2024
DOI: 10.1002/pen.26866
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Data‐driven quality prediction in injection molding: An autoencoder and machine learning approach

Kun‐Cheng Ke,
Jui‐Chih Wang,
Shih‐Chih Nian

Abstract: In the injection molding process, the pressure within the mold cavity is crucial to the quality of the final product. Due to the inability to directly observe the process, sensor technology is required to acquire data. Traditionally, experts interpret and encode pressure curves, but this method has limitations. This study proposes an innovative pressure curve encoding technique to overcome these limitations and achieve automation to obtain more comprehensive pressure information. The study employs mold flow an… Show more

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