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

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.