt - Mf) / Mt, where Mt is the initial wet weight and Mf is the final dry weight. Dehydration quality Q is assessed by measuring the chip surface flatness and residual moisture content. By comparing η and Q values ​​under different parameter combinations, the impact of parameter adjustments on batch production can be verified and the optimal dehydration plan can be generated. For example, in one experimental case, the highest dehydration efficiency and best quality were achieved when the centrifugal speed V was set to 8000 rpm and the time parameter T was set to 10 minutes, so this was selected as the final batch dehydration plan.

[0043] Step A8 extracts the operating log of washing machine 1, analyzes its status, and determines the technical configuration for long-term optimization. In practice, the operating log of washing machine 1 includes information such as maintenance cycles, fault data, and energy consumption records. For example, by analyzing the energy consumption data during a particular dehydration batch, the economic efficiency of washing machine 1's operation can be evaluated; by statistically analyzing the frequency and type of faults, weak links in the equipment can be identified. For example, suppose a washing machine 1 experiences abnormal vibration after 100 hours of continuous operation. Analysis of the log data reveals that this is due to bearing wear. To address this issue, a maintenance plan can be developed for regular bearing replacement, and the operating parameters of washing machine 1 can be optimized to extend its service life. In addition, by establishing an equipment status assessment model, equipment health can be monitored in real time and potential faults can be predicted. For example, by training equipment operation data using a neural network algorithm, an equipment health index (HI) can be generated to guide long-term optimization strategies.

[0044] Finally, in step A9, multiple batches of data are integrated, and the adaptability change trend of chip trays 3 of different specifications is analyzed to form a universal dehydration process optimization model. In this process, by analyzing multiple batches of data through the cloud computing platform, the common laws and difference characteristics of chip trays 3 of different specifications in the dehydration process can be revealed. For example, assuming that a certain model of chip tray 3 shows a high stress concentration risk in multiple dehydration experiments, its adaptability can be improved by adjusting the geometric design or optimizing the centrifugal parameters. In addition, through the cluster analysis algorithm, chip trays 3 of different specifications can be divided into several categories, and corresponding dehydration process models can be generated for each category. For example, for chip trays 3 with larger geometric dimensions and more brittle materials, it is recommended to use a lower centrifugal speed and a longer dehydration time; while for chip trays 3 with smaller geometric dimensions and more tough materials, the centrifugal speed can be appropriately increased to improve the dehydration efficiency. In this way, a highly universal and efficient dehydration process model can be formed, which is suitable for the dehydration needs of chip trays 3 of various specifications.

[0045] In this embodiment, by obtaining the chip extraction tray 3 specification data and dehydration requirement parameters, matching the adaptive dehydration model to determine the initial configuration plan, real-time collection of vibration and stress data during high-speed centrifugation, and dynamic adjustment of centrifugation parameters to optimize the operation plan. Finite element analysis is performed in combination with chip characteristics to predict potential damage risks and train damage suppression models, and environmental parameters are monitored in real time for adaptive adjustments. Digital twin technology is used to analyze the dehydration efficiency and chip quality change trends, and equipment maintenance data is combined to optimize long-term operation configurations. Multiple batches of data are integrated through the cloud platform, and the adaptability changes of chips of different specifications are analyzed to form a general dehydration process optimization model. The present invention realizes the intelligent control and process optimization of the chip extraction tray 3 dehydration process, improves the dehydration efficiency and chip quality, and enhances the adaptability and stability of the equipment.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A chip dehydration method, characterized in that: The following steps are involved: S1. Provide a jig; the jig is cylindrical in shape; the jig is provided with a plurality of limiting grooves at equal intervals along the circumference; S2, placing multiple chip trays containing gold-de-goldened chips into the limiting slots respectively; S3. Place the fixture into the washing machine; S4. Start the washing machine to dehydrate the chip after the gold is removed.

2. A chip dehydration method according to claim 1, characterized in that: In step S4, the following steps are included: A1. Obtain chip extraction tray specifications and dehydration requirements, match the appropriate dehydration model with the database, and determine the initial centrifugation speed and time parameters. A2. Based on the standardized dehydration configuration plan, real-time collection of vibration and stress distribution information during high-speed centrifugation is performed to determine whether dynamic adjustment of centrifugation parameters is required. A3. Analyze the correlation between vibration and stress distribution, optimize centrifugal speed and time parameters, and form an optimized dehydration operation plan; A4: Combine the chip plate's geometric characteristics and material properties to simulate stress concentration areas and predict potential damage risks; A5: Train the damage suppression model based on historical data, determine the vibration reduction parameter adjustment plan, and generate a damage-optimized operating configuration; A6: Real-time monitoring of temperature and humidity, adjustment of centrifugal speed or extension of dehydration time, and obtaining dehydration parameters suitable for the environment; A7: Use digital twin technology to build a virtual model, analyze dehydration efficiency and quality change trends, and generate the final batch dehydration plan; A8: Extract the washing machine's operation log, analyze the washing machine's status, and determine the technical configuration for long-term optimization. A9: Integrate data from multiple batches, analyze the adaptability trends of extraction trays for chips of different specifications, and form a universal dehydration process optimization model.

3. A chip dehydration method according to claim 2, characterized in that: The acquisition of chip tray specification data and dehydration requirement parameters in step A1 includes establishing a chip tray specification database covering geometric dimensions, material properties and dehydration requirement parameters, and generating a standardized dehydration configuration plan through an intelligent matching algorithm.

4. A chip dehydration method according to claim 2, characterized in that: The real-time collection of vibration and stress distribution information during high-speed centrifugation in step A2 includes deploying a high-sensitivity sensor network to dynamically capture the vibration amplitude and stress distribution during the centrifugation process, and determine whether the operating parameters need to be adjusted based on preset thresholds.

5. The chip dehydration method according to claim 2, wherein: The analysis of the correlation between vibration and stress distribution in step A3 includes analyzing the spatial relationship between the vibration source and the stress distribution, and optimizing the centrifugal speed and time parameters to reduce stress concentration.

6. A chip dehydration method according to claim 2, characterized in that: The simulation of the stress concentration area in step A4 includes using finite element analysis technology, combining the geometric characteristics and material properties of the chip tray, to evaluate the impact of stress concentration on the chip structure and identify potential damage risk areas.

7. A chip dehydration method according to claim 2, characterized in that: Training the damage suppression model based on historical data in step A5 includes extracting operating data of chips with similar specifications from historical dehydration cases, building a damage suppression model, and generating a vibration reduction parameter adjustment plan.

8. The chip dehydration method according to claim 2, characterized in that: The real-time monitoring of temperature and humidity in step A6 includes deploying temperature and humidity sensors, dynamically adjusting the centrifugal speed or extending the dehydration time to ensure that the environmental conditions match the dehydration requirements.

9. The chip dehydration method according to claim 2, characterized in that: The construction of the virtual model using digital twin technology in step A7 includes simulating the entire chip dehydration process, analyzing the dehydration efficiency and quality change trends, and verifying the impact of parameter adjustment on mass production.

10. The chip dehydration method according to claim 2, characterized in that: Extracting equipment operation logs in step A8 includes analyzing maintenance cycles and fault data, evaluating equipment operation stability, and formulating long-term operation optimization strategies; The integration of multiple batches of data in step A9 includes analyzing the adaptability change trend of chip extraction trays of different specifications through a cloud computing platform, optimizing equipment flexibility and forming a highly universal and efficient dehydration process model.

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