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In-mold and Machine Sensing and Feature Extraction for Optimized IC-tray Manufacturing.

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Summary

This study transforms injection molding from a "black box" to a "grey box" using advanced sensing. A new scientific method significantly reduced IC tray warpage by optimizing process parameters.

Keywords:
IC-trayfeature extractioninjection moldingparameter optimization

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Area of Science:

  • Manufacturing Engineering
  • Polymer Science
  • Materials Science

Background:

  • Injection molding quality is affected by raw materials, molds, machines, and processing parameters.
  • Traditional methods struggle with shot-to-shot geometric variations despite high precision control.
  • Understanding melt behavior is crucial for effective injection molding process control.

Purpose of the Study:

  • To develop a scientific method for optimal parameter adjustment in injection molding.
  • To transform the understanding of injection molding from a 'black box' to a 'grey box' using advanced sensing.
  • To identify key parameters for each phase of the injection molding cycle.

Main Methods:

  • Utilized advanced sensing technology to gather physical information on molten resin flow.
  • Analyzed injection speed, injection pressure, cavity pressure, and screw position profiles.
  • Determined main parameters for mold filling, velocity-to-pressure switching, and packing phases.

Main Results:

  • Developed a scientific method for optimal parameter adjustment in injection molding.
  • Successfully reduced IC tray warpage from 0.67 mm to 0.20 mm.
  • Optimized parameter profiles are applicable for continuous mass production and process monitoring.

Conclusions:

  • Advanced sensing transforms injection molding into a 'grey box' process.
  • The developed method enables scientific and optimal parameter adjustment.
  • This approach effectively reduces product defects like warpage and supports mass production.