A new approach to temperature control in the extrusion process of composite tire products

Silva, André; Silva, Francisco; Campilho, R.D.S.G.; Neves, Pedro M.P.F.

doi:10.1016/j.jmapro.2021.03.022

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…Silva et al. [ 15 ] detected the temperature and fluidity of the polymer under compound extrusion manufacturing processes. However, experimental approach is inefficient and the sensor introduced would affect the curing process.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimization of Curing Profile for Autoclave Processed Composites: Simultaneous Control of Curing Time and Process-Induced Defects

Tang

Hui

et al. 2022

Polymers

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The contribution of this work is introducing a multi-objective optimization method based on finite element (FE) numerical simulation to simultaneously control the curing time and cure-induced defects of C-shaped composites during a curing cycle. Thermochemical and thermomechanical coupled analysis is performed and validated experimentally to understand the evolution details of temperature, degree of cure and curing deformation. Aiming to achieve the simultaneous control of manufacturing cost and composite quality, the curing profile is optimized by employing the critical factors including the total curing time, the maximum degree of cure difference, and the curing deformation. The optimization result shows that the designed curing profile can effectively reduce the curing time and guarantee the curing quality. The total curing time of the optimization is reduced by 19%. The verification experiment is also conducted to prove the accuracy and effectiveness of the proposed optimization method.

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Section: Introductionmentioning

confidence: 99%

“…Sorrentino et al [14] monitored composite temperature evolution using the thermocouples inserted in prepreg sheets. Silva et al [15] detected the temperature and fluidity of the polymer under compound extrusion manufacturing processes. However, experimental approach is inefficient and the sensor introduced would affect the curing process.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Curing Profile for Autoclave Processed Composites: Simultaneous Control of Curing Time and Process-Induced Defects

Tang

Hui

et al. 2022

Polymers

View full text Add to dashboard Cite

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Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach

Lukas,

Leineweber,

Reitz

et al. 2023

Lecture Notes in Production Engineering

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Real-time temperature control in rubber extrusion lines: a neural network approach

Lukas,

Leineweber,

Reitz

et al. 2024

Int J Adv Manuf Technol

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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.

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A new approach to temperature control in the extrusion process of composite tire products

Cited by 6 publications

References 46 publications

Multi-Objective Optimization of Curing Profile for Autoclave Processed Composites: Simultaneous Control of Curing Time and Process-Induced Defects

Multi-Objective Optimization of Curing Profile for Autoclave Processed Composites: Simultaneous Control of Curing Time and Process-Induced Defects

Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach

Real-time temperature control in rubber extrusion lines: a neural network approach

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