Details
| Original language | English |
|---|---|
| Pages (from-to) | 5233-5241 |
| Number of pages | 9 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 133 |
| Issue number | 11-12 |
| Early online date | 2 Jul 2024 |
| Publication status | Published - Dec 2024 |
Abstract
In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.
Keywords
- Artificial neural network, Digital manufacturing system, Process control, Rubber extrusion, Sustainable manufacturing
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Industrial and Manufacturing Engineering
Sustainable Development Goals
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In: International Journal of Advanced Manufacturing Technology, Vol. 133, No. 11-12, 12.2024, p. 5233-5241.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Real-time temperature control in rubber extrusion lines
T2 - a neural network approach
AU - Lukas, Marco
AU - Leineweber, Sebastian
AU - Reitz, Birger
AU - Overmeyer, Ludger
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.
AB - In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.
KW - Artificial neural network
KW - Digital manufacturing system
KW - Process control
KW - Rubber extrusion
KW - Sustainable manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85197371799&partnerID=8YFLogxK
U2 - 10.1007/s00170-024-14061-1
DO - 10.1007/s00170-024-14061-1
M3 - Article
AN - SCOPUS:85197371799
VL - 133
SP - 5233
EP - 5241
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 11-12
ER -