Scientific Molding and Adaptive Process Quality Control with External Sensors for Injection Molding Process

This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external sensors, the characteristic values of nozzle pressure and clamping force were used to optimize parameters. This study defined product weight as a quality indicator and developed a scientific molding parameter setup process. The optimization sequence of parameters is injection speed, V/P switchover point, packing pressure, packing time, and clamping force. Finally, an adaptive process control system was established based on the online quality characteristic values to maintain product quality consistency. Continuous production experiments were conducted at two sites to verify the system’s effectiveness. The results revealed that the optimized process parameters can ensure product weight stability during long-term production. Furthermore, using the adaptive process control system further enhanced product weight stability at both sites, reducing the standard deviation of product weight to 0.0289 g and 0.0148 g, and the coefficient of variation to 0.065% and 0.035%, respectively.

Authors

Institutions

Publication Details

Journal
Technologies
Published
2025-03-01
DOI
https://doi.org/10.3390/technologies13030097
Citations
2
Primary Topic
Injection Molding Process and Properties
Type
article
Field-Weighted Citation Impact
1.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Scientific Molding and Adaptive Process Quality Control with External Sensors for Injection Molding Process

Ren-Ho Tseng, Chien-Hung Wen, Sheng‐Jye Hwang, Chih-Ta Tsai et al.
2 citations
Technologies
Injection Molding Process and Properties
1.43
article

Scientific Molding and Adaptive Process Quality Control with External Sensors for Injection Molding Process

Ren-Ho Tseng, Chien-Hung Wen, Sheng‐Jye Hwang, Chih-Ta Tsai, Hsin‐Shu Peng, Chen-Hsiang Chang, Yuhao Chen
article en
2 citations

Abstract

This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external sensors, the characteristic values of nozzle pressure and clamping force were used to optimize parameters. This study defined product weight as a quality indicator and developed a scientific molding parameter setup process. The optimization sequence of parameters is injection speed, V/P switchover point, packing pressure, packing time, and clamping force. Finally, an adaptive process control system was established based on the online quality characteristic values to maintain product quality consistency. Continuous production experiments were conducted at two sites to verify the system’s effectiveness. The results revealed that the optimized process parameters can ensure product weight stability during long-term production. Furthermore, using the adaptive process control system further enhanced product weight stability at both sites, reducing the standard deviation of product weight to 0.0289 g and 0.0148 g, and the coefficient of variation to 0.065% and 0.035%, respectively.

TechnologiesVol. 13(3)
Feng Chia University (TW), National Cheng Kung University (TW)
Openalex Percentile: Top 22%
Injection Molding Process and Properties
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Scientific Molding and Adaptive Process Quality Control with External Sensors for Injection Molding Process — Ren-Ho Tseng, Chien-Hung Wen, et al. · Technologies (2025) | TGRS Research Map | TGRS