Online prediction system and method for density of powder injection molded blank

A technology of powder injection molding and prediction system, which is applied in the field of powder injection molding to achieve the effect of saving cost, improving efficiency and saving tedious labor

Inactive Publication Date: 2012-08-29
UNIV OF SCI & TECH BEIJING
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  • Abstract
  • Description
  • Claims
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  • Online prediction system and method for density of powder injection molded blank
  • Online prediction system and method for density of powder injection molded blank
  • Online prediction system and method for density of powder injection molded blank

Examples

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Example Embodiment

[0040] Implementation examples:

[0041] Choose 316L stainless steel powder, the binder is 79% paraffin + 20% high density polyethylene + 1% stearic acid, and the powder loading is 53%. The powder and binder are mixed at 140℃-150℃ for 1.5h to obtain uniform feed.

[0042] The rectangular parallelepiped sample is injected on the injection machine, and the mold size is 28.3mm×20mm×6mm. Use the following series of parameter combinations for injection, the mold temperature is maintained at 300K; the injection temperature is between 420K and 450K, and an injection point is taken every 5K interval; the injection rate is 60cm 3 / S~90cm 3 / S, every 2cm 3 One injection point is taken at intervals of / S. A cuboid sample was injected under each set of parameters, and a total of 1×2×16=32 injections were injected to obtain 32 injection blank samples. At the same time, the temperature value T of each point inside the mold is monitored through the sensor network at the completion of each injec...

Example Embodiment

[0048] Example 1: Using mold temperature of 300K, injection temperature of 420K, injection rate of 63cm 3 / S is a set of parameters for injection, and the temperature and pressure values ​​of each point inside the mold are monitored by the sensor network system as follows: (T1, P1) = (419.5, 81.6); (T2, P2) = (421.3, 83.5); (T3, P3) = (419.5, 81.6); (T4, P4) = (420.0, 82.8); (T5, P5) = (422.5, 84.3); (T6, P6) = (420.0, 82.8); (T7 , P7) = (418.6, 80.6); (T8, P8) = (420.2, 83.0); (T9, P9) = (418.6, 80.6)

[0049] The data obtained from the above monitoring is normalized through the man-machine interface and then input to the artificial neural network system for gray-scale prediction, and at the same time, gray-scale detection is performed on the same sample through an industrial CT machine. The predicted and detected values ​​are listed in the table below:

[0050]

[0051] It can be seen that the predicted value of the artificial neural network system is very close to the detect...

Example Embodiment

[0052] Example 2: Using mold temperature 300K, injection temperature 422K, injection speed 75cm 3 / S is a set of parameters for injection, and the temperature and pressure values ​​of each point inside the mold are monitored by the sensor network system as follows: (T1, P1) = (421.6, 82.2); (T2, P2) = (423.5, 84.3); (T3, P3) = (421.5, 82.2); (T4, P4) = (422.6, 83.5); (T5, P5) = (424.3, 84.8); (T6, P6) = (422.5, 83.6); (T7 , P7) = (419.8, 81.9); (T8, P8) = (421.3, 83.5); (T9, P9) = (419.8, 81.8)

[0053] The data obtained from the above monitoring is normalized through the man-machine interface and then input to the artificial neural network system for gray-scale prediction, and at the same time, gray-scale detection is performed on the same sample through an industrial CT machine. The predicted and detected values ​​are listed in the table below:

[0054]

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Abstract

The invention belongs to the technical field of powder injection molding and particularly provides an online prediction system and a method for a powder injection molded injection blank. The system comprises an injection molding machine, an industrial CT (computed tomography) machine, an image processing system, a sensor network system and an artificial neural network system. The method comprises the steps as follows: uniform powder feed is conveyed into the injection molding machine and is injected according to a random group of technological parameters to form a blank body sample; at the moment that the injection process is finished, the sensor network system automatically detects temperature values T and pressure values P of all points inside an injection mould; and the temperature values T and the pressure values P are automatically conveyed to the artificial neural network system, and the neural network system automatically gives predictive values of overall grey scale H and local grey scale Li of the injection blank body sample. The online prediction system and the method have the advantages as follows: 1, the injection blank density distribution can be automatically predicted, and the efficiency is improved; 2, the sample is not damaged during the detection process, and qualified samples still can be used, so that the cost is saved; and 3, the quality of injection products can be monitored real timely, so that the quality problems of the injection blanks can be found out timely.

Description

Technical field: [0001] The invention belongs to the technical field of powder injection molding, and in particular provides an online prediction system and method for powder injection molding injection blank density. Background technique: [0002] Powder injection molding technology is a near-net-shaping technology for parts formed by combining traditional powder metallurgy technology and modern plastic injection molding technology. It can use mold injection molding blanks and quickly manufacture high-density, high-precision, shape Complex structural parts, because of its unique advantages, are known as "the most popular part forming technology today". However, the defects generated in the injection molding process, that is, the uneven density distribution of the injection molding, has always been one of the main problems that plague people. At present, in the production of powder injection molding, the judgment of the defects of the injection blank is generally to cut the...

Claims

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Application Information

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IPC IPC(8): B22F3/22
Inventor 何新波方伟韩勇吕品曲选辉
Owner UNIV OF SCI & TECH BEIJING
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