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This study introduces an adaptive control system for injection molding, significantly reducing part weight variation to 0.14%. This innovation enhances product quality consistency using minimal sensor data.

Keywords:
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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Control Systems

Background:

  • Injection molding processes are susceptible to variations affecting product quality.
  • Maintaining consistent part quality, specifically minimizing mass variation, is crucial in manufacturing.
  • Existing control systems may not adequately adapt to process fluctuations.

Purpose of the Study:

  • To develop an adaptive control system for injection molding to minimize part mass variation.
  • To enhance the consistency of product quality in injection molded parts.
  • To leverage sensor data for real-time process adjustment.

Main Methods:

  • Utilized injection nozzle pressure and temperature sensors for data acquisition.
  • Established a master pressure curve by correlating product quality with parameters like viscosity index, peak pressure, and timing.
  • Employed a backpropagation neural network (BPNN) trained on switchover position and injection speed data to compute control system parameters.

Main Results:

  • Successfully developed an adaptive control system for injection molding.
  • Minimized the variation in part weight to an impressive 0.14%.
  • Demonstrated the system's ability to maintain product quality consistency.

Conclusions:

  • The adaptive control system effectively minimizes mass variation in injection molded parts.
  • The system's reliance on minimal sensor data (pressure and temperature) makes it practical.
  • This approach offers a robust solution for improving product quality and process efficiency in injection molding.